Transcription
If I had to start an AI automation agency today with little to no coding experience or no tech background, on a relatively low budget, then here is exactly what I would do. These are the six high-impact, beginner-friendly AI skills that I would focus on, as well as the best resources and courses to master them, and how to do it all in, let's say, a few weeks, not years, without burning out or bouncing between random tools.
Because here's the truth: you do not need to know how to code necessarily. You need to know how to design outcomes, because AI is not about tools anymore. It's about systems thinking. And if you can build workflows that solve real problems, then you become impossible to ignore. And I guess it makes sense, right? Because right now the demand for AI automation is exploding. Businesses are drowning in repetitive work, and entrepreneurs want leaner, smarter systems. And every day, operators are realizing AI can significantly increase their efficiency if they know what to build. And that's why we're seeing new AI automation agencies popping up daily, impressive case studies from solo billers on what they can achieve with AI today, and companies actively hiring for this exact skill set. So if you're thinking about starting one, you're clearly not crazy. You're in fact early.
But here's the part that most people miss, and it's why they never get past step one. I guess what I've seen is that most people don't fail because they're lazy. In fact, it's exactly the opposite. They fail because they overthink the idea. They overload on tools, and they overcomplicate the execution. They get trapped in what I call the illusion of progress. They jump from one YouTube video to another, from prompt hacks to platform demos, from one shiny new AI tool to another. But they're missing probably the most important thing. The truth is that you don't need more tools. You need the right ones paired with the right skills; skills that unlock outcomes, skills that build confidence, and skills that actually help you generate revenue. Because, in my opinion, the truth is that if you can build just a few high-level automations that solve real-world problems, then you can build an entire business around that. And that is exactly why this video is not about learning everything. It's about learning what matters most, in the right order, so you can stop waiting and start taking action. Start building.
And if you're wondering how I know this, well, it's because I'm not guessing from the sidelines. Over the last four years, my husband and I built two AI-first businesses. We started with a media company powered by AI workflows—so we were client zero. And then, about a year ago, we also started an automation agency. And we've worked with real clients. We've built real systems. And we've also helped over 8,000 people through our free and paid programs. And the one thing that we see over and over and over again is that the winners do not obsess over tools. They design outcomes, and then they deliver them with precision.
All right, so let's break down the six skills that actually matter if you are a new founder looking to start an AI automation agency.
Skill number one: This is about generative AI because, listen, you don't necessarily need to code. You need to converse with intelligence. Most beginners who are trying to break into AI automation start with the wrong question: Which tool should I learn first? But tools come and go. Features change, and APIs break. The better question is, how do these systems actually think? Because if you don't understand what an AI model is trying to do, you're going to spend months automating fluff and duct-taping templates and wondering why none of it feels strategic. Well, this is why most people get stuck because they skip the fundamentals and they fall into the black hole of complexity. But complexity is the enemy of execution. You know, they build workflows without a solid reasoning engine, and they launch offers with no clarity on what is possible or profitable with generative AI. So before you even open Zapier, before you touch a no-code builder, you need to understand this: Generative AI is not magic. And having this distinction is going to help you tremendously. Generative AI—it's pattern generation at scale, and it's your new thinking partner. If you want to build systems that attract clients, that solve real problems and run while you sleep, you don't need to become a developer. You need to become a designer of intelligent outcomes.
And when I say generative AI, this skill is not about learning how to use ChatGPT. It's about learning how to think with it. You need to understand how large language models, LLMs, generate answers—whether that's text or visuals or anything else—not randomly, but based on probability and tokens and context. You need to learn to see beyond the interface and into the engine. And this is where your transformation needs to begin: from tool user to systems architect, from automation fan to outcome designer. Because once you understand what the AI is capable of, you are going to start seeing opportunities everywhere. We were just talking about that actually with the people in our community. You're going to see how to build your own internal GPT for a client niche, or how to automate repetitive thinking, not just repetitive tasks, and how to embed intelligence into onboarding and emails and customer journeys and content workflows.
So, if you want to think about it this way, you can think of generative AI knowledge as your blueprint layer. Everything else you build—automation, agents, dashboards, stacks—on top of it. So, if you're then thinking, okay, but how do I learn it? Well, the good news is that you do not need to spend thousands and thousands of dollars on a boot camp. You just need the right foundations delivered clearly, in a way that maps directly to what you want to build. So, one option that you could look into is the Generative AI Automation Specialization from Vanderbilt University. You can find this one on Coursera, and it was created by Jules White, a leading researcher in AI systems. It does take a little bit longer, so it's estimated to take you about 40 hours across four modules, but the rating is fantastic. Over 10,000 people have taken this course. And I really think it's worth it because it's not theory. This course is built for practitioners with real-world use cases like document automation and chat workflows and even RAG pipelines, which we're going to come back to in a moment. And you're going to learn the difference between generative AI and predictive models and search systems, and the core structure of LLMs like transformers and tokens and context windows in plain English. Uh, you're going to learn how to start building agent-based systems using GPT and Langchain and other AI dev tools. And if you're more pressed for time, or maybe you just want a primer, then you can also look into the Introduction to Generative AI from Google Cloud, developed with DeepLearning.AI. This one is also on Coursera. It takes a lot less time—it's one and a half hours—and it's a clear, very beginner-friendly explainer that shows what makes AI different. So, this would be a great way to get started, I guess. But the other one is a more in-depth way of understanding AI.
Now, look, most beginners try to prompt their way to results, but prompting without an understanding of how to prompt well is like trying to build a house by rearranging bricks but having no blueprint. Well, these courses fix exactly that problem because they give you the mental model that you are going to use across every skill that follows. Because if you want to start an AI automation agency, this is the one skill that lets you see the map, and everything else is execution.
Now, skill number two: This one I have spoken about a little bit before. This is prompt engineering, because prompts are not commands. They are conversations with intelligence. And what I find really funny sometimes is that many people treat prompting like Google search. They type something, they get something, and then they copy-paste and move on. But honestly, that mindset is like shouting instructions at a genius and expecting them to read your mind. But, in my opinion, the truth is that the quality of your prompt is the quality of your thinking. And prompting, instead of hindering thinking, it actually exposes it. Because in this new AI world, prompting is not a gimmicky skill. It's a business foundational skill, and, in my opinion, it's what lets you automate repetitive writing without losing nuance. It's what lets you build internal tools that talk and reason and decide and translate human requests into machine-executable outcomes. But what I see many times is that many beginners learn how to use ChatGPT, but few actually learn how to design interactions with it. And this is where probably 90 to 95% of AI agencies um fall short because they build automations that feel smart but cannot adapt or cannot personalize or cannot think. So if you want to build real workflows with real value, then you need to learn the language of power, which is prompting.
And prompt engineering is not about viral hacks or clever tricks that you find in a TikTok. It's about precision. You need to learn to define the role and the intent, feed structured context, chain reasoning into steps, prime models for reproducibility, debug output like a systems architect, not like a user. And when you get this right, your AI is not going to just respond to you. It is going to start to reason with you and sometimes even on your behalf. This is the glue layer in every system that you build. Whether you're designing a lead-qualifying GPT or a client-facing chatbot or a personal assistant that summarizes PDFs into proposals, this is the skill that makes the machine useful at the end of the day. And if you want to think about it differently, uh prompting is the syntax of systems thinking. Just like coding requires logic and structure, prompting requires intent and scaffolding. And if you get lazy, then the output is going to reflect it. But if you get sharp, then your system is going to start to think.
So, as I said, I've talked about this before. If you feel like you need to learn a bit more about prompt engineering, then you can learn that in probably a few hours and spend a lifetime mastering it. And there are some really, really good trainings out there. Two that I want to mention today: One is also from Vanderbilt University—it's the Prompt Engineering Specialization, which you can also find on Coursera. And this one is only going to take you about three to five hours in total. But just like for everything else, you can always two-exit if you feel like some of the parts you already know, and then it's going to take you a lot less time than that. It has a fantastic rating—it's 4.8 out of 5. It's extremely actionable with real examples. It teaches summarization and classification and task chaining. And I really like it because it gives you a foundational model of prompting logic with hands-on practice in labs for ChatGPT and for Claude. Now, the other one that you could look into is Prompt Engineering for ChatGPT from DeepLearning.AI and OpenAI. This one is about 2 hours. They could go very well together. You are going to learn some complimentary skills. Uh, this one specifically is built with guidance from OpenAI, and uh it gives you a clear framework for role definition, for zero-shot versus few-shot prompting, chaining techniques, context structuring. I'm not going to bore you with these details, but if you do want to start an agency, you are going to need to know all of that. Now, bonus points if you go and take the Langchain and Prompt Engineering Fundamentals course because uh this one uh which was part of the training that I mentioned for skill number one uh comes included, and this one is going to come in very, very handy a bit later. Most people stop at "make it sound more persuasive." But prompt engineering is not a copy-paste trick. It's a systems-level thinking skill. As I said, one good prompt can replace five automation steps. One good prompt can radically impact a full workflow because you don't just tell AI what to do, you architect how it thinks with you. And if you master this, then everything else you build becomes smarter, faster, more scalable because now you're not just building systems, you're embedding strategy into language.
Okay. Now, skill number three: This one is so, so, so critical. This one is about design thinking and business process mapping. This is probably one of the skills that we use the most in our agency because you don't start with automation. You need to start with awareness. And this is where so many beginners mess up. They think that AI automation is about doing things faster. But speed only helps if you're moving in the right direction. Before you start building agents or changing tools or automating workflows, you actually need to understand the workflow. I hope that makes sense, right? Because without clarity, all you're doing is automating a mess, right? You're automating chaos. If you digitize a mess, you get a digital mess. That's something that W. Edward Deming actually said, and he's so right. Most beginners skip this part and they build cool tools that no one ends up using because they duct-tape steps without understanding how the system actually flows. We spend a good amount of time with our clients on this step specifically because if you don't, then you wonder why the automation breaks, or worse, you solve the wrong problem.
So here is what we, in our agency, actually do instead. We zoom out. We look at the entire customer journey or the entire workflow, depending on which part of the business we're working on, and then we're asking questions like: What is the current bottleneck? Where does time leak? Which step could be eliminated and not just automated? What is the actual outcome that the client is trying to achieve? We don't think in tasks, we think in processes. So essentially this is like the blueprint stage of system design because if the automation tools are your power tools, design thinking is the architect's sketch before anything is built. So if you're thinking, okay, but what is that? Well, think of it as critical thinking for systems. This combines two very, very important critical muscles. On the one hand, you have design thinking: how to frame the right problems and create human-centered solutions. And then, on the other hand, you have business process mapping: how to visualize and structure workflows before you automate them. And together these will help you identify what to build, what to skip, and what to simplify before you even open a pen or make or Zapier. Because you don't need to be a designer. You just need to stop solving the wrong problem. I mean, this is the skill that is going to help you say, "Wait, do we even need this step?" Is people continue doing things that they've always done just because they've always done them like that? But do they really?
And now, if you're thinking, "Okay, but I have no idea how to learn this," well, you want practical, non-bloated courses, uh something that is going to teach you how to think like a systems designer and not just draw pretty diagrams and workflows. So, um, here are some suggestions for you. You've got the Design Thinking for Innovation from the University of Virginia. This is a course that takes about three hours. It's not the most in-depth, but it's definitely going to give you a little bit of, let's say, understanding of what we're talking about here. There are 100,000 people who've taken the course. It's going to teach you how to identify user-centered problems, reframe challenges to uncover better solutions, how to use low-fidelity prototypes to test fast. I mean, this is the perfect option to reset your thinking if you tend to jump straight into tools. And then you've got another one about business process modeling. This is from the University of Illinois. This one is a bit longer—I think it's 8 to 10 hours. Both of these you can find on Coursera, by the way. Uh, and this one teaches you the skill of visualizing any workflow: how to use real-world models to map client onboarding, fulfillment systems, sales journeys. And by the end, you are not going to be blind and build blind ever again. Uh, there is a much more in-depth one. It's called Design Thinking Specialization from Darden. This one is much longer. It's going to take you about 40 hours to go through at regular speed, but if you have time, I encourage you to go through it because it is best if you want a deeper strategic edge. Um, it is used by Fortune 500 innovation teams. It teaches you how to think in systems that scale, and you will not regret taking it if you have the time, of course. But look, people skip to "how can I automate this" before even asking "should this even exist?" And this leads to wasted time, bloated workflows, and tools that are duct-taped together to solve imaginary problems. But when you think in systems first, you stop building for effort and you start building for elegance, I guess. Because at the end of the day, AI rewards those who reduce complexity, not those who add features. This skill does not make your systems bigger for no reason. It makes them lean and smart.
Now, skill number four is about no-code automation platforms, because you and every other entrepreneur out there do not need a big team. You need a trigger and a defined workflow or outcome. Most people think they need to learn how to code. But let's be honest, in 2025, you do not need to build everything from scratch like a traditional developer would. You need to think like a systems architect. And automation tools are not just productivity hacks. They are your digital workforce, because if you can describe it, you can probably automate it. At least this is what we are discovering in many of the calls we have with our prospects. Here's where I believe many beginners go wrong: They binge-watch YouTube tutorials. They try to learn every single tool out there. They confuse complexity with capability. And then they build these half-working Frankenstein stacks that crash the minute a client asks for something custom. But around here, we call ourselves fifth-level disruptors. And we do things differently. We pick a core stack. We master a few high-level tools, and we build systems that run without us, ideally, or without our client. I mean, you can think of this as the wiring and plumbing of your automation agency, because if your AI skills are the brain, Make and Zapier are the nervous system. These are the platforms that connect everything. And when they're wired the right way, you do not have to touch them again, or at least not too soon.
So what is this skill specifically? Well, no-code automation is about chaining logic across platforms. Essentially, what it means is connecting your lead form to your CRM, or sending an automated proposal after a call, or triggering personalized onboarding once a Stripe payment lands, or auto-summarizing a client intake form and adding it to a Notion database. It's about thinking in logic, not in code, and letting tools, like I said, like Zapier and Make and n8n handle that execution. Now, you can clearly see that in 2025, the skills that we're talking about is not programming, it's precision configuration. So, if you're thinking, okay, I like the sound of that, but I need to learn this. Well, here is, in my opinion, the curated stack for fast results without overwhelm. Uh, number one, you can go to make.com's academy. It's completely free. It is ideal for complex multi-step workflows, and you're going to find real-world use cases like client onboarding and content repurposing and AI logic chains. It is used by tens of thousands, if not hundreds of thousands, of pros building, you know, pretty nice no-code businesses. Now, if you want to focus more on Zapier, you can also learn this one for free because they offer courses on their website. This one is perfect for event-based automation—so, "when this happens, do that" kind of situations. It has over 5,000 apps that it can integrate with. And it's less flexible than Make, but easier for beginners. It shines when it comes to speed and simplicity. Now, if you're like my husband and you do have technical background, even a little bit—he has a lot, but even if you have a little bit—look into n8n. They also have a lot of free video and text courses on their website. This is an open-source powerhouse for both simple and advanced workflows. It is best if you want self-hosted control. Um, it's growing fast in the AI agent and automation community, and it gives you a lot, a lot of flexibility. I mean, whichever you decide to choose, this is a great long-term skill for building private or client-specific automation stacks. But one thing that you do want to be aware of is that most people try to build big before they build smart. And they create these 10-20 step automations before understanding if a five-step flow is enough. So my two cents advice for you is: start with one problem, one trigger, one automation, and then scale clarity, not mess. Because it's not about what you can automate. It's about what you can automate that matters. Because when you master this layer, you stop being the
Operator, and then you start being the architect. Now, skill number five, this one is about no-code AI agents. I mean, nowadays everybody wants to stop doing all the tasks and start assigning them to the right specialists. And these days, AI agents are the new cutting-edge specialists that can work both smart and 24/7 for you or for your clients.
But if you think about it, in a traditional business, when something needed to get done, then you would hire someone, right? In the new economy, you design a system, and then you delegate predictable work to an AI agent. You have employees work on creative, high-value tasks. This is how you keep them engaged and motivated, and then everything else gets assigned to an AI agent. The agent doesn't just respond; it can think, it can decide, it can act based on how you train it.
So before we move on, let's get something clear. This is not about building the next ChatGPT. It is about giving AI a job. It's about embedding reasoning into your workflows so that you can stop executing tasks manually and start designing or engineering decisions. And one thing that I'm seeing a lot is that many people get stuck building systems that only do what they tell them to do. But real business leverage comes when those systems also decide when or how and why to act within the guardrails that you give them.
Of course, so far AI agents have been the missing layer, until now, because the best part is that you don't need to code any of this from scratch. You have no-code, low-code tools that are going to get you going fast. So when you think about creating AI agents, basically you can think about it this way: If automation tools are the wires, AI agents are the brains. They don't just transmit signals; they interpret context, and they make choices. They make decisions, and they learn based on that.
So what am I talking about here? This is not chatbot building. Uh, this is process thinking and embedded AI logic. You will be able to design intelligent systems that ask clarifying questions and pull from knowledge bases and respond in natural language and trigger next steps like scheduling meetings and sending things and qualifying things like leads. These are not tech tools; they are significantly critical building blocks for autonomous layers of any business.
So, in terms of how you learn this, um, there are many ways you can do it, but I recommend mastering um three tiers of tools. Number one is Voiceflow. This is for UX and conversation design, and you can learn this one on Coursera. There is also many uh free resources that you can think about, but the course that I'm referring to from Coursera teaches you the way to map agent logic visually, and um it's widely used in conversational design teams like Amazon and BMW, and it's a great starting point for customer support agents or intake flows. This is a Coursera project that is called "Build a Chatbot with Voiceflow," and it's a really, really short one. It takes about an hour; it's super practical and it has great, great ratings.
The second one that you want to look into is BotPress. It's uh for logic and memory and APIs. I've talked about it before; it's very, very beginner-friendly. It's great for conditional workflows—if this, then that. It's very good for scoring; it's very good for decision trees. Um, it easily integrates with custom APIs and um lots of other things. And it's very, very useful if you want to build advanced lead qualification or booking bots, for example. Um, you can start by watching their YouTube channel. They have some very, very good videos. I know that in our community we did a chatbot challenge a few weeks back, and many people ended up going back to the videos uh on BotPress's YouTube channel.
Now, the third one you might want to consider is Flowise. Uh, this is Langchain-powered, and it is also no-code. There is um training on Coursera on Langchain with Flowise and LangFlow that you could take a look at. Um, basically what this does is it turns GPT into an agent by chaining prompts and tools. As I said, it's no-code, and it can build memory, logic, document retrieval in visual flows. It has a fantastic rating. I think it's 4.9 on Coursera, and it takes about 8 hours to complete. I would say it has really real-world skills like RAG agents, dynamic retrieval, multi-step reasoning. So if you feel like you need a little bit more depth in this space, that is a good one that you can look into.
What I also see a lot is that many people build bots that are just chat widgets, but clients don't want gimmicks, you know, they want outcomes. They want a lead that becomes qualified and a refund that becomes handled or a proposal that gets sent and a problem that ultimately gets solved. And with the right agent tools, you become a product builder; you're not a tool stacker. And I think the distinction you want to have here is that automations move, but agents decide.
So let's move to our last but certainly not least skill. This is knowledge systems and RAG architecture. Um, because no one needs to know everything; you just need to get the information you need when you need it. And this is where this skill comes in so, so, so handy. You cannot scale your business, or anyone's business, if all of the information lives in somebody's head. Uh, and in this day and age with AI automation, knowledge isn't just something that you store; it's something that your systems need to access and understand and act on. And that is what this skill is about and why it's so important. It's about connecting LLMs with your internal knowledge. It's about designing custom search systems. It's about making AI aware of a context, because AI is only as smart as the data that you feed it. Otherwise, it starts hallucinating, like I'm sure you experienced yourself.
Look, the person who designs how knowledge flows will outperform the person who memorizes it, because modern intelligence is not about recall; it's about retrieval. Most automations send emails or move data, right? Real business power lives in contextual reasoning. Can your agent pull answers from a 30-page onboarding document? Can your system personalize recommendations based on historical data, for example, or can your content assistant reference your best-performing posts from, I don't know, last 6 months or last year? That is the difference between a basic automation and a RAG-enabled business brain.
Okay, I've used this term plenty of times, so I think it's time to explain what RAG actually means. Uh, it stands for retrieval augmented generation. And in plain English, what this means is storing your knowledge in a structured format like a vector database. Essentially, what you're doing is you're letting AI search and use that knowledge in real time, and you're guardrailing it; you're constraining it, right? This is how you turn Notion pages into AI knowledge and PDFs into real-time answers and, I don't know, client documentation into chat-based support.
So I want you to imagine giving your AI tools knowledge about your client's business or your own business, and not just general instructions. What you really need to learn in order to acquire this skill are three components. Number one, vector databases. So the filing system here, you can look into Pinecone; you can look into Weaviate. Now, the second one is embedding models—basically turning text into retrievable meaning chunks—uh, for example, OpenAI embeddings that are used to split the knowledge for effective retrieval and understanding. And then the third one is integration layers, letting AI query your data with Flowise, with BotPress, with Langchain, like we've discussed.
So if you are a beginner, how do you even learn this? Well, you do not need a computer science degree. Um, you just need the right curriculum. So here's what I recommend if you want to get started quickly and you don't have a lot of knowledge, but you're willing to learn it. Number one, you can look into the Vector Database Fundamentals specialization on Coursera. This is a very, very good one. It's created by Pinecone, uh, which is the industry standard when it comes to RAG. It teaches core RAG concepts from scratch and it includes uh very practical hands-on labs with real AI pipelines. Uh, it has a fantastic rating of 4.8. It is a bit longer; it takes about 9 hours, but by the end you're going to know how to set up vector databases, how to generate embeddings, and how to connect them to GPT agents.
Now, if you want to look into something else, there's also an option about RAG and Langchain with GPT, also on Coursera. This one is more beginner-friendly. Uh, it's a good intro to building agents powered by RAG, and it shows you how Langchain and GPT talk to your own documents or your client's documents. And it's great for client portals, for customer support bots, for knowledge assistance. And this one has a 4.9 score. So, I think it's really, really incredible value that you're getting. It's about 10 hours worth of content, and by the end, you will be able to launch your first custom GPT with memory and document access.
So, maybe the thing that you might want to keep in mind here is that many beginners jump into agents before they have a proper knowledge system, and then they wonder why their bot gives vague answers or doesn't remember anything and things like that. Well, in my opinion, the truth is that you didn't design the knowledge base the right way. You just plugged in an LLM into general information that everyone else has. But what you're able to do with RAG is that you can give AI the mindset of an expert in any business because you can feed it unique data that an expert in whichever field would know.
Okay. So, if you've been nodding along as I was going through this and thinking, "Okay, I really want to learn all these skills, but where do I even begin?" Well, you clearly are not alone. A lot of people are looking for ways to get these skills and acquire and develop them as fast as possible, which is one of the reasons why I partnered with Coursera for this video, because right now they are running a limited-time 50% off deal on their annual plan. So you can access incredibly valuable AI courses from places like Google and Vanderbilt and deep learning AI for the price of a few coffees a month, essentially.
So if you want a quick summary, this is where I recommend you get started with practical, industry-backed courses: prompt engineering, generative AI automation, no-code agents, and AI business workflows. These are the exact skills that we have talked about today and that are taught by people building the tools. So if you want to move from "I'm interested in AI and AI looks cool" to "I build with AI," then check the links in the description. We are going to link to all of the courses that I mentioned today, and honestly, future you will thank you.
So let's bring this full circle. You do not need to master 100 tools. You do not need to become a full-stack developer. You do not need 20 years of tech experience necessarily, and you definitely don't need to spend years figuring it out. To start a real AI automation agency, one that solves problems, not just builds nice-looking projects and workflows, you need six core technical skills—just a lean stack of high-leverage capabilities that compound and that can get you off the ground and get your business started uh as soon as possible.
So let me recap: Generative AI fundamentals, because you want to understand what's possible; prompt engineering, because you want to communicate with AI like it's your co-founder; design thinking and business workflow mapping, because you want to diagnose real business problems, not just automate some tasks; no-code automation, because you want to connect the dots and replace routine with logic; AI agent building—I mean, this is a big one—because you want to turn workflows into autonomous digital team members; and then number six, knowledge systems with RAG and vectors, because you want to build intelligence that gets smarter with use.
This was a long one, but each one builds into the next. And if you are willing to take the time and put in the effort to learn all of these together, they are going to allow you to develop a system, a playbook, a productizable skill set that you can start monetizing in weeks, not years. People don't pay for hours anymore; they pay for outcomes. I hope I've made myself clear over the last, I don't know, how many videos. And I believe that if you can deliver outcomes that used to take a team, you are not just employable; you are unstoppable.
Now, look, all of these six skills are incredibly critical if you want to start an AI automation agency, and they are definitely going to help you do this in a sustainable way from a technical perspective. But if you want to launch a successful business, there are some other components that I believe are really, really critical in having a successful business launched um and sustainably developed so that it can become a long-term project, not just a short-term one. And I've made a video where I talk about all the other skills, which we're going to link up here, and you're more than welcome to watch that one next.
And if you want to practice all of these skills with like-minded people and learn from each other and go through very hands-on bi-weekly AI challenges, then you are more than welcome to come join our community. We are almost 9,000 people over there at the moment, and it's getting more and more fun by the day. We have so, so, so interesting conversations. We have bi-weekly calls as well where you have the opportunity to ask questions; uh, we get together, we share what we've learned, what we've built, what we've earned. So, if you want to be part of this movement, then you are more than welcome to come join us. Hopefully, we've remembered to put the QR code here. We are very, very much looking forward to meeting you. And in the meantime, like this video if you did. Be sure to subscribe if you haven't done so. And also share it with a friend who might be interested in starting an AI automation agency. Until next time, we'll make sure to share here the video that we think is going to be best for you, and I'll see you soon. Bye.