I'm going to show you exactly how I'm
using OpenClaw. I have spent countless
hours over the last few weeks exploring
every nook and cranny of what is
possible with OpenClaw. I truly believe
I am one of the most advanced users of
OpenClaw on the planet. And I'm going to
show you everything that I've learned.
And it all starts right here. This is a
fresh MacBook Air. I wiped it clean and
I installed OpenClaw on it. This thing
lives right on my desk. I have it in
clamshell mode. So when I close it, it
does not shut off. It is running 24
hours a day, connected to the internet.
And I make it easily available anywhere.
I installed team viewer on it so that if
I'm remote and something happens and I
need to change something directly on the
computer, I just team viewer into it and
I can do that. I also set up tail scale
so I can easily SSH into it. So if I
want to code something with cursor on
it, I can SSH in from any other computer
and that one just sits on my desk. Here
is the high-level overview of everything
going on in my system. And I know it
looks complicated. I'm going to get into
detail about all of it. All right, so
here is the overview. First, we have the
interfaces. This is how I'm actually
talking to my openclaw. I use Telegram
as the primary one. And if you watched
my previous video, you know I'm using
Telegram groups. I have a bunch of
different topics. I keep all of the
topics very narrow, very niche, and I
actually don't have it start new
sessions anymore. Previously, a default
setting in OpenClaw is to start a new
session every day at like 4:00 a.m. That
means it basically forgot everything in
the chat prior, which would make sense
if I had a single DM that spanned
forever, but instead I have all of these
individual channels, so I didn't want to
do that anymore. And the solution, I
simply had it set the expiration of a
session to be one year. And so look at
all these different sessions. I have my
knowledge base, my food journal, cron
updates, video research,
self-improvement, business analysis,
meeting prep, all of these different
ones, and it allows me to stay on topic.
All right, so now back here, second, I
have Slack, but I have it very narrowly
implemented in Slack. It's only
available in two channels, and it's only
available to me. I am the only one who's
able to invoke my open claw. If somebody
else tries to invoke it, it just ignores
it. Then of course we have our command
line interface. I sometimes SSH in and
we also have scripts. I'm using multiple
models of course. So we have Anthropic,
Opus, Sonnet and Haiku. I'm using Google
Gemini for a bunch of stuff. We're using
XAI Gro and XARCH. And of course we're
using Open AI. And it already has after
just two plus weeks of using it a ton of
different skills. I use it as a personal
CRM. I use it as a knowledge base, a
video idea pipeline, X and Twitter
research, business meta analysis. That's
a crazy one. Wait till I tell you about
that. HubSpot Ops. I always apply the
humanizer skill. I want all the text
coming out of it to be humanlike, not AI
like. No M dashes. I plugged it into my
to-do list and I kind of use it as a
task management system as well. I track
all of the usage across everything I do.
And of course, I use it for YouTube
analytics. And I'm also storing a ton of
data. I actually try to store all the
data I possibly can. And I usually do
that in a traditional database mixed
with a vector column. So I want to be
able to do traditional SQL searches, but
I also want to be able to do natural
language searches using the vector
column. And so I created a standardized
way of creating this hybrid database and
I use it across a bunch of different
skills. So I have my contacts in my CRM.
Again, I'm going to go over all of these
use cases. So I have my knowledge base,
I have video pitches, my business meta
analysis, views of my social channels,
the cron log, everything is stored. Then
I've plugged in a bunch of different
external services. We have Google
Workspace via GG. I have a sauna. I have
HubSpot. I have to-d doist, Fathom,
Brave, and GitHub. I have X. I have
YouTube's API. So much is happening
right now. And by the way, a quick shout
out to the sponsor of this video,
Greile. I use Grapile to review all the
code that I'm putting out for OpenClaw,
and it's a lot. And it catches bugs that
I wouldn't have seen, that even Opus or
GPT 5.3 Codeex wouldn't have seen. And
it's just a great system to really shore
up all of the rough edges of my OpenClaw
instance. These guys at Grappile spend
all of their time thinking about code
review and it helps me and my team save
a bunch of time. It's used by some of
the most popular repos in the world
including OpenClaw, Nvidia, PyTorch,
Postto, Storybook, and many more. Don't
waste your team's time on code review,
but code review is still critically
important. Let Greile do it. Try Reptile
for free for 14 days. I'll drop a link
down below. They've been a fantastic
partner. So, help us help yourself. Go
check them out. Click through the link
and get your 14 days for free. All
right. So, the first workflow I want to
talk about is my personal CRM. It is
incredible because it allows me to plug
in all of this builtup knowledge into
everything else I'm doing. Let me just
explain. So, I basically have my
OpenCloud download all of my emails and
start to parse through them, understand
who's the email from, filter out
contacts that I don't want saved, like
newsletters and cold outreach, cold
pitches, and then it finds the highest
quality contacts, and starts saving it
to my CRM. It reads all of the
downloaded emails, builds out a graph,
builds out an understanding of all the
conversations I've had with each
contact, and starts to save that in the
database. and I have it do that every
single day. So, it's always up todate.
It always knows who I'm talking to, what
I'm talking about, and it's just super
helpful because I can always ask
questions like, "Who is the last person
I talked to at Grapile and what did we
talk about?" Or, "Who else do I know at,
you know, this other company?" And so,
this is how it works. I have the daily
ingestion trigger, which is a cron job.
It downloads Gmail and my calendar, by
the way, not just Gmail, also my
calendar. It extracts people from the
senders and participants. It dduplicates
everything, merges contact records. It
uses AI to classify the role in context
and I use a very inexpensive very fast
Gemini 2.5 flash model I believe to do
that classification. Then we start to
update the timeline in Last Touch. It
does semantic indexing and it sends me
updates via Telegram when I want it and
I can also just query against it as I
said earlier. Now, where this is also
very useful is in my meeting prep
workflow. Every single day, first thing
in the morning, it looks at my calendar
for the day. It filters out events that
don't have anybody or only have my
internal team. And it basically gives me
a meeting prep for the day. It says,
"Hey, you're meeting with this person.
Here's the last thing you talked about.
Here's what they want to talk about in
this meeting. Here's who they are." And
it's just super helpful to keep me up to
date going into these meetings. All
right. Next is my knowledge base. I am
constantly on X and searching the web
and reading articles all about
artificial intelligence. And I wanted a
single place where I can just throw
everything interesting that I find into
a single repository and I'm able to use
natural language to search against it.
And again, all of the things that I'm
building, I want to be able to use
across different workflows. That's
really important. And so when I get to
the video ideiation workflow later,
you're going to see it actually
references different articles that are
that have been saved to this knowledge
base. And so the way this works is I
take a file or URL, I drop it in
Telegram, it detects the source type,
extracts all the information from it,
normalizes it, chunks it, puts it in the
vector database, and then stores it. So
then when I have a question like, find
me all the articles about the Opus 4.6 6
model. And so I put that user question
in, it embeds it, it grabs all of the
candidate articles, and then it answers
with sources. So now I'm building up
this infinite knowledge base of
everything that has interested me and it
just stores it and I can always
reference it. If I'm creating a video
and I want to reference a previous
article, it is now so easy to find.
Okay, next. And this is one of my
favorite ones. This is the video idea
pipeline. This was something that I
spent a ton of time on. The way I was
doing it previously is I would drop
links, again, something that I'm now
doing via the knowledge base into Slack
to share with my team. We would talk
about it and the ones that interested us
the most, we would decide to make a
video about. Then I would create an ASA
card. I would do research, find all
relevant articles, find relevant
exposts, and put it all together in that
ASA card. And it would just, you know,
take a lot of time. But now I don't have
to do any of that. So first remember the
previous knowledge base? Well, part of
that workflow is when I drop an article
in Telegram and it gets all that
information, it posts to the Slack
channel for me and it says, "Hey, this
is what Matt's looking at." And we can
have that discussion there. Also, if
somebody from my team drops a link in
Slack, I can actually tag my open claw
and say, "Hey, let's make a video idea
about this." And so both of these ways
work. Let me show you what happens after
that. So we have this idea trigger. It
either comes from Slack or Telegram. We
parse the video topic or intent. Then it
does research on X and it also does
research on the web. I don't know why it
doesn't say that here, but it does. Then
we query the knowledgebased context. So
it looks for potential articles that
might be relevant. It comes up with
video pitches and make sure it hasn't
pitched us something like that already.
Then it starts to build hooks and an
outline for the video. It links all
sources, creates that ASA task, and then
sends confirmation to wherever I invoked
it. All of this happens in like 30
seconds. Now, super super valuable,
right? So, here's an example. In
Telegram, I dropped this link. So,
Thomas Donkey, former CEO of GitHub,
started a new company. Sounds
interesting. I dropped it in here. It
saved it into my knowledge base. Then it
also pushed a quick summary and link to
the Slack channel where we talk about
all of this stuff. Then if we wanted to
create a video about it, this is about a
different topic, but I said, "Whoa,
Claude, short video idea." Then Claude
jumps in, does a bunch of research, adds
the card, and everything. It's just so
easy. Now, now you're seeing how all of
these different pieces work together to
create this incredibly autonomous system
that saves me a bunch of time. And by
the way, if you want to see 25 more use
cases, my team put together a completely
free ebook all about open claw use
cases, specifically how to implement
them, how to get them going, why they're
valuable, and so much more. Go download
the ebook. It's completely free. I'll
drop a link in the description below.
Okay. Next, I built out an entire
workflow just for searching on Twitter
because I do so many searches on
Twitter, whether it's getting the data
from specific posts, and there are so
many different ways to do it. I actually
built an entire fallback daisy chain
system that handles it for me and it's
costoptimized and yeah, it's amazing.
Let me show you. All right, so here's
how it's actually working. So I actually
had So I had cursor tell me exactly
what's happening. So first tier one, it
uses FX Twitter's API, which is
apparently free. I don't really
understand how it's free, but it is. You
can only grab individual tweets with it.
Single tweet lookup only. There's no
search. You can't say show me trending
topics or show me topics or show me
posts related to this other topic. You
can't do that. So fine. Then it goes to
the lowcost tier 2 which is the
twitterapi.io.
And I'm still playing around with this.
I'm kind of new to this service, but
it's a relatively inexpensive way to
query against Twitter. So it's 15 cents
per thousand tweets. It does search
profiles, user tweets, thread context,
and I did grab an API for that. Then I
use the expensive tier three as another
fallback. The official X API v2. This is
very expensive. So it's 0.005 cents per
tweet, pay per use, but you get
basically everything. Then as a
fallback, we use the XAI API with the
XARCH tool. Sometimes we use Grock to
search against Twitter. All of these
things come together and just give me an
optimized, cheapest, best, fastest
result. And so here's what that workflow
looks like. All right. Next, obviously,
I use it to track my YouTube analytics
and some of my competitors or the
channels that I keep a close eye on. So,
it hits the API daily, pulls down all of
my stats for all of my videos, the
channels growth, everything. It persists
it and takes a snapshot and records it
in a database locally. Then it does some
computations on it and again stores all
that locally. We scan our competitors,
their uploads, their cadence to see what
they're doing. We get PNG charts, and
then it feeds all of those insights into
the metaanalysis workflow, which I'll
get to in a moment. And so, we can kind
of get a sense of which types of videos
are you guys liking, what titles are
working, what thumbnails are working,
which are not, and it gives me
recommendations all the time. All right,
this next one is absolutely insane. And
I actually got this idea from Brian
Armstrong, the CEO of Coinbase. He said
they're using AI in a really novel way
where they basically plug in all of
their data and have AI review all of it
and look for gaps in their understanding
of their business, ways to improve. And
I thought, hey, we can do that. We're
already collecting all this data. Let's
do it. So, check this out. I basically
ingest all the data from my business. I
put together a council of AI experts, AI
agents that all work together,
collaborate, and then put together a
daily report for me on things that I'm
missing from the business, ways to
improve the business, and more. So,
first, here are all the signals. We have
YouTube metrics, CRM health, crown
reliability, social growth, Slack, all
the Slack messages, emails, ASA X,
Fathom meetings, that's one of the
coolest ones. So, Fathom, which is an AI
notetaker, joins all my meetings,
records them, transcribes them, and then
I ingest that. So, I have a record of
all the meetings that I have, and then
also my HubSpot pipeline. Then, we
compact it all down into the top 200
signals by confidence. And I have first
a draft created. So, it looks at all of
these different signals and is prompted
with something like, "What can we do to
improve the business?" It's obviously a
much more sophisticated prompt than
that, but that's the gist. Then phase
two, I have a growth strategist, a
revenue guardian, a skeptical operator,
and a team dynamics architect all review
it, collaborate, go back and forth with
each other. They come to a consensus. I
have a council moderator again, Opus
4.6. Sorry, you can't see that. They
reconcile disagreements, put it all
together, and finally rank everything
and give me that report. That runs once
a day in the middle of the night. So,
it's when I'm not using a lot of usage
from Opus anyways. And then it provides
me with that report and it gives me
great actionable insights about the
business. It is super super cool. All
right. Next, I plugged in HubSpot into
my OpenClaw. And I'm not actually using
this all that much, but I do allow
OpenClaw to reference my deal pipeline.
And of course, that's for sponsorships
for this channel. And so, it does have
access to it. I'm not doing a lot of
natural language queries like what this
is making possible, but here it is
nonetheless. So, a natural language
request comes in, it classifies the
intent, maps to the endpoint, looks it
all up, and then returns a summary. So,
I can say what deals are in my
qualification stage. So, kind of useful,
but again, the more useful thing is just
it having access to all of the deals
that I'm currently working on. All
right, next. And this is something that
I use across the board both in my direct
messages with my open claw as well as
any content it writes. It basically uses
the humanizer skill against everything.
And so that removes the AI smell from
writing. And it's very easy. It is a
skill. It's on Clawhub. You can download
it, install it, and it's constantly
being updated with AI smells. So I have
a draft input. It detects the AI writing
patterns, marks the problem at expands,
rewrites it, and then publishes it when
it's ready. But it also just looks at
everything. It's not just reactive, it's
also proactive. So that's a cool one as
well. All right. Next is image
generation and also video generation. I
plugged in Nano Banana and VO as APIs to
OpenClaw and now it has the ability to
create images and video anytime I want
for any use case. So here's that
workflow. I send it an existing image
with edit instructions or I tell it what
I want. All of this happens through
Telegram. I have a separate Telegram
topic for images and another one for
video. So then it interprets it,
generates the images, and goes back and
forth until it's finally ready. Let me
actually show you what that looks like.
So here I just say create an icon to use
in Telegram to represent yourself. Boom.
And I said make another but make it
bigger and easier to see. And it did
that but it's kind of horizontal format.
I said make it square and boom, there it
is. Very easy. Now I have basically
infinite image gen capability now. So
I'll just say make another. And I
explicitly told it rather than saving
the image locally, which would be on my
open claw MacBook Air, I said just send
it to me in Telegram. And here we go.
Here's another one. A wonky looking one,
but nonetheless, it knew what I was
talking about. Then we also have video
generation. So I said make a video. And
here's that. And so now I can just
generate any video I need. It's so
crazy. And again, these are skills that
can be used in any other automation
throughout OpenCloud. That's the key.
Make them modular. Make them reusable.
And you just have to tell it. Just tell
it to do that. All right. Here's another
one. I also have it managing my to-do
list. And this one is crazy. I just set
this up. All right. So, there's a few
ways that this can get started. One, I
have a meeting. I mentioned earlier
whenever I have a video conference, I
have my Fathom notetaker join it and
transcribe all the notes. Then rather
than using Fathom's builtin takeaway
generator, which was wonky and didn't
really work all that well, I take the
transcript and send it to Gemini 2.5
Flashlight and say, "Look through
everything. Tell me what are key
takeaways for me to do and takeaways
that my attendee needs to do." And
record them both. Again, all of this
gets stored locally. I can also just
simply say, okay, add a task to follow
up with X person by Friday. And all of
it gets extracted, actions, owner,
deadlines, cross referenced with the
CRM, who is the person, what is the
company, it looks at all of the context
it has about them, shows me the task
list, if I approve it, it puts it into
my to-doist, which is the to-do list app
that I use, and it just manages it. It's
so clean. And I just think the coolest
part of this is that it actually will
read the transcript of my meetings and
automatically suggest to-dos. All right.
Next, I started to notice because I'm
doing so much with it. Occasionally, I
would get charged a lot of money for an
API call or for a model, and I wanted to
keep an eye on it. I wanted to make sure
that there weren't any unexpected
charges. And, you know, I'm still paying
per month. I pay a hundred bucks per
month for the Cloud subscription. I pay
for the Gemini API calls, the X API
calls. It's not cheap. I'm probably
paying about $150 per month in total for
all of this, which relative to the value
I'm getting from it is very cheap in my
opinion. So, I have something now that
tracks all of my spend and my usage. So,
this is something that runs silently in
the background. Every single AI call,
every single API call gets logged to a
single place. And I can ask how much I
spent this week, which workflows are
costing a lot of money, show me the
30-day trend. Again, records all of it
to a usage log. It queries a log, gives
me cost breakdowns, gives me usage
breakdowns, which is just very useful
for figuring out what I'm doing, maybe
things that are automated and running
that I didn't even know about. Just
keeping an eye on everything. This is
very useful. All right, let me show you
all of the services I'm using now. So, I
use Telegram, obviously. I use Slack. So
that's just another surface in which I
can communicate with OpenClaw, Google
Workspace via Gogg, and that's for
calendar and email ingestion, drive
backup. So I'm going to get to how I
save all of this because if I lost my
OpenClaw, I would be very upset if I
could not easily replicate it. I use
Asauna, To-Doist, HubSpot, my YouTube
APIs. I use X and Twitter search,
Fathom, GitHub, Google Drive. Again,
that should be included here. I don't
know why it listed it twice. I use brave
search for searching the web and
firecrawl as well for searching the web.
All right. So next, let me just show you
the holistic view of all the automations
I'm doing. So every hour I sync the code
repo. I check for changes and that might
be changes OpenClaw makes to itself.
It's self-evolving. That might be
changes that I'm making in cursor to it.
Anything. It checks for changes and just
backs it up to GitHub. So critical. But
I don't back up databases to GitHub
because that's kind of a waste of space.
So instead, I have a Google Drive and I
save all of the databases there. I also
have a very detailed document about how
to restore everything in the case that I
lose everything. We also check the CRM
for changes. We scout for new signals
from anywhere basically. Then every
single day we're ingesting emails. We're
collecting YouTube analytics. We're
performing health checks against the
whole platform. and I'll explain that in
a moment. And then we're doing the
nightly business briefing. Every week we
synthesize daily notes into long-term
memory. That's something built into
OpenClaw. It's doing some housekeeping.
And all of this follows the same
pattern. It's using cron executes tasks,
notifies me in Telegram. So I have this
telegram channel about all the cron jobs
that run. I get a notice about them,
what happened, if they failed, if they
succeeded, and all of that again just
gets piped to Telegram. All right. Now,
let me talk a little bit more about the
backup because again, I don't ever want
to lose this. I've spent a lot of time
setting it up. And so, it is all tracked
in Git. I push all the code including
the markdown files to Git. It is
constantly keeping track of it. And so,
if I ever get to a bad state, I can roll
it back, although that hasn't happened.
And then, as I mentioned, all of my
data, the databases, all get backed up
to Google Drive all the time so that I'm
not losing anything. So, here it is. the
CRM data, the analytics, the knowledge
base, business analysis, cron logs,
everything gets backed up, timestamped,
boom, Google Drive, and if something
goes wrong, here's how we bring it back.
And the code gets put into GitHub again,
always up to date. And I have all of
this running on a schedule, so it
updates about every hour. Okay, next
memory. How do we remember all of this
stuff? Well, I'm using kind of just the
default memory built in. I'm not even
using QMD and I probably should, but I
haven't figured out exactly how I want
to use it. So, it's all pretty standard.
Here's what happens. During the day,
conversations with me, task completed,
mistakes made all get piped into daily
notes. Those have a weekly synthesis. It
distills the patterns and preferences,
and gets stored into long-term memory.
Then, we also have the learnings folder,
corrective patterns, mistakes not to
make again, and it just gets better over
time without needing to manually do
that. All right. Now, let me show you
how I'm actually building OpenClaw. I
know a lot of you are probably just
chatting with OpenClaw directly and
having it build stuff for you, which is
fine, but I have actually found that I
prefer developing in cursor. And so,
there's a couple reasons for that. One,
I just find the interface much easier to
use. I can see the files being created.
It is just built for development versus
Telegram, which is just a single chat
window. It's just much more difficult.
Again, you can do it, but I prefer using
cursor. And so, what I do is on my Mac
Studio or again, wherever I am, I have a
few different laptops. I have basically
installed cursor and I SSH into the
MacBook Air, which openclaw is on. So, I
have cursor SSH, I have direct SSH, and
then I use team viewer. I install team
viewer if I need to really just take
control of the computer completely. The
MacBook Air is always on, never leaves
home, and I have multiple different gits
that I'm using. One for major projects
like the CRM and then one for OpenClaw
as a whole. I'm making new edits.
Everything I'm doing is mostly happening
in cursor and then I just verify it in
Telegram. I write tests for everything
and I am committing and pushing to
GitHub frequently. All right, the last
thing I want to show you is how I keep
all of the markdown files up to date
because there can be a lot of drift.
There are multiple different markdown
files. They all do something specific
and as you're adding skills, as you're
telling OpenClaw what to do, sometimes
it puts it in multiple places. Also,
everything that's in the markdown files
is really dependent on the model you're
using. Opus 4.6, for example, really
listens to every single word that it is
prompted with. You don't need to use
bold. You don't need to use all caps.
You don't need to say things like don't
ever forget this or make sure you do
this critical. You don't need to do any
of that. But that's very different from
how Opus 4.5 was. So here's what I do.
First, I created this workspace.md file
which specifies how everything works
based on how I've implemented it. So
here the table of contents and yes, it
is a very large file, but it is a
reference to be used in other places. So
I have what the workspace architecture
looks like. I have key patterns SQLite
for all persistent storage vector
telegram I have the platform
configuration I have model providers
that I'm using fallback chain plugins
etc etc but then we have each of these
files the agent file the heartbeat the
identity the memory all of these files
do something different and so how I made
sure that they stayed upto-date and
accurate is I had openclaw go to the
openclaw website download the best
practices and store them locally and I
have it always reference it and once a
day I have it look through all of the
markdown files and cross reference
against the best practices and say is
there anything you need to change
recommend things to me. Then finally for
Opus 4.6 best practices I had it go and
download the prompting guide for Opus
4.6 six and I also store that locally
and I also have it cross reference that
and I say look for anything that goes
against the prompting best practices
provided by anthropic and so it does
this once per day and it's constantly
updating itself cleaning itself very
useful and so that's it for today I know
that was a lot hopefully that shows you
not only what's possible but how to get
the most out of openclaw if you enjoyed
this video please consider giving a like
and subscribe and I'll see you in the
Next one.