OpenClaw Use Cases that are actually helpful...

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.

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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 Drve. 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 Drve 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 Drve 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 Drve, 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.