i have learned all the AI things for you So here's the cliffnotes version of everything you need to know about AI in my opinion in 2025 We'll be going from beginner to intermediate to advanced and I'll be giving you a crash course on each topic as well as providing more resources for you if you want to dig deeper into any of them By the end of this video you will know more about AI than like 99% of the population But not if if you don't actually retain that information So there will be little assessments throughout the video Now pay attention Let's go A portion of this video is sponsored by Retool Here's the structure of the video First we're going to go over the basic definitions of AI and how they work Then we'll be covering prompting followed by agents very hot these days followed by AI assisted coding We're building applications through what is called vibe coding and finally looking at some emerging technologies going into the second half of 2025 All right let's get started by first defining artificial intelligence Artificial intelligence refers to computer programs that can complete cognitive tasks typically associated with human intelligence Now AI as a field has been around for a very long time And some examples of traditional artificial intelligence which back in the day we used to call machine learning include things like Google search algorithms or YouTube's recommendation system for recommending you content like this video But what we typically refer to as AI these days is what is called generative AI which is a specific subset of artificial intelligence that can generate new content such as text images audio video and other types of media The most popular example of a generative AI model is one that can process text and output text otherwise known as a large language model or LLM Some examples of large language models include the GPT family from OpenAI Gemini from Google and the Claude models from Anthropic These days there are so many different types of models now and many models are also natively multimodal which means that you can input and output not only text but also images audio and video Your favorite models like GPD40 or Gemini 2.5 Pro are all multimodal Okay great Now you know some of the basic key terms that is used in the AI world So now I'm going to put on screen a little quiz for this section Please put it in the comments below your answers to these questions Also if you want more details about these Genaii models including a deeper dive under the hood of these models how they're being used in our workplaces as well as how to use AI responsibly I recommend that you check out this video which I'll link over here where I condense Google's 8-hour AI essentials course into 15 minutes But for now let's move on to the next section on how to actually get the most out of these AI models through prompting Let's first define prompting Prompting is the process of providing specific instructions to a Genai tool to receive new information or to achieve a desired outcome on a task This can be through text images audio video or even code Prompting is the single highest return on investment skill that you can possibly learn It's also foundational for every other more advanced AI skill And this makes sense because prompting is how to communicate with these AI models Like you can have the fanciest models the fanciest tools the fanciest whatever but if you don't know how to interact with it it's still useless So if you want to get started and practice prompting as a beginner the first step is just to choose your favorite AI chatbot That could be Chad GBT or Gemini or Claude or whatever it is that you like Next I have two pneumonics for you which if you can remember and implement will make you better at prompting than 98% of the population The first one is what I call the tiny crabs ride enormous iguanas framework which stands for task context resources evaluate and iterate When you are crafting a prompt the first thing that you want to think of is the task that you want it to do What do you want the AI to do For example maybe you want the AI to help you make some IG posts to market your new octopus merch line You could just prompt it create an IG post marketing my new octopus merch line And with that you'll probably get some okay results but you can make the results much better First you can add in a persona by telling the AI to act as an expert IG influencer to make the IG post This allows the AI to take on the role of an IG influencer and use some of that more specific domain knowledge to make a better IG post Then you can also add in the desired format of the output The default right now is a generic caption with some hashtags right But maybe you want something that's a little bit more structured You can ask it to start the caption with a fun fact about octtopi then followed by the announcement and ending with three relevant hashtags Great This is now already looking much better but there is still so much more we can do The next part of this framework is context The general rule of thumb is that the more context that you can provide to the AI the more specific and the better the results are going to be The most obvious piece of context that we can provide right now is some pictures of the actual merch that we're selling We can also add in some background about our company Like our company is called Lonely Octopus where we teach people AI skills like our recent AI agents boot camp which by the way we sold out last time within just 40 hours through the wait list So thank you so much for that And we're actually going to be opening up a new cohort soon So do sign up for the weight list if you're interested I will link it over here also linked in description Anyways some additional context that we can give the AI is that our mascot which is what is on the merch here is called Inky We can also be more specific about our launch date and our target audience for the merch like people between the ages of 20 to 40 mostly working professionals something like that With this context your results are going to be so much more precise and specific to what you want But we can do even better That's where the next step of the framework comes in which is references This is where you can provide examples of some other IG posts that you like This way the AI can take inspiration from this example Providing examples can be so powerful because you can describe things with words as much as you like But you know if you just provide it with an example there's like so much there that you can capture the nuances that you can incorporate into the results And voila you press enter and here is your IG post Now you want to evaluate Do you like it Is there anything that you want to tweak or want to change If so you go into the final step of the framework which is to iterate When interacting with AI models it is a very iterative process So even at the first time it doesn't get what you want you can tell it like tweak a little bit about this add something over here change the color of something and you work alongside AI to get the result that you finally want Tiny crabs ride enormous iguanas If you can remember this pneummonic and how to use it you would be better than 80% of people at prompting Let's call it 88 because that is a lucky Chinese number But if you want to be better than 98% of the population I have one more framework for you This is when you do the tiny crabs ride enormous iguanas framework and you feel like the results are still not quite there Well you can elevate this even further using the ramen saves tragic idiots framework First part of the framework is just to revisit the tiny crabs ride enormous iguanas framework See if you can add in something else maybe a persona Be more detailed about the output more references Also consider taking out something Is there any conflicting information in there that could be confusing for the AI Second part of the framework is to separate the prompt into shorter sentences Talking to AI is similar to talking to a human if you just like word vomit all over them and just say like a bunch of things It can be confusing for the AI So you can consider splitting what you're saying into shorter sentences to make it more clear and more concise So instead of just being like blah blah blah blah blah blah blah blah blah blah blah blah blah blah all over the place you could just be like blah then blah then blah Make sense Third part of the framework is to try different phrasing and analogous task For example maybe you're asking AI to help you write your speech and it's just like not quite there you know It's just like not really hitting it So maybe you can reframe this Instead of saying "Help me write a speech," say instead "Help me write a story illustrating whatever it is that you want to illustrate." After all what makes a good speech is a compelling and powerful story Hello So this is Tina from the future I have just gotten back to Hong Kong from Austin and it seems like in my jetlegged state I have forgotten to record the last part of this framework So I'm going to do that now which is introducing constraints Do you have one of those friends where you know maybe you are that friend when someone asks like hey what do you want to get for lunch and they're just like oh anything Yeah not very helpful Similarly if you feel like the output from your AI is just like not quite there You can consider introducing constraints to make the results more specific and targeted For example maybe you're making your playlist for a road trip that you're going on across Texas and you know you're just really not quite vibing with it You can introduce a constraint like only include country music in the summertime Much more suitable vibes All right now back to pastina Got that Ramen saves tragic idiots With these two frameworks together you'll be better than 98% of people at prompting By the way I also just want to say that I didn't just make up these frameworks myself I only take credit for the cool pneumonics The actual framework comes from Google itself So if you want to dive even deeper and be better than like 99% or even 100% of people at prompting I recommend that you check out this video over here which I'll link in which I summarize Google's prompting course which is the best general prompting course that I found so far Also I would recommend checking out some of the prompt generators for specific models like this one from OpenAI this one from Gemini and this one from Anthropic These are helpful for generating a first draft and for getting the most out of specific models For anybody that thinks that prompting as a skill is going to become obsolete think again Especially for more advanced applications like building agents and coding prompting is getting more important than ever It's like the glue that holds everything together to make sure that you get the results that you want consistently Now speaking of more advanced skills let's now move on to the next topic which is agents AI agents are software systems that use AI to pursue goals and complete tasks on behalf of users When we refer to AI agents we usually refer to it as an AI version of a specific type of role For example a customer service AI agent should be able to receive an email maybe of somebody being like I forgot my password and I can't log in And it should be able to reply to that email and should be able to reference the forgot password page on the website As of today it can't do everything and it can't handle all of the queries that a customer service person should receive but it can handle a lot of these kind of generic or common questions that people may have all autonomously Similarly for a coding agent if you prompt it well and you tell it to build like a web application it should be able to come back with an MVP version of that web application Still got to like add on a bunch of things and tweak it for sure but it can write the code for the first version of it AI agents is a space where there's a lot of interest and a lot of money that is being poured into it and I really expect them to get better and better over time and incorporate into all sorts of products and businesses In fact the most golden piece of advice that I have ever heard about AI agents was from this YC video which is for every SAS software as a service company there will be a vertical AI agent version of it Every company that is a SAS unicorn you could imagine there's a vertical AI unicorn equivalent So what exactly makes up an AI agent Well there are a lot of frameworks out there but the best one that I've seen so far comes from OpenAI They list six components that make up an AI agent The first one is the actual AI model Can't have an AI agent without a model This is the engine that powers the reasoning and the decision-m capabilities of the AI agent Second is tools By providing your AI agent with different types of tools you allow it to be able to interact with different interfaces and access to different information For example you can give your AI agent an email tool where it's able to access your email account and be able to send emails on your behalf Next up is knowledge and memory You can give your agent access to say like a specific database about your company so that it's able to answer questions and be able to analyze data specific to your company Memory is also important when it comes to specific types of agents Like say if you have a therapy agent and you have like a really great session with it and then next time around it just like completely forgets what you're talking about That probably wouldn't be great So that's why you want to allow your agent to have access to memory So it's able to remember all the different sessions that you've had previously Then we have audio and speech This gives your AI agent the capability of interacting with you through natural language like being able to just to talk to it in a variety of different languages Then we have guardrails Be no good if your AI agent goes rogue and starts doing things that you don't intend it to do So we have systems for that to make sure that your AI agent is kept in check And finally there is orchestration These are processes that allow you to deploy your agent in specific environments monitor them and also improve them over time After you build an AI agent you don't just run away and hope that it works by itself Speaking of AI agents Retool just launched its enterprisegrade agentic development platform Right now there's still a big gap between building AI demos and AI that actually does useful stuff in your business Retool allows you to build apps that connect to your actual systems and take real actions You can use any LM like Claude Gemini OpenAI whatever you want Your agents can actually read and write to your databases not just chat with you It also has endto-end support including test and emails to track performance monitoring access control and a lot more These are all things that are not flashy but really crucial to real implementation in your business Companies that are using retool plus AI are already seeing really genuinely impressive results For example the University of Texas Medical Branch has increased their diagnostic capacity by 10 times Over 10,000 companies already use Retool So if you want to build AI that is actually useful instead of just look impressive do check out retool.com/tina also linked in description Thank you so much retool for sponsoring this portion of the video Models provide intelligence tools enable action memory and knowledge informs decisions voice and audio enables natural interaction Guard rails ensure safety and orchestration manages them all I do also want to point out that prompting is also really really important when it comes to agents especially if you're building multi- aent systems where you're not just having a single agent but you actually have networks of agents that are interacting with each other Your prompts need to be very precise and produce consistent results So how do we actually build these AI agents like what are the technologies for this There are quite a few currently available for no code and low code tools I personally think nend is the best for general use cases and gum loop is great for enterprise use cases If you do know how to code I recommend checking out OpenAI's agents SDK which does have all these components built into it Or if you want something that is free there is Google's ADK agent development kit There's also the Claude Code SDK which is specific for coding agents Honestly these different technologies implementation methods are going to keep changing over time and I'm sure within the next few months there's going to be even more agent builders for you to build agents with That's why I really recommend that you actually focus on this fundamental knowledge about the components of AI agents what are the different protocols and the different systems because this foundational fundamental knowledge is not going to change so quickly and it's going to be applicable to whatever new tool and technology comes out So if you do want to dive a little bit deeper into AI agents I have a video over here that I made about AI agent fundamentals And if you want to get started in building your AI agents I also have another video called building AI agents which you can check out over here as well And I go into a lot more detail about AI agents So these are the components that make up a single AI agent But often times you may also want to build multi- aent systems in which you don't have just one agent but you could have a system of agents that are working together And the reason for this is kind of like if you have a company and you just have like one person trying to do everything in the company it's probably going to not be great right That person is going to get very confused trying to manage everything at the same time So it's much better to have people with specific roles that make up that company Very similar with agents If you just have one single agent trying to do everything then it's going to get confused there's going to be like a lot of stuff that's happening So it's often good to break it down into different sub aents that have specific roles and work together in order to get the result that you want If you want to learn more about multi- aent systems Anthropic has a really great article for that and I'll link it in the description By the way I'll link all the resources that I'm referring to in the descriptions You may also have heard about MCP which is what a lot of people are talking about these days This is also developed from Anthropic and it's basically a standardized way for your agents to have access to tools and knowledge You can think about it like a universal USB plug Prior to MCP it was actually quite difficult to give your agents access to certain tools because all the different websites and all the different APIs they do it in a different way and databases as well They're all configured slightly differently So it was kind of a pain in the ass trying to like connect that with your agent But with MCP because there's a universal USB plug you're now able to give your agents any type of tool and any kind of knowledge very easily assuming it follows the MCP protocol All right here is a little assessment on this agent section Write the answers in the comments Next up let's move on to using AI to build applications aka AI assisted coding aka vibe coding In February of 2025 Andre Kaparthy the co-founder of OpenAI made a viral tweet He says "There's a new kind of coding I call vibe coding where you fully give into the vibes embrace exponentials and forget that the code even exists It's possible because the LMS are getting too good You simply tell the AI what it is that you wanted to build and it just handles the implementation for you And this in my opinion is the new way of incorporating AI into your products and your workflows using vibe coding to build things For example you can simply tell an LM please create for me a simple React web app called Daily Vibes Users can select a mood from a list of emojis Optionally write a short note and submit it below Show a list of past mood entries with a date and a note And you just click enter And the LLM writes the code for you and generates this app And voila there you go But it doesn't just end there There still are skills principles and best practices for how to work with AI in order to vibe code properly and produce products that are actually usable and scalable Let me present to you now a five-step framework for vibe coding with the pneummonic tiny ferrets carry dangerous code dangerous code because if you don't do it properly you could potentially end up like this guy over here who vibe coded an app and then lost all of it because he didn't understand something called version control Tiny ferrets carry dangerous code stands for thinking frameworks checkpoints debugging and context Thinking as it sounds is about thinking really hard about what it is that you actually want to build If you don't even know exactly what it is that you want to build how do you expect AI to be able to do so The best way of doing this in my opinion is to create something called a product requirements document or a PRD This is where you define your target audience your core features and what it is that you're going to use to build the product with I'll link an example PRD in the description but basically you just want to spend significant amount of time thinking through what it is that you're trying to build Next up is frameworks Whatever it is that you're trying to build there has probably been very similar things that have been built before So instead of just trying to reinvent everything and telling the AI to figure everything out it's much better to point the AI towards the correct tools for building your specific product by telling it to use React or Tailwind or 3.js if you're making 3D interactive experiences But Tina you may ask how am I supposed to know what to tell the AI to use if I don't even know what it's supposed to use Great question AI can help you with that too When you're building your PRD ask the AI directly I'm trying to build something that's like you know like this and it's very 3D animationheavy for example and I want it to be a web app What are the common frameworks for building something like this When you're asking in this way you're also learning yourself what are the common frameworks for building specific things And over time you're going to have a much better grasp of what you need to use as well In the era of vibe coding you may not need to code everything by yourself but it still serves you very well to understand the common frameworks that are used for building different types of applications You should also know how different parts and different files in your project are interacting with each other This is going to help you out so much as you're building more and more complex features into your product Third step of the framework is checkpoints Always use version control like Git or GitHub or else things will break and you will lose your progress and you will feel very very sad like this guy who vibe coded an entire application and then lost all of it because he didn't understand version control Fourth step debugging you are probably going to spend more time debugging and fixing your code than actually building anything new That is the reality Be methodical and be patient and guide the AI towards where it is that it needs to fix When you're debugging if you understand the file structures and what's happening then you're much better at providing specific instructions for where in your codebase the AI should be debugging The first place to start when you come across an error is to copy paste the error message directly into the AI and tell it to try to fix it If it's something visual that needs to be fixed also provide a screenshot for the AI The more details and the more context that you give the AI the better it would be at figuring out how to fix the problem And speaking of context the final part of the framework is context Whenever you're in doubt add more context Generally speaking the more context that you provide to AI whether you're building or debugging or you're doing whatever the better the results are going to be This includes providing the AI with mockups examples and screenshots The pneummonic to remember for this five-step framework is tiny ferrets carry dangerous code thinking frameworks checkpoints debugging and context A helpful way of thinking about how these principles of the framework work well together in the process of vibe coding is to realize that there's only two modes that you're ever in You're either implementing a feature where you're debugging your code When you're implementing features you should be thinking about how to provide more context mentioning frameworks and making incremental changes You always want to approach building new things one step at a time Implement one feature at a time as you build your product When you're in debugging mode you should be thinking about the underlying structure of your project where it is that you should be pointing the AI towards changing as well as providing more context like error messages and screenshots So we now know the fundamentals of what makes good vibe coding So what are the actual tools that we use There are a full spectrum of development tools available On one of the spectrum is for complete beginners people who have no engineering background and no coding background Some popular beginnerfriendly vibe coding tools include lovable vzero and bolt Then slightly more intermediate we have something like Replet This is still very beginner friendly but it also showcases the codebase so you can actually dig into a little bit more and understand the structures of the projects Then a little bit more advanced you have something like Firebase Studio Firebase Studio has two modes to it It has the very user-friendly prompting mode as well as a full ID experience which stands for integrated development environment an interface that is specifically designed for writing and working with code In this case it was built on top of VS Code which is a very popular ID With Firebase Studio you can alternate between the no code prompting view and decoding mode Firebase Studio also has the benefit of being free Now moving on to the more advanced vibe coding tools This will include AI code editors and coding agents like Windsurf and Cursor Everything that we talked about earlier was all web- based so the setup is really easy The environment is isolated and it takes care of a lot of things for you But if you really want to produce productionready scalable code then you generally need to start migrating to using something like windsurf and cursor Development is going to be on your local machine So the setup is going to be a little bit more complex but you also have access to a full suite of development tools and different features for Windsor and cursor You just directly have that coding environment that IDE Then on the most advanced side of the spectrum you have command line tools like cloud code for example These are tools that live directly in your terminal in the root of your computer With these tools you need to be comfortable working in the terminal or the command line But it does give you so much more functionality and you can use it with any type of ID of your choosing Something like cloud code really begins to shine when you're working on complex code bases But the expectation here is that you do really need to know how to code and know your way around a computer and have a deep understanding of software All right that is a crash course on vibe coding If you do want to dig into this more I made a full video called Vibe Coding Fundamentals where I go into a lot more detail I also made a video specifically about Firebase Studio which I'll link over here and another one where I talk about the cloud for models and cloud code which I'll link over here too Now I will put on screen a little assessment to see if we have retained information about vibe coding Final section out What are things looking like going into the future In the AI world we don't measure things in terms of years or even months We measure things in terms of weeks And the timelines are just getting more and more compressed When I was at the code with cloud conference Daario the CEO of Enthropic made a really good analogy He says that it's basically like being strapped on a rocket that is going through time and time and space are warping so that everything is speeding up faster and faster and faster And especially because of this if you're just trying to keep up with all the AI news all the things that are coming out all the new models all the new tools all the new technologies you will never be able to catch up with everything and probably get really stressed along the way too So that's why my advice is to not pay too much attention to all the new things that are coming out but instead focus on the underlying trends that are happening And I think there are three major underlying trends The first one is integration into workflows and existing products 2025 is definitely the year in which people are taking the AI and actually integrating it into their existing workflows Prime example of this is Google itself I was at their Google IO conference and they are putting a lot of effort into just making Google products better by integrating AI throughout And I think this should be a model for all companies Think about how do you improve your processes by incorporating AI to have a better user experience and also to reduce your cost And when it comes to implementation of this there's massive productivity boost if you learn how to do AI assisted coding or vibe coding With this full spectrum of coding tools there's a dramatic decrease in barrier of entry for people who want to build things and who may not know how to code But there's also a big push towards increasing the productivity of developers After experiencing command line tools like cloud code I can absolutely see the massive benefits of tools like this And I think there's going to be massive focus of developing and improving command line tools So I think if you are technical or if you're someone who's willing to learn technical things learning command line tools like cloud code is going to be where it's at And finally the focus on AI agents is not going away at all In fact there's more and more interest in building AI agents because AI agents have so much potential in improving existing products and for building new products as well AI agents allow experiences to be personalized available 24/7 and at much much lower cost Like Weissy said for every SAS unicorn company there will probably be an equivalent AI agent company I'm sure in the coming few months there's going to be more and more tools that will allow you to implement and build agents even more easily So if you want to build something build a business do a startup whatever I would recommend looking into AI agents All right that is all I have for you today Here is a final little assessment Please answer these questions in the comments Thank you so much for watching till the end of this video I'm so excited to see all the things that you guys are going to do and build using AI I really hope this is helpful and good luck on your AI journey I will see you guys in next video or live stream