In the past year, I've built over 200 AI automations in Naden. These automations have either been for a real client, for YouTube videos, or for me personally in my own business. And when I look at all these different automations that I've built, I notice that I'm using roughly the same 17 nodes throughout all of them. So, if you want to get started with Naden, I would just focus on these 17 core nodes first and then branch out when you need to. So, in today's video

I'm going to be breaking down those 17 nodes that I'm talking about and I'm going to show you guys real examples of workflows that I've built that have those nodes in them. And I know you guys like to take notes, but just sit back and watch because I threw together a 39page document with all of these 17 nodes with different examples, how to use them, best practices, and stuff like that. And so you can grab this entire doc for free. All you have to do is join

my free school community. The link for that is down in the description. So super pumped about this one. Let's not waste any time and get straight into the video. Okay, so not counting the manual trigger here as the first node, we're going to count number one as the scheduled trigger. So what this does is it lets you trigger your workflows based on some sort of schedule. So that can be once a day. It can be every day at 6 am and 6 pm. It could be every Tuesday.

Whatever you want it to be. This is how you can make automations that go off while you're sleeping. And all you have to do to configure it is double click into it. And you can see you can set the interval as days, seconds, minutes hours, weeks, or months. Or if you really want to get technical, you can do a custom cron. And then you can say how many days between trigger, what hour do you want to trigger at? What minute do you want to trigger at? So you can get

really, really specific here. So a real example of a workflow that I use with a schedule trigger is my personal AI news summary. So, I have this thing go off every day at 5 a.m. It scrapes the internet for latest AI news and some other topic of my choice. Right now, I'm having voice AI be in there. And then it sends that to two agents that will write me a newsletter and it's trained on my data and what my business is. So, it makes sure that I'm getting news tailored towards me. And then it

stylizes it and chucks it to my email every, morning., All right., So,, number two, we have event triggers. Now, these event triggers can be a lot of different types of apps. You can see here as an example, I threw in a Gmail trigger and a Slack trigger. And so this one would go off whenever you get a new email. This one would go off whenever there's a new message in a channel. But this doesn't have to be just messaging platforms. It could be a new record in your CRM. It could be a new form

submitted on your website. It could be a new payment processed. I think you guys see where I'm going with this. And so a real example that I'm using right now is one of my email agents. And this one is kind of just classifying emails labeling them, and then just shooting off kind of a basic draft or moving them around. And as you can see, the trigger over here is a Gmail trigger because every time this email account gets an email, it will go ahead and take care of

it for me. And once again, another super simple, workflow., All right,, number three, we have subworkflows. Now, these are super super powerful. And one of the things that I saw about Niden that made me switch over to it. You have the ability to have any workflow be triggered by another workflow. And you have the ability to have any workflow call on another workflow. So, think about it like this. You build a tool. Let's say you build a send email tool. You can now have an infinite number of

your other workflows use that tool. So you never have to build that function or that tool again. And these make your agents and your workflows extremely modular and scalable. So an example that I wanted to show is we have this marketing team AI agent. And you can see it's calling on these different tools down here. And all of these tools are different end workflows. So let's say it wants to call on the blog post tool. Okay. If I click into this workflow to see what it actually is. You can see

that it starts right here with a when executed by another workflow trigger. And so that basically links this workflow back to this parent workflow. And now our workflows can talk to each other and send data between each other. All right., So, number, four, right, here, we have split out. Now what this does is it takes an array of items and it splits them out into separate ones. If I run this node real quick, we can see that we have a list of sports. We have golf

baseball, and basketball. But you'll notice right here it's coming through as one item. We can also see right here the green line right there it says one item. And now if I run the split out node what it's going to do is it's going to output three items because it took this field of sports and it outputed each of those sports as their own individual items. And why that's helpful is because now our AI agent is actually going to run three separate times. Because however many items go into a node

that's how many times that node's actually going to run. So if we want this agent to write a five sentence description about the sport that you're given, and I execute that now, it's going to, output, three, different descriptions. One about golf, as you can see, one about baseball, and then one about basketball. But if we wouldn't have used the split out node, it would have gotten only one item and it would have just tried to make one sentence about all three of those sports

combined. But you can see the agent also outputs three items. And so one example of that in real time is this AI versus workflow that I built for YouTube. It's taking a type of animal and it's going to create different scenes. And what we need to do down here where it's creating the close-up images, we can't have it just run one request to make those images because we have like eight different animals. So we have to split those up so that we can make eight

different requests to PI API to actually make those requests. So right here you can see we have eight items leaving the image prompt generator and then we have 16 items leaving the split out. And so now this is actually going to make all the different images that we need because we were able to use the split out node. And then literally the exact opposite of splitting out is aggregating. So once we have three items, if we want to turn it back into one for whatever reason, maybe want to

process them all together, we can go ahead and aggregate them. So, if I go ahead and run this one, you can see that we turned three items back into an original list of sports and we have our sentences about each of them. As you can see, we've got golf, we've got baseball and then we've got basketball down here. And so, in this same exact example where we're basically splitting out all of the images so we can make more. What do we have to do later? We have to bring them

back together so that we can write them back into the Google sheet. So, you can see we have this pattern of splitting out, aggregating, and then we're aggregating back again so that we can have everything come together in one final video that we render at the end. And if you want to play around with this template for free and watch the video I'll leave that link up above. All right, so number six, we have the edit fields node. Although you'll probably hear me referring to it as the set node

because essentially what you're doing here is you're just setting data and you're setting the data types. So you can see that we set four different things. We set a field called name as a string and it's Nate. We have a field called age as a number and it's 23. We have a field called male and it's a boolean and it's true. And then we have an a field called interests and it's an array. and we have AI traveling and dogs. And so this lets us take full control of our data. We can change the

variables, we can change the types, and we can set things if we want. And this node is super super versatile. So I'm going to, show, you, guys, three, quick examples real quick of how I've used it before. This first one is what I like to call just the source of truth. So this was a travel agent video I did a while back. We got data from a web hook and I immediately just set it all right here. So you can see I was setting the origin the destination, the departure date, the

return date, travelers, all this information just so later on I knew that whenever I needed to reference that data, I could just reference back to this core node. Now, another way that you can use it is to keep things really dynamic. So in this human in the loop workflow, what I did was I used it here to make sure that this was always the most recent version of our email. Basically, the sales agent would make an email and then we'd have the revision agent make revisions over and over and

over until we're happy. And so the set node was the answer to make sure that we're always looking at the most recent version of our email. And then finally you could also use it to be able to change data. So in this YouTube strategist workflow, when I gave this to you guys, I said, "All right, when you guys, get, in, here,, what, you're, going to do is you're going to come into this broad niche node and you're going to set your own broad niche. And then you're

going to come into the niche node and set your own niche. You're going to come into the channel URL node and set your own channel. And that's how you could set data. And then when you hit run it'll pass through the rest of the flow dynamically with your information. All right. So the next one is the if node and this one is basically just going to check if a condition is true or false. So in this case what you can see is that I put we're checking if the person's age

from this left-h hand side is greater than 21. So this is our if node bouncer and then when we hit execute step it puts it down the true branch not the false branch because it sees that my age right here is greater than 21. And what happens is it actually goes down two different paths. So you can have data get sent this way if it's true and this way if it's false. So, a cool example of this is something called polling. As you guys might have seen in a lot of my

video generation type of videos. So right here, what we're doing is we're making a request to Sora 2 and we're basically asking it to turn our text into a video. And so, what happens right here is the first node is making that request to Sora and then the second node is basically saying, "Is it done yet?" So, when you go order fast food and they give you number 43, you're basically waiting, and, then, you're, going to, go, say, "Hey, I'm number 43. Is it done yet?"

And then the if node is either saying "It's done or it's not done." And you can see twice it's already said it's not done yet. So this if node basically is checking is the state success or is the state still generating. And so every time that it checks in and the state isn't success, it's going to go down the false, branch., It's, going to, come, back and wait again. And then it's going to check again. So we're basically stuck in this infinite loop until state equals

success. And that's all thanks to this if node right here, which is checking on our, condition., All right,, so, that, just finished up. It had to check in 16 times. And you can see that the final run the state equals success. And that's why it finally pushed it down the true branch. And also, I didn't include it on this list, but honorable mention to the weight node. Also super powerful. All right, so moving on to number eight. This is a switch node. Now, this is

very, very similar to the if, except for it lets you have way more than just two paths. You could come in here and you could add as many as you want. So you could add eight different routing rules if you wanted, as long as they don't like contradict with each other. But here you can see we have four different paths. So a quick example of that switch node. You can see right here, this is a rag pipeline, and we're checking in this Google Drive what type of file we're

going to have. So, in this switch node you can see that we're either checking if it's a PDF, a text file, an Excel, or three different types of Windows docs. And once again, in here, we're just setting those different conditions. And if it meets that condition, it will go down that respective path. All right the next one we have, number nine, is a code node. Now, don't get scared because I know we're trying to typically build workflows that are no code and low code

but sometimes the code node is super super effective and powerful because the code node is really good at taking predictable structure of data and manipulating in some way. And so, if you can do something with code rather than AI, you'd rather do it with code because it's, going to, be, cheaper,, quicker,, and more predictable. So, as you build more automations, you'll get more familiar with when you may need a code node. But typically the way I like to think about

it is if I'm getting the same type of structure of data every single time and I need to do something based on that structure rather than something really creative, then I'm going to try a code note first. But anyways, of course, I wanted to show you guys a real example and I don't know why I just copied this example in rather than going to the actual workflow like I've done for all the other videos. But anyways, this one was my YouTube transcript sort of metadata rag agent. So what I do in the

form is I drop in a YouTube video link and then ampify HTTP request gets the transcript from that video. Now in the transcript it's super messy because what you can see is we get these different chunks of data and they all have the text and also like 2 seconds of actual words. And this is just really really messy to work with. But I recognize that every time I made this request to Ampify, it always came in this structure. Even though we weren't sure if there was going to be 50 total blocks

or 46, we knew that it would hold this structure. So what I did is I worked with Claude to help me write this code. And so the first one is a transcript one. It takes all of the text fields as you can see and it just combines them all together. So now we have this field right here called combine text. And this basically is the actual transcript that we want to play with. But I also wanted to get a little more granular here and I wanted to get timestamps. So what I did

down here is I worked with Claude once again to give me timestamps. So you can now see we have 31 items and each of those items have the text. But then for each of those blocks of text, we have the start time, the end time, and the duration. So now we're getting different chunks of text that we can vectorize. And for each of those chunks, we can see exactly where it came from in that YouTube video transcript. So really the key here, because I don't know how to

code, is to use an AI assistant like Claude, cursor, whatever you want to use, but you have to give it here's the input, here's what I want you to do, and then you test it. And it's usually not going to work the first time perfectly. So then what I would do is I would say "Okay, here's the input. Here's what you gave me, and here's the output I got and here's why that's wrong. So here's what I'd rather get." And you just have to go back and forth a couple times, but

typically that's how I do all of my code nodes., All right,, so, the, next, one, is number 10, HTTP requests. This is probably the most important node that you'll have to learn in NN because without an HTTP request, you're basically just stuck to the internal environment of NAND itself. And just keep in mind when you click on this plus and you click on action in an app and you see these thousands of integrations all of these are HTTP requests. They're just not as scary because they're

packaged up nicely because any went out and made an integration for it. So like when you're sending an email or you're getting a record or you're making a call to Plexity, all of this is an HTTP request. It just was wrapped up a little prettier for you. And most things nowadays have an API. You would just have to go find out if it does. So let's say you want to work with something like Fathom and you can't find it right here in Nitn because there's no native Fathom

integration. You would just go to Google, you would type Fathom API documentation and then you would basically be able to find it right here and you can go to the API docs and set up your endpoint, your body, your parameters and all this kind of stuff. So you would just type in your tool name and, then, API, documentation., All right, so number 11, we have the loop. Now, the loop is super important because sometimes you may have hundreds of items in your workflow and you don't want to

process all a hundred at the same time because you may hit rate limits or you may have a memory issue or whatever it is. So, the loop basically lets you process them in batches or loop over items. And I think originally this node was called split in batches. So, if you're using some AI assistants or looking up old reference docs, you may see split in batches, but now it's called loop over items. And in here, it lets you customize the batch size. It also lets you customize if you want to

have a loop branch, a done branch, and like an error branch, whatever it is you're able to take a larger list and just do it one at a time. So, if I go ahead and run this one, what you can see is that we're outputting a list of URLs. We have three items. And then what it did here is it ran each of those one at a, time., And, so,, that, was, kind, of a, quick example, so it was hard to see, but some ways that I've used this before have been in something like this, a rag

system, where maybe we're processing five files at once, but we want to just be able to download them one at a time and then continue on. or something like this scraper that I've made. So, this actually uses another one which is the execute a different workflow node. But what it would do is right here we have 97 items going through and I don't want to run all 97 at once. So, I'm utilizing a a loop to send them one at a time to this other workflow. And then I'm also

utilizing a weight so that we're not overloading the workflow. And then it's going to go ahead and grab the second one, run it, grab a third one, run it. And that's how we can basically keep control of our workflow. And by the way all of these workflows that you guys have been seeing me flip between, you can go ahead and download for free in my free school community. And speaking of free workflows, I wanted to take a quick second to show you guys how I've been

self-hosting my NDN that comes with 100 plus free workflows and unlimited active workflows and executions. So, I'm using Hostinger VPS for my NEN self-hosting. As someone who doesn't have a DevOps background, Hostinger makes it super super easy for you to update your instance, maintain it, scale it up, and do what you need to do, especially because they have an AI assistant that can help answer all your questions. So let's just take the next 30 seconds and

let me show you guys how quickly you can get set up. So, here are the different plans. Don't worry about the specs. Right, now,, I'm, just, going to, go, with KVM2, which is the most popular plan because you can always change this later. So, once you're in here, you pay for a certain amount of months for your VPS. In this example, we're looking at 24 months, which actually comes out to just $6 a month, which is insanely cheap. You can also enable your daily auto backups for another six bucks a

month, which is just going to help you sleep at night a lot better. Then, down here at the bottom, you need to select an operating system. You can see right now I just have the basic end. There's niten with 100 workflows. There's nodn with q mode. And there's lots of other things that you can add on later. And so the last step here is if you're purchasing a annual plan, so the 12 or 24 months, you're able to use coupon code Nate Herk and get 10% off your entire order. And as a special treat

anyone that is in my plus community can actually receive a larger discount. So check out that link in the description for more details there. All right, so there we go. Here's our dashboard. There's a few different settings that we'll, be able, to, look, at, and, some metrics we'll be able to track once our instance has been running for a while but what you guys want to see is what Nin looks like. So, I'm going to go ahead to the top and click on manage app. And we're going to go ahead and get

to set up our Nitin account right now. And as you can see, this is just the same as your typical NIDN workflow. I'll go ahead and open up a new one so you guys can in fact see this is regular NIDAN, except for now, we're just not being limited on our workflow executions. So, if you've been wanting to switch to self-hosted for a while then this is your sign. Click on that link in the description and use code Nate Herk to get 10% off. But let's get back to the video. Okay, so now we have

number 12 and 13, which are the web hook and respond to web hook nodes. Now, the web hook is actually very, very similar to what we talked about earlier up here with the event triggers. It's kind of the same relationship as HTTP requests where sometimes we have a native node sometimes we don't. And so these two triggers are actually just web hooks but they've just been natively integrated with NAND and Gmail or NAND and Slack. So maybe you notice that your CRM or your service doesn't have a

native web hook or a native trigger. You could go ahead and set this web hook by yourself. And then what the respond web hook lets you do is it lets you send data right back to that original source if you need to. You don't always need to, but sometimes you do. So if you've seen any of my videos where I built with lovable or base 44, you see how I have the landing page that I built send data to nen. N processes it with some sort of agent and then it sends that data back

to the front end for it to be displayed over there. Another example of this is something like my voice email agent that I did with 11 Labs. So 11 Labs would send the request to N. Our email agent would take action and then it would tell 11 Labs that it actually took that action. So these two nodes let us get really powerful by connecting to other services., All right,, coming, down, to number 14, which is my personal favorite, good old AI agent. And really when I say I agent, a lot of times I

just mean an LLM because within this node, you don't even have to connect it to tools or memory to really make it like a true agent. You can just use Open Router or a different chat model to connect to any LLM you want and have it process your data and output some sort of decision or some sort of creative output. And there's so many levers in here that you can tweak. You can give it a system prompt. You can have it look at different things. You can, of course

give it memory or tools or a structured output parser. And so, you actually may notice, in, a, lot, of my, workflows,, I, have AI agents even though they're not doing any tool calling. And that's simply because I like to just keep it consistent. So, whenever I need an LLM I'll typically just grab an AI agent. I'm really used to the interface. I think it might make it easier for you guys too to always see the same thing. And then if I do want to add a tool I've already got that functionality

right there. And now, just like we talked about with tools, that's number 15, which actually lets your agents have the ability to take action on something. And so this is basically any sort of HTTP request. It could be this native node like send an email in Gmail. But if you click on tools, you can also see it could be calling another end workflow like we saw earlier. It could be making an HTTP request. It could be connecting to an MCP server. It could be a vector

store. It could be a database. It could be air table. It could be any of these integrations that we have. And like I said, your agent's able to not only fill in the parameters by itself, but it's also able to decide, okay, I have these six tools. Here's the request I got. Here are my instructions. Which tool or which tools do I use to achieve the end goal? And what's cool about these AI tools, like I said, is the agent is able to fill in the different parameters. So

in this tool, you can see when you send an email, you typically have to configure who's it to, what's the subject, and what is the message. And all of these, you can see, are being defined automatically by the model. So if I open up the agent and I say "Please send an email to nate@acample.com asking him if he wants to get lunch today or tomorrow." I go ahead and shoot this off. It's going to use its brain. It's going to call the tool and it's going to fill in the two

the subject, and the email. So if I click into this, you can see that it filled out the two as nateample.com. It made the subject lunch plans, and it made the message, "Hi Nate, I hope you're doing well. Do you want to get lunch today or tomorrow? Let me know what works best for you. best regards. And then it didn't sign off because we didn't give it a personality. So, that's awesome. Now, let's say we got rid of this tool and we wanted this agent to create that output, create that email

and then we wanted to just use, you know, like a typical Gmail send message node to actually send it off. Well, what you'll, notice, is, we, have a, two,, we, have a subject, and we have a message. And we can't just drag the output into one of those because it's all lumped together. So, what we have to do is that's when we use a structured output parser. So, I'm going to go ahead and connect that structured output parser. You won't natively see that option right here.

You'll have to click into the agent and then toggle this on. And then it allows you to connect a structured output parser. But all we're doing in here is we're basically saying this is how we want you to output information. You have to output a two, a subject, and an email. And so now, if I basically just run that same exact query once again this agent will use its structured output parser to make sure that it gives us that output. It gives us a two, a subject, and an email. And then we're

able to turn this Gmail node back on. And all of these fields are individual and we can actually drag them into the three separate parameters right here. And then our agent could go ahead and shoot off that email just like we wanted to. So understanding how to use this tool is super super crucial. And once again, I would never go write this by myself. I would use Claude or Chat GPT to help me write the structured output parser. All right. And last but not least, one of my favorite all-time

nodes, Google Sheets. This node is so versatile. It's free. I hardly ever run into rate limit issues. And being able to store data externally somewhere third party is really really important to know how to use because sometimes like in this deep research workflow we have so much data to process that I don't want to keep it throughout the entire flow. So in this flow it's doing a bunch of deep research and it's getting different chapters. And so what I'm doing at the

end of each chapter is I'm writing all of that data back to a Google sheet. And then what I'm able to do at the very end when we're actually formatting its sources in our table of contents, I'm pulling it all back in and then we can turn it into a PDF. So it's just so much data to work with. It's nice to be able to grab some, go store it, grab some more, go store it, and then at the end grab it all and bring it back together. And now another cool way to use it is

actually storing information that we can then process. So for this UGC ads workflow, what I do is I have a Google sheet like this that's storing information about our products and our features and stuff like that. And so this lets me, the user, fill in my products, my product photos. I can choose the different model I want, and then I can mark them off as ready or finished. And so it's a really easy place for me to just manage all my creatives here. But the workflow itself

is going to be making sure to check that it's only pulling in records where the status equals ready. And it's only going to be pulling in one row at a time. It's also then later using a switch node to check if the model was selected as VO3.1, nano plus VO, or Sora 2. And you can see once again we're able to control all of that right here from the front end which is our Google sheet. But anyways that is going to do it. That is the 17 of the most common nodes that I

use in Nen across all of the different workflows I built 200 plus which have been for real clients for YouTube videos and templates for you guys and for automations that I use myself every day. And trust me this workflow here of all these different random nodes this is not holistic of all the different types of use cases and things that I've built or experienced. So once again, I chucked all of this into this 39page document where I highlight each of the nodes

what they do, best practices, when to use them, all this kind of stuff. As you can see, it's pretty holistic. And you can get this for completely free by joining my free school community. The link for that is down in the description. Once you get in here, all you'll have to do is go to YouTube resources or search for the title of the video or go to the classroom and look at free and templates. You'll be able to find it in here, trust me. And once you do, let's say that this is the video

that you're looking for, there'll be the PDF attached right here. So, you'll be able to download that and use it for later. And if you're interested in taking your learnings a little deeper and connecting with 2,800 members who are building businesses with AI automations, then definitely check out my plus community. The link for that's also down in the description. We've got four full courses in here. We have agent zero, which is the foundations of AI for beginners. We've got 10 hours to 10

seconds where you learn how to identify design, and build times automations. For our premium members or annual members we've got one person AI agency where you learn how to lay the foundations for a scalable AI automation business. And then we have a new one, Subs to Sales where I basically teach you my process for how I make YouTube videos and how I was able to build a content engine that fuels my AI automation business. We've also got projects, which are basically

step-by-step tutorials and projects that you guys can follow along with me on where you see pretty much live how I build things and why I'm doing what I'm doing. We also run one live call per week in here. So, I'd love to see you guys in those calls in the communities. But that's going to do it for today. So if you enjoyed the video or you learned something new, please give it a like. It definitely helps me out a ton. And as always, I appreciate you guys making it

to the end of the video. I'll see you on the next one.