Ever feel like your digital life is just overwhelming,
like you're constantly wading through this flood
of tech news, maybe a dozen tabs open, hitting
refresh, and you still feel like you're missing
the important stuff? Yeah, that feeling is real.
It's this like background anxiety of trying to
stay current, manage your own projects, and maybe
just have a minute to think. What if you could
actually... change that? What if there was this
open source tool that lets you build your own
kind of digital assistant to cut through all
that noise? Well, welcome back to the deep dive.
That's exactly what we're digging into today.
How you can actually build your very own personal
intelligence agent. We've got this really comprehensive
guide here covering how to build a pretty sophisticated
automation system. And our mission today, it's
simple, really. We want to show you how to take
that information overload and turn it into your
own personalized AI powered command center. We'll
walk through setting up the tool, building an
AI news aggregator, exploring direct command
line stuff. setting up a YouTube monitor and
even getting into AI agents that can actually
think and act. You know, that feeling of drowning
in digital stuff. It's so common for anyone who's
really into tech. Your home lab, personal projects,
just keeping up. It can be completely exhausting.
Oh, absolutely. And that's exactly where NN,
that's N8N, by the way, comes in. Think of it
as this like open source automation powerhouse.
It's ready to genuinely shake up your workflow.
N8N. Got it. Yeah, it's got a bit of a nerdy
name, maybe a pun in there, but honestly, the
power is serious. It sounds like it. I've heard
comparisons. Like if the usual automation tools
are, say, a friendly family car, then N8N is
more like a custom -built street -legal F1 car.
It's sleeker, way smarter, and just has this
monstrous engine. That's a great analogy, actually.
And, crucially, it's free. Right. And it really
respects your privacy. That feels like a big
deal. It's not just about automating tasks. It's
about taking back control, keeping your data
yours and building something exactly for you.
That's the core idea. You become the architect.
Yeah. But fair warning here. N .A .N. It's incredibly
addictive. Yeah. Seriously. You might start simple,
maybe just thinking, I'll build a little news
digest. Right. Next thing you know, you're deep
down the rabbit hole building AI agents that
are troubleshooting your servers for you. You've
been warned. Okay. Noted. So for this deep dive,
what's the plan? We're building something. Specific?
Yeah. We're constructing a smart news aggregator
with AI summaries, an automated YouTube channel
monitor, getting instant Discord notifications,
powerful command line integrations, and finally
building a thinking AI agent. Wow. Okay. That's
quite a build. So what's the fundamental shift
this tool offers compared to the more traditional
automation platforms? It's really about moving
away from those fixed kind of rigid rules towards
having that adaptable, powerful F1 car we talked
about. One that respects... your privacy and
lets you build precisely what you need. That
makes sense. Adaptability and control. Okay,
so once you decide you want this power, the first
big question is, where do you actually put it?
Where does it live? Exactly. That's the foundational
decision. Where does your NANN instance run?
Cloud or on -premise? It's that classic fork
in the road. Right. The cloud path sounds like
the express lane, especially if you're just starting
out. Using a cloud provider seems like it gets
you automating faster. It absolutely does. Less
complexity, you don't manage the underlying server,
better connectivity usually. It's quick. Like
how quick? We're talking maybe a five -minute
launch sequence. Sign up, pick a plan like a
virtual private server with NNN pre -installed,
maybe two cores, four gigs of RAM, deploy it,
create your admin account. Done. Five minutes.
So then there's the on -prem path. This is for
the, let's call them the home lab warriors. Ah,
yes. I know a few of those. Right. Running it
on your own hardware, you get absolute control,
maximum data privacy, and honestly, that satisfaction
of running it yourself. Definitely more control,
more privacy, maybe more rewarding if you're
into that stuff. For sure. Full control, data
stays home, personal reward. So for someone just
curious, maybe a beginner, what's the biggest
plus of starting with that cloud path? Speed
and simplicity, hands down. You're automating
almost instantly, no fuss. Right, gets you going
quickly. Okay. Okay, so we know where we could
build it. Let's actually build something, something
useful right away, like that AI news aggregation
machine you mentioned. Let's do it. The core
idea in NAN, the philosophy, it's pretty intuitive.
Think of a workflow like a... A digital Rube
Goldberg machine. Okay. Data goes in, flows through
these different blocks called nodes, each doing
one specific job, and then something useful comes
out. You've got triggers, which start things.
The on switch. Exactly. Data nodes gather stuff.
Logic nodes are the brains doing filtering or
changing things. And action nodes, well, they
do something. Send a message, save a file, whatever.
It's like stacking Lego blocks, but with data.
Perfect analogy. So you start with a blank workflow.
First thing, add a manual trigger. That's your
test button. Lets you run it anytime. Crucial
for building, right? Instant feedback. Absolutely.
But for real automation, you need it to run on
its own. So you add a second trigger, a schedule
trigger, maybe every day at 7 a .m. So you have
both a test button and a schedule. Yep. It's
kind of like a pro setup, a dev mode and a prod
mode trigger. You can turn the schedule off while
you're tinkering. Smart. Okay, triggers are set.
Now, fetching the news. Right. You need a data
gatherer. For RSS feeds, there's an RSS read
node. You just point it at a feed URL, like a
bleeping computer's feed, for example. Just paste
the URL in? Pretty much. Then you run just that
node and bam, that's the aha moment. What happens?
The output panel just lights up. Suddenly you
see all the structured data titles, links, authors,
dates, the content snippets, all pulled neatly
from the feed. Ah, okay. So you see the raw data
grabbed. Exactly. And you realize, okay, this
isn't just clicking buttons. This is actually
commanding information. So what's the core concept
that makes... these workflows so flexible then?
I'd say it's about chaining those specific function
nodes like Lego blocks to build complex processes
step by step. Precisely. OK, so we have the news
data, but data trapped inside NANN isn't very
useful, is it? No, you need to get it out. And
you mentioned Discord. Like a private channel
as a command center. Yeah, exactly. Pipe that
curated news right into a place you already check.
So you add a Discord node after the RSS node,
set its operation to send message. And for connection,
you use something called a webhook. Webhook.
Sounds technical. It's just a special private
URL. Think of it like a dedicated phone number
for automated messages to that specific Discord
channel. You grab it from your Discord channel
settings under integrations. Gotcha. Copy paste
the URL. Then what? Then the fun part. crafting
the message. Instead of just new post, you use
NAN's expressions, little snippets of code that
pull data dynamically. Ah, so you can say like
title .title and it fills in the actual title.
Exactly. Title, author, link, date, dot, format
it nicely. But when you first test this. Let
me guess. Spam. A dozen messages? Yep. Because
N810 runs that Discord node for every single
item it got from the RSS feed. Right. You're
going to tame that beast. So you slot in a limit
node right between the RSS reader and Discord.
Tell it only let three articles through or maybe
five. Okay, that makes sense. Limit the flow.
Test again. Perfect. Just the top few articles.
And here's that pro tip again. Pin your data.
After you run the RSS node and get good data,
click the little pin icon on its output. Pin
the data. Why? It saves that specific test data.
So when you're tweaking the Discord message or
adding filters later, you don't have to rerun
the RSS node and fetch the news all over again
every single time. Saves time, saves API calls
if the source was something else. It's huge.
Ah, okay. So why is pinning data such a pro tip
then for building these workflows? Simply put,
it prevents refetching data constantly, speeding
up your building process and saving costs. Sponsor.
All right. Buckle up. Time to unlock some real
power. This next step is kind of why NAN feels
like it's in a different league. We're going
to add command line execution directly into the
workflow. Command line? Like Linux terminal commands?
Exactly. It's like being handed the admin password
to the server NAN is running on. You're taking
the safety locks off. Okay. This sounds potentially
dangerous, but powerful. How do you do it? You
just add an execute command node, maybe branch
off your main flow. In the command field, you
could start simple, type ping 1 .1 1 .1 money
C3. Ping Cloudflare's DNS from inside the workflow.
Yeah, run it, and boom, real terminal output
appears in the results panel. Whoa. Mind blown,
right? What this actually means is you just ran
an arbitrary command on the server's operating
system. Anything you could type in a terminal,
LS, Doi Clea, Docker OA, custom scripts, anything.
Whoa. Okay, wait. So you could have an automated
check on your Docker containers? Docker WA runs
every hour. Or check disk space with DFTT. Precisely.
Monitor system health, free DSM, run your own
Python or bash security scripts on a schedule.
Maybe even carefully automate server updates
with apt -get update. The possibilities there
are staggering. Imagine an assistant checking
server health automatically. Wow. And to bring
it all together, you use a merge node. Put it
right before your Discord node. Merge. Yeah.
You connect the output from your news filtering
to one input and the output from your command
execution like the ping results to the other
input. Set the mode to append. Append. So it
just sticks them together. Exactly. Combines
the two streams of data. So your Discord message
now has both the news and the server status report.
One single pane of glass. That level of direct
system access fused with web data, that's pretty
unique. Okay. So what's the biggest difference
this direct command line access really offers
them? Freedom. Pure freedom. You're not limited
by pre -built integrations anymore. You can make
your systems do anything. Freedom. Got it. Okay.
We have data. We have system access. Now we need
the intelligence part, right? The brain. Make
it feel like. J -A -R -V -I -S. Exactly. Time
to add the J -A -R -V -I -S layer. We do this
with AI, specifically large language models or
LLMs. Okay. How do we plug that in? You add a
node called basic LLM chain. You place it, say,
after you filter the news articles, but before
they get merged for Discord? LLM chain. Okay.
And so you connect it to your AI model of choice.
OpenAI is GPT -4, Anthropix Cloud, maybe even
a local Alama model if you're running one. Right.
And then you tell it what to do. The prompt.
Precisely. And the prompt is everything. You
can't just say, summarize this. You need to be
specific. Like what? You are a world -class intelligence
analyst briefing a tech CEO. Summarize the following
article in exactly two sentences. Your summary
must be engaging and directly state why this
news is important for a tech professional to
know. Here's the article content. And then you
dynamically insert the article content from the
previous note. Ah, okay. Give it a role, a specific
task, length constraints, the audience. Very
detailed. Super detailed. Pro tips, be specific,
two sentences, give context for a tech professional,
and definitely test different models. GPT -4
often gives much better, more nuanced summaries,
usually worth the cost. Makes sense. And you
mentioned AI for the ping results too. Yeah.
You could add another LLM node after the execute
command node. Give it a different personality
in the prompt. You are a laid back network admin
with a sense of humor. Analyze this ping output.
If it's all good, tell me in a funny, reassuring
way the internet is fine. If there are errors
or packet loss, be serious and flag it. Huh.
That's brilliant. So you get AI checking your
network status with personality. Exactly. And
sometimes getting the prompt just right. I still
wrestle with prompt drift myself, you know. Yeah.
Trying to get the AI to follow instructions exactly
every time. But when it works, the power is undeniable.
Yeah, I can imagine. So the daily briefing goes
from just raw data to actual insight, AI summaries,
AI analysis of system checks. Yeah. So what's
the secret to getting those really useful AI
summaries? It really boils down to this. Precise,
contextualized prompts make. make all the difference
got it precision and the prompt okay that news
aggregator is already pretty powerful yeah but
you mentioned monitoring multiple sources like
YouTube channels that sounds like a step up in
complexity it is but it's totally doable we've
handled one stream of data now we build a system
that monitors multiple sources loops through
them and applies smart filtering the mission
build that personal Scout for your favorite YouTube
tech channels Okay, how do you even get YouTube
updates without opening YouTube? Is there an
API? There is, but there's a simpler way. The
secret YouTube RSS hack. RSS. For YouTube. Yep.
Every single YouTube channel has a hidden RSS
feed. It just lists their latest videos. It's
like a secret backdoor. No API keys needed. Wow,
okay. That's handy. So how do you build the monitor
with that? It's kind of a three -step process.
First, create your watch list. You use a node
called set field to just create a list, an array
of the YouTube channel IDs you care about. Okay.
A list of IDs. Then you need the one -by -one
machine. There's a node called split out. It
takes that list and processes each channel ID
individually. It basically loops through your
list. So it handles the looping for you. Clever.
Exactly. And third, you use the RSS read node
again, but this time the URL isn't fixed. It
dynamically uses the channel ID that the split
out node is currently processing. So it fetches
the feed for each channel in your list, one after
the other. Okay, fetch feed for channel one,
then channel two, etc. But wouldn't that pull
old videos too? It would. By default, it grabs
the last 10 or 15 videos, old or new, which is
noisy. So we add a filter node right after the
RSS read. Filter based on what? Date. Precisely.
We filter based on date logic. The filter's job
is simple. Check each video's publication date
and only let it pass if it meets our condition.
Like, is this video new? And that involves those
scary date expressions you mentioned. It can
look intimidating, yeah. You'll see things like
jcsun .isodate .towermat compared to something
like nasaday .minus .format uiyy -mndd. Okay,
yeah, that looks a bit much. But break it down.
The first part just grabs the video's date. make
sure it's treated as a date and formats it consistently
the second part gets today's date now subtracts
three days and formats that the filter just checks
if the video date is after three days ago so
it's just saying only show me videos from the
last three days exactly much simpler when you
translate it and honestly Don't feel you need
to memorize this stuff. Use ChatGPT. Totally.
Ask your favorite LLM to be your AI code translator.
Describe what you want. I need to filter for
videos newer than three days, and it'll often
spit out the exact expression you need. Works
surprisingly well. Nice tip. So why is that split
-out node, the looping one, so essential for
this kind of monitor? Because it lets you process
a list of items individually, making the whole
thing scalable to many channels. Okay, everything
we've built so far. Powerful automation, yes.
Scripting on steroids. But this next part, it's
different. We're giving the machine a brain.
Welcome to AI agents. AI agents. How are they
different from just an AI node in a workflow?
A regular workflow follows a fixed script, right?
Trigger, fetch data, filter, summarize, notify,
done. An AI agent, it can think, it can plan,
it can use tools to achieve a goal you give it.
Think dot plan. Use tools. Okay, that sounds
like a big leap. It is. Think of the difference
between an old video game character stuck on
rails and a modern NPC in a game like Red Dead
Redemption 2. That MTC has goals, perceives the
world, makes decisions. That's kind of the leap
from a workflow to an agent. Script versus mind.
Okay, I get the concept. How do you build one
in NAN, the J -A -R -V -I -S blueprint? It's
surprisingly straightforward. The core is the
AI agent node itself. That's the brain. a chat
trigger node so you can talk to it. And you hook
it up to a powerful AI model, GPT -4, Claw 3
Opus, something good at reasoning. Right. Brain
chat interface, powerful model. But you said
tools. Yes. An agent needs superpowers, right?
Tools. And in ADN, tools are just other nodes
or even entire workflows you've already built.
Oh. So the ping command we made earlier could
be a tool. Exactly. You define it as a tool for
the agent. You give it the command, ping 1 .1
.1 .1 IC4. And crucially... A description. This
is vital. Why is the description so important?
Because that's how the AI brain knows when to
use the tool. For the ping tool, the description
might be. Use this to check if a public website
is up and responding. This is for testing the
general internet connection. Clear and specific.
Okay. So you could have another tool. Maybe pinging
a local server. Yep. A server health tool. Command
ping 192 .168 .1 .1SE3. Description. Use this
to check if the main local file server is responding.
The server's host name is mainframe. Got it.
Tools are nodes with good descriptions. And then?
Then the magic moment. You open the chat interface
connected to your agent, and you just talk to
it. Like, is the internet working? Exactly. And
the agent receives that. Its AI brain thinks,
okay, user wants to know about internet status.
Aha, I have a tool for checking public websites.
It runs the website ping tool automatically.
Wow. Then it gets the result and responds to
you, yes, the internet connection seems stable
based on the ping test. And if I ask, check if
the mainframe is up. It uses the other tool,
the server health tool. And if you ask something
complex, like give me a full network status report.
It might use. Both tools. It might. If it reasons,
that's the best way to answer. It could run both
pings, then synthesize the results into one response
for you. That's genuinely powerful, having a
conversation with your infrastructure. And this
is just scratching the surface. Imagine giving
it a server medic tool belt tools to check disk
space, find large files, maybe even clear logs.
You could ask servers slow, find out why. Or
a cybersecurity analyst agent, checking firewall
rules. running network scans. The possibilities
just explode once you start thinking in terms
of agents and tools. It's a completely different
way of interacting with your systems. Okay, so
what's the single most important element when
giving an AI agent one of these superpower tools?
Without a doubt, the tool's clear, precise description.
That's what guides the AI's decision -making
process. Now, as you build more of these, whether
simple workflows or complex agents, you'll want
some best practices and automators code, if you
will. Okay, lay it on me. First, save early,
save often. Just Shreetrol plus S, CMD plus S
all the time. Seriously, workflows can get complex
fast. Good habit. What else? Pin your test data.
We talked about this. Saves so much time, saves
API costs, especially when tuning AI prompts.
Just pin the input data to the AI node and you
can tweak the prompt over and over without new
API calls. Right. The pinning tip. Crucial. Also,
duplicate for experimentation. Before you try
something major, just duplicate the whole workflow.
It's your safe sandbox, like saving your game
before a boss fight. Makes sense. Don't break
the working version. And related to AI, watch
your AI tokens. Refining pumps with pin data
helps, but be mindful of how much you're asking
the AI to process, especially with expensive
models. Got it. Save, pin, duplicate, watch tokens.
So once you master these basics, what's the next
quest? What other cool things could you build?
Oh, man, where to start? You could build AI that
rates news articles from 110 based on your specific
interests or an AI that summarizes YouTube transcripts
to tell you if a video is actually worth watching.
Transcript summary. I like that. For the home
lab folks, build that master health check agent
we talked about or a chat bot. You can just ask
questions about your infrastructure, integrate
with security cameras, maybe use AI object detection,
automate social media posting. OK, lots of ideas.
What about when things go wrong? Common problems.
Good question. Quick troubleshooter. Connection
failed. Almost always check your credentials
or maybe an IP block on the server side. Workflow
is not triggering. Double check the active toggle
is on and look at the executions log for errors.
What if the AI responses are just... Weird. 99
% of the time, it's a prompt problem. Go back,
refine your instructions, check the raw JSON
view of the output. Sometimes the AI adds weird
formatting. And getting too many notifications.
More limit and filter nodes. Be aggressive. Automation
should reduce noise, not add to it. Right. Okay,
so beyond all the technical stuff, what's the
mindset shift someone needs to really embrace
this kind of automation power? I think it's starting
to see the digital world less as a series of
websites and apps and more like a giant Lego
set. Everything's potentially connectable, ready
for your creative connections. A giant Lego set.
I like that. So thinking back on what we covered,
it feels like a significant journey. You start
with maybe feeling overwhelmed. And you end up
deploying your own private automation engine,
not just using a service. You have control. You
built an AI intelligence agent analyzing the
world for you. You gave your workflow direct
OS access, maybe most profound. You gave an AI
a brain and tools to think. It really is like
that moment in The Matrix, you know, where Neo
stops seeing the bullets. And starts seeing the
code. Exactly. APIs, servers. Data streams, they
stop looking like chores and start looking like
interconnected systems, a vast play where that's
your new superpower, understanding the code,
making the connection. And it feels like the
potential is just huge. You mentioned over 400
pre -built integrations, plus building your own.
It's not just endless possibilities, it feels
like. Yeah. Like exploring this vast archipelago,
maybe Ha Long Bay with all its islands. Each
island is a new idea, a new connection waiting.
That's a beautiful way to put it. And it leads
to the real question, doesn't it? It's no longer
if you can automate something. Right. That's
simply what you will choose to automate first.
A powerful question indeed. Well, thank you for
joining us on this deep dive today. We encourage
everyone listening. Keep exploring. Keep building.
Absolutely. And always embrace that curiosity
that drives this whole journey forward. Outro
music.