# OpenClaw Use Cases that are actually helpful...

| Field | Value |
| --- | --- |
| **Video ID** | `Q7r--i9lLck` |
| **URL** | <https://www.youtube.com/watch?v=Q7r--i9lLck> |
| **Channel** | Matthew Berman |
| **Date Captured** | 2026-02-13 |
| **Views** | 84,614 |
| **Published** | 2026-02-11 |

---

## Transcript

**[0:02]** using OpenClaw. I have spent countless

**[0:05]** hours over the last few weeks exploring

**[0:07]** every nook and cranny of what is

**[0:10]** possible with OpenClaw. I truly believe

**[0:13]** I am one of the most advanced users of

**[0:16]** OpenClaw on the planet. And I'm going to

**[0:19]** show you everything that I've learned.

**[0:21]** And it all starts right here. This is a

**[0:24]** fresh MacBook Air. I wiped it clean and

**[0:27]** I installed OpenClaw on it. This thing

**[0:30]** lives right on my desk. I have it in

**[0:33]** clamshell mode. So when I close it, it

**[0:35]** does not shut off. It is running 24

**[0:37]** hours a day, connected to the internet.

**[0:39]** And I make it easily available anywhere.

**[0:41]** I installed team viewer on it so that if

**[0:44]** I'm remote and something happens and I

**[0:46]** need to change something directly on the

**[0:48]** computer, I just team viewer into it and

**[0:49]** I can do that. I also set up tail scale

**[0:52]** so I can easily SSH into it. So if I

**[0:55]** want to code something with cursor on

**[0:57]** it, I can SSH in from any other computer

**[1:00]** and that one just sits on my desk. Here

**[1:03]** is the high-level overview of everything

**[1:06]** going on in my system. And I know it

**[1:08]** looks complicated. I'm going to get into

**[1:10]** detail about all of it. All right, so

**[1:12]** here is the overview. First, we have the

**[1:14]** interfaces. This is how I'm actually

**[1:16]** talking to my openclaw. I use Telegram

**[1:19]** as the primary one. And if you watched

**[1:21]** my previous video, you know I'm using

**[1:23]** Telegram groups. I have a bunch of

**[1:25]** different topics. I keep all of the

**[1:27]** topics very narrow, very niche, and I

**[1:29]** actually don't have it start new

**[1:31]** sessions anymore. Previously, a default

**[1:34]** setting in OpenClaw is to start a new

**[1:37]** session every day at like 4:00 a.m. That

**[1:39]** means it basically forgot everything in

**[1:41]** the chat prior, which would make sense

**[1:43]** if I had a single DM that spanned

**[1:46]** forever, but instead I have all of these

**[1:49]** individual channels, so I didn't want to

**[1:51]** do that anymore. And the solution, I

**[1:53]** simply had it set the expiration of a

**[1:55]** session to be one year. And so look at

**[1:57]** all these different sessions. I have my

**[1:59]** knowledge base, my food journal, cron

**[2:01]** updates, video research,

**[2:02]** self-improvement, business analysis,

**[2:05]** meeting prep, all of these different

**[2:07]** ones, and it allows me to stay on topic.

**[2:09]** All right, so now back here, second, I

**[2:11]** have Slack, but I have it very narrowly

**[2:14]** implemented in Slack. It's only

**[2:16]** available in two channels, and it's only

**[2:18]** available to me. I am the only one who's

**[2:21]** able to invoke my open claw. If somebody

**[2:24]** else tries to invoke it, it just ignores

**[2:26]** it. Then of course we have our command

**[2:28]** line interface. I sometimes SSH in and

**[2:31]** we also have scripts. I'm using multiple

**[2:33]** models of course. So we have Anthropic,

**[2:36]** Opus, Sonnet and Haiku. I'm using Google

**[2:38]** Gemini for a bunch of stuff. We're using

**[2:40]** XAI Gro and XARCH. And of course we're

**[2:43]** using Open AI. And it already has after

**[2:47]** just two plus weeks of using it a ton of

**[2:49]** different skills. I use it as a personal

**[2:51]** CRM. I use it as a knowledge base, a

**[2:54]** video idea pipeline, X and Twitter

**[2:57]** research, business meta analysis. That's

**[3:00]** a crazy one. Wait till I tell you about

**[3:02]** that. HubSpot Ops. I always apply the

**[3:05]** humanizer skill. I want all the text

**[3:07]** coming out of it to be humanlike, not AI

**[3:09]** like. No M dashes. I plugged it into my

**[3:12]** to-do list and I kind of use it as a

**[3:14]** task management system as well. I track

**[3:17]** all of the usage across everything I do.

**[3:20]** And of course, I use it for YouTube

**[3:21]** analytics. And I'm also storing a ton of

**[3:24]** data. I actually try to store all the

**[3:25]** data I possibly can. And I usually do

**[3:28]** that in a traditional database mixed

**[3:31]** with a vector column. So I want to be

**[3:34]** able to do traditional SQL searches, but

**[3:36]** I also want to be able to do natural

**[3:38]** language searches using the vector

**[3:40]** column. And so I created a standardized

**[3:43]** way of creating this hybrid database and

**[3:45]** I use it across a bunch of different

**[3:47]** skills. So I have my contacts in my CRM.

**[3:50]** Again, I'm going to go over all of these

**[3:52]** use cases. So I have my knowledge base,

**[3:54]** I have video pitches, my business meta

**[3:56]** analysis, views of my social channels,

**[3:59]** the cron log, everything is stored. Then

**[4:02]** I've plugged in a bunch of different

**[4:03]** external services. We have Google

**[4:04]** Workspace via GG. I have a sauna. I have

**[4:08]** HubSpot. I have to-d doist, Fathom,

**[4:10]** Brave, and GitHub. I have X. I have

**[4:13]** YouTube's API. So much is happening

**[4:16]** right now. And by the way, a quick shout

**[4:18]** out to the sponsor of this video,

**[4:19]** Greile. I use Grapile to review all the

**[4:22]** code that I'm putting out for OpenClaw,

**[4:26]** and it's a lot. And it catches bugs that

**[4:28]** I wouldn't have seen, that even Opus or

**[4:31]** GPT 5.3 Codeex wouldn't have seen. And

**[4:33]** it's just a great system to really shore

**[4:36]** up all of the rough edges of my OpenClaw

**[4:38]** instance. These guys at Grappile spend

**[4:41]** all of their time thinking about code

**[4:43]** review and it helps me and my team save

**[4:45]** a bunch of time. It's used by some of

**[4:47]** the most popular repos in the world

**[4:50]** including OpenClaw, Nvidia, PyTorch,

**[4:53]** Postto, Storybook, and many more. Don't

**[4:55]** waste your team's time on code review,

**[4:57]** but code review is still critically

**[4:59]** important. Let Greile do it. Try Reptile

**[5:02]** for free for 14 days. I'll drop a link

**[5:04]** down below. They've been a fantastic

**[5:06]** partner. So, help us help yourself. Go

**[5:09]** check them out. Click through the link

**[5:11]** and get your 14 days for free. All

**[5:12]** right. So, the first workflow I want to

**[5:15]** talk about is my personal CRM. It is

**[5:18]** incredible because it allows me to plug

**[5:20]** in all of this builtup knowledge into

**[5:22]** everything else I'm doing. Let me just

**[5:24]** explain. So, I basically have my

**[5:25]** OpenCloud download all of my emails and

**[5:28]** start to parse through them, understand

**[5:30]** who's the email from, filter out

**[5:32]** contacts that I don't want saved, like

**[5:34]** newsletters and cold outreach, cold

**[5:37]** pitches, and then it finds the highest

**[5:39]** quality contacts, and starts saving it

**[5:41]** to my CRM. It reads all of the

**[5:44]** downloaded emails, builds out a graph,

**[5:46]** builds out an understanding of all the

**[5:49]** conversations I've had with each

**[5:51]** contact, and starts to save that in the

**[5:53]** database. and I have it do that every

**[5:55]** single day. So, it's always up todate.

**[5:57]** It always knows who I'm talking to, what

**[5:59]** I'm talking about, and it's just super

**[6:02]** helpful because I can always ask

**[6:03]** questions like, "Who is the last person

**[6:05]** I talked to at Grapile and what did we

**[6:07]** talk about?" Or, "Who else do I know at,

**[6:09]** you know, this other company?" And so,

**[6:11]** this is how it works. I have the daily

**[6:13]** ingestion trigger, which is a cron job.

**[6:16]** It downloads Gmail and my calendar, by

**[6:18]** the way, not just Gmail, also my

**[6:20]** calendar. It extracts people from the

**[6:22]** senders and participants. It dduplicates

**[6:25]** everything, merges contact records. It

**[6:27]** uses AI to classify the role in context

**[6:30]** and I use a very inexpensive very fast

**[6:33]** Gemini 2.5 flash model I believe to do

**[6:36]** that classification. Then we start to

**[6:38]** update the timeline in Last Touch. It

**[6:40]** does semantic indexing and it sends me

**[6:42]** updates via Telegram when I want it and

**[6:44]** I can also just query against it as I

**[6:46]** said earlier. Now, where this is also

**[6:48]** very useful is in my meeting prep

**[6:50]** workflow. Every single day, first thing

**[6:52]** in the morning, it looks at my calendar

**[6:54]** for the day. It filters out events that

**[6:57]** don't have anybody or only have my

**[6:58]** internal team. And it basically gives me

**[7:00]** a meeting prep for the day. It says,

**[7:02]** "Hey, you're meeting with this person.

**[7:03]** Here's the last thing you talked about.

**[7:05]** Here's what they want to talk about in

**[7:06]** this meeting. Here's who they are." And

**[7:08]** it's just super helpful to keep me up to

**[7:11]** date going into these meetings. All

**[7:12]** right. Next is my knowledge base. I am

**[7:15]** constantly on X and searching the web

**[7:18]** and reading articles all about

**[7:20]** artificial intelligence. And I wanted a

**[7:22]** single place where I can just throw

**[7:24]** everything interesting that I find into

**[7:26]** a single repository and I'm able to use

**[7:29]** natural language to search against it.

**[7:32]** And again, all of the things that I'm

**[7:34]** building, I want to be able to use

**[7:35]** across different workflows. That's

**[7:37]** really important. And so when I get to

**[7:40]** the video ideiation workflow later,

**[7:42]** you're going to see it actually

**[7:44]** references different articles that are

**[7:46]** that have been saved to this knowledge

**[7:48]** base. And so the way this works is I

**[7:51]** take a file or URL, I drop it in

**[7:53]** Telegram, it detects the source type,

**[7:55]** extracts all the information from it,

**[7:58]** normalizes it, chunks it, puts it in the

**[8:00]** vector database, and then stores it. So

**[8:03]** then when I have a question like, find

**[8:04]** me all the articles about the Opus 4.6 6

**[8:07]** model. And so I put that user question

**[8:10]** in, it embeds it, it grabs all of the

**[8:13]** candidate articles, and then it answers

**[8:16]** with sources. So now I'm building up

**[8:18]** this infinite knowledge base of

**[8:21]** everything that has interested me and it

**[8:23]** just stores it and I can always

**[8:25]** reference it. If I'm creating a video

**[8:27]** and I want to reference a previous

**[8:28]** article, it is now so easy to find.

**[8:30]** Okay, next. And this is one of my

**[8:32]** favorite ones. This is the video idea

**[8:35]** pipeline. This was something that I

**[8:37]** spent a ton of time on. The way I was

**[8:39]** doing it previously is I would drop

**[8:42]** links, again, something that I'm now

**[8:44]** doing via the knowledge base into Slack

**[8:46]** to share with my team. We would talk

**[8:48]** about it and the ones that interested us

**[8:50]** the most, we would decide to make a

**[8:51]** video about. Then I would create an ASA

**[8:54]** card. I would do research, find all

**[8:56]** relevant articles, find relevant

**[8:58]** exposts, and put it all together in that

**[9:00]** ASA card. And it would just, you know,

**[9:03]** take a lot of time. But now I don't have

**[9:06]** to do any of that. So first remember the

**[9:08]** previous knowledge base? Well, part of

**[9:10]** that workflow is when I drop an article

**[9:13]** in Telegram and it gets all that

**[9:15]** information, it posts to the Slack

**[9:17]** channel for me and it says, "Hey, this

**[9:19]** is what Matt's looking at." And we can

**[9:21]** have that discussion there. Also, if

**[9:23]** somebody from my team drops a link in

**[9:25]** Slack, I can actually tag my open claw

**[9:28]** and say, "Hey, let's make a video idea

**[9:30]** about this." And so both of these ways

**[9:32]** work. Let me show you what happens after

**[9:34]** that. So we have this idea trigger. It

**[9:36]** either comes from Slack or Telegram. We

**[9:39]** parse the video topic or intent. Then it

**[9:42]** does research on X and it also does

**[9:45]** research on the web. I don't know why it

**[9:46]** doesn't say that here, but it does. Then

**[9:48]** we query the knowledgebased context. So

**[9:51]** it looks for potential articles that

**[9:53]** might be relevant. It comes up with

**[9:55]** video pitches and make sure it hasn't

**[9:56]** pitched us something like that already.

**[9:58]** Then it starts to build hooks and an

**[10:00]** outline for the video. It links all

**[10:02]** sources, creates that ASA task, and then

**[10:05]** sends confirmation to wherever I invoked

**[10:07]** it. All of this happens in like 30

**[10:10]** seconds. Now, super super valuable,

**[10:13]** right? So, here's an example. In

**[10:15]** Telegram, I dropped this link. So,

**[10:17]** Thomas Donkey, former CEO of GitHub,

**[10:19]** started a new company. Sounds

**[10:20]** interesting. I dropped it in here. It

**[10:23]** saved it into my knowledge base. Then it

**[10:25]** also pushed a quick summary and link to

**[10:29]** the Slack channel where we talk about

**[10:30]** all of this stuff. Then if we wanted to

**[10:33]** create a video about it, this is about a

**[10:34]** different topic, but I said, "Whoa,

**[10:36]** Claude, short video idea." Then Claude

**[10:39]** jumps in, does a bunch of research, adds

**[10:42]** the card, and everything. It's just so

**[10:44]** easy. Now, now you're seeing how all of

**[10:46]** these different pieces work together to

**[10:48]** create this incredibly autonomous system

**[10:50]** that saves me a bunch of time. And by

**[10:53]** the way, if you want to see 25 more use

**[10:55]** cases, my team put together a completely

**[10:57]** free ebook all about open claw use

**[11:01]** cases, specifically how to implement

**[11:03]** them, how to get them going, why they're

**[11:04]** valuable, and so much more. Go download

**[11:06]** the ebook. It's completely free. I'll

**[11:08]** drop a link in the description below.

**[11:10]** Okay. Next, I built out an entire

**[11:12]** workflow just for searching on Twitter

**[11:14]** because I do so many searches on

**[11:16]** Twitter, whether it's getting the data

**[11:18]** from specific posts, and there are so

**[11:20]** many different ways to do it. I actually

**[11:22]** built an entire fallback daisy chain

**[11:24]** system that handles it for me and it's

**[11:27]** costoptimized and yeah, it's amazing.

**[11:30]** Let me show you. All right, so here's

**[11:31]** how it's actually working. So I actually

**[11:33]** had So I had cursor tell me exactly

**[11:36]** what's happening. So first tier one, it

**[11:38]** uses FX Twitter's API, which is

**[11:41]** apparently free. I don't really

**[11:42]** understand how it's free, but it is. You

**[11:44]** can only grab individual tweets with it.

**[11:46]** Single tweet lookup only. There's no

**[11:48]** search. You can't say show me trending

**[11:50]** topics or show me topics or show me

**[11:52]** posts related to this other topic. You

**[11:54]** can't do that. So fine. Then it goes to

**[11:57]** the lowcost tier 2 which is the

**[12:00]** the lowcost tier 2 which is the twitterapi.io.

**[12:02]** And I'm still playing around with this.

**[12:04]** I'm kind of new to this service, but

**[12:05]** it's a relatively inexpensive way to

**[12:07]** query against Twitter. So it's 15 cents

**[12:10]** per thousand tweets. It does search

**[12:12]** profiles, user tweets, thread context,

**[12:15]** and I did grab an API for that. Then I

**[12:18]** use the expensive tier three as another

**[12:20]** fallback. The official X API v2. This is

**[12:23]** very expensive. So it's 0.005 cents per

**[12:28]** tweet, pay per use, but you get

**[12:30]** basically everything. Then as a

**[12:32]** fallback, we use the XAI API with the

**[12:35]** XARCH tool. Sometimes we use Grock to

**[12:38]** search against Twitter. All of these

**[12:41]** things come together and just give me an

**[12:43]** optimized, cheapest, best, fastest

**[12:45]** result. And so here's what that workflow

**[12:47]** looks like. All right. Next, obviously,

**[12:50]** I use it to track my YouTube analytics

**[12:53]** and some of my competitors or the

**[12:55]** channels that I keep a close eye on. So,

**[12:57]** it hits the API daily, pulls down all of

**[12:59]** my stats for all of my videos, the

**[13:02]** channels growth, everything. It persists

**[13:04]** it and takes a snapshot and records it

**[13:06]** in a database locally. Then it does some

**[13:08]** computations on it and again stores all

**[13:11]** that locally. We scan our competitors,

**[13:13]** their uploads, their cadence to see what

**[13:15]** they're doing. We get PNG charts, and

**[13:18]** then it feeds all of those insights into

**[13:20]** the metaanalysis workflow, which I'll

**[13:23]** get to in a moment. And so, we can kind

**[13:24]** of get a sense of which types of videos

**[13:26]** are you guys liking, what titles are

**[13:29]** working, what thumbnails are working,

**[13:30]** which are not, and it gives me

**[13:32]** recommendations all the time. All right,

**[13:34]** this next one is absolutely insane. And

**[13:37]** I actually got this idea from Brian

**[13:38]** Armstrong, the CEO of Coinbase. He said

**[13:40]** they're using AI in a really novel way

**[13:43]** where they basically plug in all of

**[13:44]** their data and have AI review all of it

**[13:48]** and look for gaps in their understanding

**[13:50]** of their business, ways to improve. And

**[13:51]** I thought, hey, we can do that. We're

**[13:53]** already collecting all this data. Let's

**[13:55]** do it. So, check this out. I basically

**[13:57]** ingest all the data from my business. I

**[14:00]** put together a council of AI experts, AI

**[14:04]** agents that all work together,

**[14:06]** collaborate, and then put together a

**[14:08]** daily report for me on things that I'm

**[14:10]** missing from the business, ways to

**[14:12]** improve the business, and more. So,

**[14:14]** first, here are all the signals. We have

**[14:16]** YouTube metrics, CRM health, crown

**[14:18]** reliability, social growth, Slack, all

**[14:20]** the Slack messages, emails, ASA X,

**[14:25]** Fathom meetings, that's one of the

**[14:26]** coolest ones. So, Fathom, which is an AI

**[14:28]** notetaker, joins all my meetings,

**[14:30]** records them, transcribes them, and then

**[14:33]** I ingest that. So, I have a record of

**[14:35]** all the meetings that I have, and then

**[14:37]** also my HubSpot pipeline. Then, we

**[14:39]** compact it all down into the top 200

**[14:42]** signals by confidence. And I have first

**[14:45]** a draft created. So, it looks at all of

**[14:48]** these different signals and is prompted

**[14:49]** with something like, "What can we do to

**[14:52]** improve the business?" It's obviously a

**[14:53]** much more sophisticated prompt than

**[14:55]** that, but that's the gist. Then phase

**[14:57]** two, I have a growth strategist, a

**[14:59]** revenue guardian, a skeptical operator,

**[15:01]** and a team dynamics architect all review

**[15:05]** it, collaborate, go back and forth with

**[15:07]** each other. They come to a consensus. I

**[15:10]** have a council moderator again, Opus

**[15:12]** 4.6. Sorry, you can't see that. They

**[15:15]** reconcile disagreements, put it all

**[15:17]** together, and finally rank everything

**[15:19]** and give me that report. That runs once

**[15:21]** a day in the middle of the night. So,

**[15:22]** it's when I'm not using a lot of usage

**[15:25]** from Opus anyways. And then it provides

**[15:27]** me with that report and it gives me

**[15:30]** great actionable insights about the

**[15:32]** business. It is super super cool. All

**[15:35]** right. Next, I plugged in HubSpot into

**[15:36]** my OpenClaw. And I'm not actually using

**[15:39]** this all that much, but I do allow

**[15:42]** OpenClaw to reference my deal pipeline.

**[15:45]** And of course, that's for sponsorships

**[15:46]** for this channel. And so, it does have

**[15:49]** access to it. I'm not doing a lot of

**[15:50]** natural language queries like what this

**[15:53]** is making possible, but here it is

**[15:55]** nonetheless. So, a natural language

**[15:56]** request comes in, it classifies the

**[15:58]** intent, maps to the endpoint, looks it

**[16:01]** all up, and then returns a summary. So,

**[16:04]** I can say what deals are in my

**[16:06]** qualification stage. So, kind of useful,

**[16:09]** but again, the more useful thing is just

**[16:10]** it having access to all of the deals

**[16:13]** that I'm currently working on. All

**[16:14]** right, next. And this is something that

**[16:16]** I use across the board both in my direct

**[16:18]** messages with my open claw as well as

**[16:21]** any content it writes. It basically uses

**[16:23]** the humanizer skill against everything.

**[16:26]** And so that removes the AI smell from

**[16:29]** writing. And it's very easy. It is a

**[16:32]** skill. It's on Clawhub. You can download

**[16:34]** it, install it, and it's constantly

**[16:36]** being updated with AI smells. So I have

**[16:40]** a draft input. It detects the AI writing

**[16:42]** patterns, marks the problem at expands,

**[16:44]** rewrites it, and then publishes it when

**[16:47]** it's ready. But it also just looks at

**[16:48]** everything. It's not just reactive, it's

**[16:50]** also proactive. So that's a cool one as

**[16:53]** well. All right. Next is image

**[16:54]** generation and also video generation. I

**[16:57]** plugged in Nano Banana and VO as APIs to

**[17:02]** OpenClaw and now it has the ability to

**[17:04]** create images and video anytime I want

**[17:06]** for any use case. So here's that

**[17:08]** workflow. I send it an existing image

**[17:10]** with edit instructions or I tell it what

**[17:12]** I want. All of this happens through

**[17:14]** Telegram. I have a separate Telegram

**[17:16]** topic for images and another one for

**[17:19]** video. So then it interprets it,

**[17:22]** generates the images, and goes back and

**[17:24]** forth until it's finally ready. Let me

**[17:26]** actually show you what that looks like.

**[17:27]** So here I just say create an icon to use

**[17:30]** in Telegram to represent yourself. Boom.

**[17:32]** And I said make another but make it

**[17:34]** bigger and easier to see. And it did

**[17:36]** that but it's kind of horizontal format.

**[17:38]** I said make it square and boom, there it

**[17:40]** is. Very easy. Now I have basically

**[17:43]** infinite image gen capability now. So

**[17:46]** I'll just say make another. And I

**[17:48]** explicitly told it rather than saving

**[17:51]** the image locally, which would be on my

**[17:53]** open claw MacBook Air, I said just send

**[17:56]** it to me in Telegram. And here we go.

**[17:58]** Here's another one. A wonky looking one,

**[18:00]** but nonetheless, it knew what I was

**[18:02]** talking about. Then we also have video

**[18:04]** generation. So I said make a video. And

**[18:07]** here's that. And so now I can just

**[18:09]** generate any video I need. It's so

**[18:11]** crazy. And again, these are skills that

**[18:14]** can be used in any other automation

**[18:15]** throughout OpenCloud. That's the key.

**[18:17]** Make them modular. Make them reusable.

**[18:21]** And you just have to tell it. Just tell

**[18:22]** it to do that. All right. Here's another

**[18:24]** one. I also have it managing my to-do

**[18:27]** list. And this one is crazy. I just set

**[18:29]** this up. All right. So, there's a few

**[18:30]** ways that this can get started. One, I

**[18:32]** have a meeting. I mentioned earlier

**[18:34]** whenever I have a video conference, I

**[18:36]** have my Fathom notetaker join it and

**[18:39]** transcribe all the notes. Then rather

**[18:41]** than using Fathom's builtin takeaway

**[18:44]** generator, which was wonky and didn't

**[18:47]** really work all that well, I take the

**[18:49]** transcript and send it to Gemini 2.5

**[18:52]** Flashlight and say, "Look through

**[18:54]** everything. Tell me what are key

**[18:56]** takeaways for me to do and takeaways

**[18:59]** that my attendee needs to do." And

**[19:01]** record them both. Again, all of this

**[19:03]** gets stored locally. I can also just

**[19:05]** simply say, okay, add a task to follow

**[19:07]** up with X person by Friday. And all of

**[19:11]** it gets extracted, actions, owner,

**[19:14]** deadlines, cross referenced with the

**[19:16]** CRM, who is the person, what is the

**[19:18]** company, it looks at all of the context

**[19:20]** it has about them, shows me the task

**[19:23]** list, if I approve it, it puts it into

**[19:25]** my to-doist, which is the to-do list app

**[19:28]** that I use, and it just manages it. It's

**[19:31]** so clean. And I just think the coolest

**[19:33]** part of this is that it actually will

**[19:34]** read the transcript of my meetings and

**[19:37]** automatically suggest to-dos. All right.

**[19:39]** Next, I started to notice because I'm

**[19:41]** doing so much with it. Occasionally, I

**[19:43]** would get charged a lot of money for an

**[19:45]** API call or for a model, and I wanted to

**[19:47]** keep an eye on it. I wanted to make sure

**[19:49]** that there weren't any unexpected

**[19:51]** charges. And, you know, I'm still paying

**[19:52]** per month. I pay a hundred bucks per

**[19:54]** month for the Cloud subscription. I pay

**[19:56]** for the Gemini API calls, the X API

**[19:59]** calls. It's not cheap. I'm probably

**[20:01]** paying about $150 per month in total for

**[20:05]** all of this, which relative to the value

**[20:08]** I'm getting from it is very cheap in my

**[20:11]** opinion. So, I have something now that

**[20:13]** tracks all of my spend and my usage. So,

**[20:16]** this is something that runs silently in

**[20:18]** the background. Every single AI call,

**[20:20]** every single API call gets logged to a

**[20:22]** single place. And I can ask how much I

**[20:24]** spent this week, which workflows are

**[20:27]** costing a lot of money, show me the

**[20:28]** 30-day trend. Again, records all of it

**[20:31]** to a usage log. It queries a log, gives

**[20:34]** me cost breakdowns, gives me usage

**[20:36]** breakdowns, which is just very useful

**[20:38]** for figuring out what I'm doing, maybe

**[20:41]** things that are automated and running

**[20:43]** that I didn't even know about. Just

**[20:44]** keeping an eye on everything. This is

**[20:46]** very useful. All right, let me show you

**[20:48]** all of the services I'm using now. So, I

**[20:51]** use Telegram, obviously. I use Slack. So

**[20:54]** that's just another surface in which I

**[20:55]** can communicate with OpenClaw, Google

**[20:57]** Workspace via Gogg, and that's for

**[21:00]** calendar and email ingestion, drive

**[21:02]** backup. So I'm going to get to how I

**[21:04]** save all of this because if I lost my

**[21:06]** OpenClaw, I would be very upset if I

**[21:08]** could not easily replicate it. I use

**[21:11]** Asauna, To-Doist, HubSpot, my YouTube

**[21:13]** APIs. I use X and Twitter search,

**[21:15]** Fathom, GitHub, Google Drive. Again,

**[21:19]** that should be included here. I don't

**[21:20]** know why it listed it twice. I use brave

**[21:22]** search for searching the web and

**[21:24]** firecrawl as well for searching the web.

**[21:26]** All right. So next, let me just show you

**[21:28]** the holistic view of all the automations

**[21:30]** I'm doing. So every hour I sync the code

**[21:34]** repo. I check for changes and that might

**[21:36]** be changes OpenClaw makes to itself.

**[21:38]** It's self-evolving. That might be

**[21:40]** changes that I'm making in cursor to it.

**[21:43]** Anything. It checks for changes and just

**[21:45]** backs it up to GitHub. So critical. But

**[21:48]** I don't back up databases to GitHub

**[21:50]** because that's kind of a waste of space.

**[21:52]** So instead, I have a Google Drive and I

**[21:54]** save all of the databases there. I also

**[21:57]** have a very detailed document about how

**[22:00]** to restore everything in the case that I

**[22:02]** lose everything. We also check the CRM

**[22:04]** for changes. We scout for new signals

**[22:07]** from anywhere basically. Then every

**[22:09]** single day we're ingesting emails. We're

**[22:11]** collecting YouTube analytics. We're

**[22:13]** performing health checks against the

**[22:15]** whole platform. and I'll explain that in

**[22:17]** a moment. And then we're doing the

**[22:18]** nightly business briefing. Every week we

**[22:21]** synthesize daily notes into long-term

**[22:23]** memory. That's something built into

**[22:24]** OpenClaw. It's doing some housekeeping.

**[22:27]** And all of this follows the same

**[22:28]** pattern. It's using cron executes tasks,

**[22:31]** notifies me in Telegram. So I have this

**[22:34]** telegram channel about all the cron jobs

**[22:36]** that run. I get a notice about them,

**[22:38]** what happened, if they failed, if they

**[22:40]** succeeded, and all of that again just

**[22:42]** gets piped to Telegram. All right. Now,

**[22:44]** let me talk a little bit more about the

**[22:46]** backup because again, I don't ever want

**[22:48]** to lose this. I've spent a lot of time

**[22:50]** setting it up. And so, it is all tracked

**[22:52]** in Git. I push all the code including

**[22:55]** the markdown files to Git. It is

**[22:58]** constantly keeping track of it. And so,

**[23:00]** if I ever get to a bad state, I can roll

**[23:02]** it back, although that hasn't happened.

**[23:03]** And then, as I mentioned, all of my

**[23:05]** data, the databases, all get backed up

**[23:07]** to Google Drive all the time so that I'm

**[23:10]** not losing anything. So, here it is. the

**[23:12]** CRM data, the analytics, the knowledge

**[23:14]** base, business analysis, cron logs,

**[23:16]** everything gets backed up, timestamped,

**[23:19]** boom, Google Drive, and if something

**[23:21]** goes wrong, here's how we bring it back.

**[23:23]** And the code gets put into GitHub again,

**[23:26]** always up to date. And I have all of

**[23:28]** this running on a schedule, so it

**[23:30]** updates about every hour. Okay, next

**[23:32]** memory. How do we remember all of this

**[23:35]** stuff? Well, I'm using kind of just the

**[23:37]** default memory built in. I'm not even

**[23:39]** using QMD and I probably should, but I

**[23:42]** haven't figured out exactly how I want

**[23:43]** to use it. So, it's all pretty standard.

**[23:45]** Here's what happens. During the day,

**[23:46]** conversations with me, task completed,

**[23:48]** mistakes made all get piped into daily

**[23:51]** notes. Those have a weekly synthesis. It

**[23:54]** distills the patterns and preferences,

**[23:55]** and gets stored into long-term memory.

**[23:58]** Then, we also have the learnings folder,

**[24:00]** corrective patterns, mistakes not to

**[24:03]** make again, and it just gets better over

**[24:05]** time without needing to manually do

**[24:07]** that. All right. Now, let me show you

**[24:09]** how I'm actually building OpenClaw. I

**[24:12]** know a lot of you are probably just

**[24:14]** chatting with OpenClaw directly and

**[24:16]** having it build stuff for you, which is

**[24:18]** fine, but I have actually found that I

**[24:21]** prefer developing in cursor. And so,

**[24:25]** there's a couple reasons for that. One,

**[24:26]** I just find the interface much easier to

**[24:28]** use. I can see the files being created.

**[24:31]** It is just built for development versus

**[24:33]** Telegram, which is just a single chat

**[24:36]** window. It's just much more difficult.

**[24:37]** Again, you can do it, but I prefer using

**[24:40]** cursor. And so, what I do is on my Mac

**[24:43]** Studio or again, wherever I am, I have a

**[24:45]** few different laptops. I have basically

**[24:47]** installed cursor and I SSH into the

**[24:50]** MacBook Air, which openclaw is on. So, I

**[24:53]** have cursor SSH, I have direct SSH, and

**[24:55]** then I use team viewer. I install team

**[24:57]** viewer if I need to really just take

**[24:59]** control of the computer completely. The

**[25:01]** MacBook Air is always on, never leaves

**[25:04]** home, and I have multiple different gits

**[25:07]** that I'm using. One for major projects

**[25:09]** like the CRM and then one for OpenClaw

**[25:11]** as a whole. I'm making new edits.

**[25:13]** Everything I'm doing is mostly happening

**[25:15]** in cursor and then I just verify it in

**[25:18]** Telegram. I write tests for everything

**[25:20]** and I am committing and pushing to

**[25:22]** GitHub frequently. All right, the last

**[25:24]** thing I want to show you is how I keep

**[25:26]** all of the markdown files up to date

**[25:28]** because there can be a lot of drift.

**[25:30]** There are multiple different markdown

**[25:32]** files. They all do something specific

**[25:35]** and as you're adding skills, as you're

**[25:36]** telling OpenClaw what to do, sometimes

**[25:39]** it puts it in multiple places. Also,

**[25:41]** everything that's in the markdown files

**[25:43]** is really dependent on the model you're

**[25:45]** using. Opus 4.6, for example, really

**[25:48]** listens to every single word that it is

**[25:50]** prompted with. You don't need to use

**[25:52]** bold. You don't need to use all caps.

**[25:54]** You don't need to say things like don't

**[25:55]** ever forget this or make sure you do

**[25:57]** this critical. You don't need to do any

**[25:58]** of that. But that's very different from

**[26:00]** how Opus 4.5 was. So here's what I do.

**[26:03]** First, I created this workspace.md file

**[26:06]** which specifies how everything works

**[26:09]** based on how I've implemented it. So

**[26:11]** here the table of contents and yes, it

**[26:13]** is a very large file, but it is a

**[26:15]** reference to be used in other places. So

**[26:17]** I have what the workspace architecture

**[26:19]** looks like. I have key patterns SQLite

**[26:21]** for all persistent storage vector

**[26:23]** telegram I have the platform

**[26:25]** configuration I have model providers

**[26:27]** that I'm using fallback chain plugins

**[26:31]** etc etc but then we have each of these

**[26:33]** files the agent file the heartbeat the

**[26:35]** identity the memory all of these files

**[26:37]** do something different and so how I made

**[26:40]** sure that they stayed upto-date and

**[26:42]** accurate is I had openclaw go to the

**[26:45]** openclaw website download the best

**[26:47]** practices and store them locally and I

**[26:50]** have it always reference it and once a

**[26:53]** day I have it look through all of the

**[26:54]** markdown files and cross reference

**[26:56]** against the best practices and say is

**[26:58]** there anything you need to change

**[27:00]** recommend things to me. Then finally for

**[27:03]** Opus 4.6 best practices I had it go and

**[27:06]** download the prompting guide for Opus

**[27:09]** 4.6 six and I also store that locally

**[27:12]** and I also have it cross reference that

**[27:15]** and I say look for anything that goes

**[27:17]** against the prompting best practices

**[27:19]** provided by anthropic and so it does

**[27:22]** this once per day and it's constantly

**[27:25]** updating itself cleaning itself very

**[27:28]** useful and so that's it for today I know

**[27:30]** that was a lot hopefully that shows you

**[27:31]** not only what's possible but how to get

**[27:33]** the most out of openclaw if you enjoyed

**[27:35]** this video please consider giving a like

**[27:37]** and subscribe and I'll see you in the

**[27:39]** Next one.