Inside OpenClaw: Should You Actually Use It? Probably Not.

Everyone's talking about Openclaw, but it's difficult to figure out who this new framework is actually for.

On top of security issues and vulnerability risks, it's also technically demanding. So in this episode, we get to the bottom of it: should you actually use Openclaw?

And if so, how should you do it?

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TIMESTAMPS

0:00 The OpenClaw Archetypes
3:02 Serious Operators
6:55 The Knowledge Worker
11:47 Privacy-Conscious Users
14:50 Security Concerns
18:10 How To Set Yours Up
21:17 Future of Agents
22:07 Closing Thoughts

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RESOURCES

My Claw: https://myclaw.ai/

Josh: https://x.com/JoshKale
Ejaaz: https://x.com/cryptopunk7213

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Not financial or tax advice. See our investment disclosures here:
https://www.bankless.com/disclosures⁠

Creators and Guests

Host

Ejaaz Ahamadeen

Host

Josh Kale

What is Limitless: An AI Podcast?

Exploring the frontiers of Technology and AI

Speaker0:
OpenClaw was a weekend project that turned into the fastest growing open source project ever.

Speaker0:
It got acquired by OpenAI in just 80 days for billions of dollars.

Speaker0:
But the number one question we keep asking ourselves is,

Speaker0:
What can this thing actually do? Is this something for me or someone more technical?

Speaker0:
Is this something that is useful for me right now? Or do I just need to wait

Speaker0:
for a bit? So we did all the hard work for you.

Speaker0:
We've been testing OpenClaw ourselves for the last week, watching every single

Speaker0:
demo we could get a hand on.

Speaker0:
And on this episode, we're going to show you exactly what OpenClaw can do,

Speaker0:
from all the manual stuff to all the actual really useful, mind-blowing stuff.

Speaker0:
And we're going to answer one simple question. Is this for you?

Speaker1:
The answers to a lot of these questions might surprise you. and

Speaker1:
the way we're going to outline this is is kind of through three archetypes the

Speaker1:
first being the operator and builder this is kind of

Speaker1:
the person who creates net new value maybe the entrepreneur

Speaker1:
the second is the knowledge worker or the creator this is

Speaker1:
kind of where we fall into as podcasters and then we have the

Speaker1:
privacy focused professionals and within

Speaker1:
that there's a lot of cool use cases and examples so the first one that we want to

Speaker1:
highlight here is this guy nat eliason he's this incredible follow on twitter

Speaker1:
and he released a bot called felixbot and felixbot was an agent that his OpenClaw

Speaker1:
spawned up and FelixBot has done a series of incredible things that I didn't

Speaker1:
think were possible and allowed me to reconsider what OpenClaw is actually capable of.

Speaker1:
So in this instance, he trained FelixBot to make him money.

Speaker1:
And what we're seeing on screen here is the first post of the weekly revenue

Speaker1:
numbers in which FelixCraft, FelixBot, whatever you want to call it,

Speaker1:
it actually generated $41,000 in a week. Now, how did it do this?

Speaker1:
First, it created its own book. So this Felix Craft bot, by interfacing with

Speaker1:
Nat, decided that it was best in order to make money to create a book about

Speaker1:
AI and about OpenClaw and sell that book.

Speaker1:
It then marketed its own book through this Twitter profile that we're looking at right now.

Speaker1:
And it sold 132 copies in that one week that yielded $3,828.

Speaker1:
Now, the second thing it did is it went and it spawned up a token.

Speaker1:
And it earned trading fees on that token from people who are speculating.

Speaker1:
And the trading fees from that token were $37,698.

Speaker1:
And this was all done through the text interface on Telegram

Speaker1:
Going back and forth, chatting with the bot, asking it what to do.

Speaker1:
And it had the agency and it had the creativity to go and create these pieces of value.

Speaker1:
Now, the next thing that this thing built is this service called ClawMart.

Speaker1:
And ClawMart is what we're seeing on screen here.

Speaker1:
It's a service in which agents, OpenClaw instances, can sell their skills to other OpenClaws.

Speaker1:
So if I was using my OpenClaw and I was actually scrolling through the website

Speaker1:
and I was looking at interesting things.

Speaker1:
And one that I found really cool was they had this browser-based research tool

Speaker1:
that allowed you to scan through a lot of the top news articles and understand

Speaker1:
what was happening in the world of AI and Frontier technology.

Speaker1:
I could have my CloudBot instance go to this website that FelixBot built and

Speaker1:
buy a skill from it that teaches it how to do these skills.

Speaker1:
So you could see the most popular persona is the Felix one.

Speaker1:
He's selling it for $99 on the website where you can actually emulate all

Speaker1:
of the abilities of this agent and i found this to be

Speaker1:
such a fun interesting use case of how you can actually

Speaker1:
use this thing to generate money and generate real productive value like claw

Speaker1:
mart is a really valuable service that i think a lot of other ais can use and

Speaker1:
it's funny because it's all ai to ai transactions i think in the last week there's

Speaker1:
a post somewhere that says they had two thousand dollars in sales transactions

Speaker1:
between OpenClaw agents it's pretty awesome Yeah,

Speaker0:
Yeah, super cool. What this reminds me of is the early versions of the Apple App Store.

Speaker0:
It kind of looks like an app store as you're scrolling through this,

Speaker0:
except it's like services through these agents.

Speaker0:
I think this is the future of how all these online interactions actually happen.

Speaker0:
It kind of makes sense that an agent doesn't really interact with another human,

Speaker0:
and it doesn't really kind of code skills from scratch each and every time.

Speaker0:
This is the whole argument around the SaaS debate and why SaaS stocks have been dumping.

Speaker0:
It's because, oh, this AI can just kind of vibe code your product.

Speaker0:
No, that's not really what's going to happen. you're going to just rely on the

Speaker0:
agent that has the best product and pay them whatever it is between 50 to 100

Speaker0:
bucks to get access to that thing. And turns out these things kind of make money.

Speaker0:
In this particular example, I think they're using kind of crypto or stablecoin

Speaker0:
payments to pay for each of these different skill accesses, which kind of gives

Speaker0:
them this autonomous feel. Now, it's not quite autonomous.

Speaker0:
They're not kind of like independently doing this themselves.

Speaker0:
They're being directed by their human supervisors, if you want to call them

Speaker0:
that, the owners or the creators of these different OpenClaw agents,

Speaker0:
but it's still nevertheless very cool to see.

Speaker0:
And the speed at which these things are kind of popping up every now and then, Josh, is kind of crazy.

Speaker0:
And I think that's kind of like the main message I want to like share for this particular archetype.

Speaker0:
If you are someone that has high agency or that has a lot of operational work

Speaker0:
in their lives and you want to try and automate that and you have the kind of

Speaker0:
technical know-how skill set to interact with the CLI interface or whatever

Speaker0:
that might be, you can do these right now.

Speaker0:
And these demos really, really prove that.

Speaker0:
There's one other example that I want to show, which I thought was kind of crazy.

Speaker0:
This guy wanted to buy a car, AJ Steubenberg.

Speaker0:
And he asked his Claude bot the night before he went to bed,

Speaker0:
this is the car model that I'm looking for.

Speaker0:
This is the kind of price range I'm looking for. I think he said it was like

Speaker0:
$10,000 to $15,000 that he was willing to spend. I think it was secondhand.

Speaker0:
And he specified his area that he lives in. And he said, if you could do some

Speaker0:
research for me, and if you find a good deal, let me know. It took two to three days,

Speaker0:
This AI agent didn't ping him at all, handled the negotiations,

Speaker0:
found the car room, evaluated the car itself online through imagery,

Speaker0:
cross-referenced it with a bunch of other show dealers.

Speaker0:
And in the end, not only did it get him his dream car, but he saved him $4,200 doing that.

Speaker0:
That would have taken a car dealer or some kind of like intermediate broker

Speaker0:
to do that for you, which you would need to pay that $4,200 for.

Speaker0:
But an agent did this for the cost of your electricity. It's pretty awesome.

Speaker1:
Yeah, and why is this unique to OpenClaw? it's because of the

Speaker1:
tool use if you think of OpenClaw it's kind of like giving hands

Speaker1:
and a tool belt to something like chat gpt

Speaker1:
where now it has the ability to go and use tools

Speaker1:
on your behalf so in this example where it saved this

Speaker1:
person forty two hundred dollars in a car purchase it contacted dealers via

Speaker1:
email and iMessage because if you run it on a mac mini it can actually control

Speaker1:
your i messages um and it handles the back and forth negotiation it actually

Speaker1:
works directly with the dealer in a long time frame in which you can't do using

Speaker1:
these traditional products.

Speaker1:
So a product like Claude Cowork, it probably wouldn't be able to handle this

Speaker1:
because it doesn't have the extensive

Speaker1:
tool use or the thinking patterns or the heartbeats baked into it to continue

Speaker1:
to follow up over and over and over for multiple days without prompting it at all.

Speaker1:
So this to me, the really cool example, because everyone buys cars,

Speaker1:
right? And this applies to other things.

Speaker1:
A lot of people buy stuff on Facebook marketplace. They're looking for a specific

Speaker1:
thing somewhere. It can just go scan it. It can negotiate on your behalf.

Speaker1:
You could tell it the parameters that you want. And it's a pretty powerful use case.

Speaker0:
So moving on to the second archetype of user, we kind of bracket this as the

Speaker0:
knowledge worker or the creator, right?

Speaker0:
So this is kind of like you have some competency using computer.

Speaker0:
Maybe you do it in your day to day, but it doesn't consume your entire life.

Speaker0:
And you want to know what OpenClaw can do for you.

Speaker0:
There's this really fun example that you had here, Josh. Let's walk through it.

Speaker1:
Oh, this was great. Yeah. So there's this woman. She's so sweet.

Speaker1:
She lives at home with her kids and she's homeschooling them.

Speaker1:
And she has created this curriculum that she wants to teach her kids throughout the school year.

Speaker1:
And she fed the curriculum to her agent, her OpenClaw instance.

Speaker1:
What she also did is she bought a 3d printer for the

Speaker1:
home and because gemini 3 now works

Speaker1:
with 3d printer files she created an api

Speaker1:
key she fed the api key to her OpenClaw agent

Speaker1:
and she said hey go through the itinerary that

Speaker1:
i have developed for my children who i'm teaching a series of different

Speaker1:
subjects figure out which subjects are interactive

Speaker1:
enough to warrant you printing a 3d printed

Speaker1:
like thing let's say you're learning about biology it'll 3d print

Speaker1:
a brain or 3d print a bone to see what it looks like

Speaker1:
and proactively go and print these items for each day's agenda so she hooked

Speaker1:
it up to her 3d printer she gave it a gemini api key and now every day before

Speaker1:
the kids are going to learn their lesson the printer turns on it spins up it

Speaker1:
3d prints whatever they're going to be learning about for the day and they had this visual aid

Speaker1:
that's physical and tangible to help them with the lessons.

Speaker1:
So it's really bizarre and strange use case, but fun. It's like you really are

Speaker1:
only limited by your creativity when it comes to using this stuff.

Speaker0:
Within this archetype, I also want to use our personal experience interacting with CloudBot.

Speaker0:
You and I have been testing it around for about a week or so.

Speaker0:
And we've also been comparing it with other similar tools like CloudCowork.

Speaker0:
As podcast creators or content creators in general, one massive unlock for CloudBot

Speaker0:
is that it automates not just the research side of things, which I relied upon

Speaker0:
for ChatGPT or Anthropics Claude quite a bit, but it actually kind of helps form the agendas.

Speaker0:
I can connect it and it texts me about certain updates of news headlines and stuff like that.

Speaker0:
The major unlock for me, at least, is that added step of cognition for me,

Speaker0:
that instead of me being like, oh, I see this news article, here are my thoughts

Speaker0:
on it, let's put that in a document and let's create an outline for it.

Speaker0:
Claudebot can actually just do all of that for me. Now, it comes with a twist,

Speaker0:
which is you need to give Claudebot access to, I keep calling it Claudebot,

Speaker0:
it's OpenClaw, but it was also called AutoClaudebot as well.

Speaker0:
You need to give it access to certain files, components, and your desktop.

Speaker0:
So you need to be comfortable enough to know that and also have the know-how

Speaker0:
to make sure that it doesn't become a larger security implication,

Speaker0:
it's really useful for just automating a bunch of stuff. And the net positive

Speaker0:
is I have a bunch of free time now for me to do other stuff to create other kind of episode stuff.

Speaker1:
So my experience has actually been a little bit different than that using it

Speaker1:
because the one use case that I had is well, we spend a lot of time on Limitless.

Speaker1:
How can I automate as much of the process as possible? So we spoke for a little while.

Speaker1:
I probably spent half a day pretty

Speaker1:
casually kind of going back and forth over the course of half a day,

Speaker1:
just really explaining to it what I need, how things work,

Speaker1:
where it could possibly help me and what i found is

Speaker1:
that every along every step we would create a new

Speaker1:
skill there were more and more blockers and more

Speaker1:
and more like issues that i would run into that i had to fix i had to get api

Speaker1:
keys i had to use different browser sessions it created a lot of complexity

Speaker1:
that for it to actually help me and do the things that i wanted to do i found

Speaker1:
it really it was just taking more time than it was worth to debug all of these

Speaker1:
things every time we tried to So for example,

Speaker1:
I was trying to get it to upload our content to YouTube and to Spotify and to

Speaker1:
RSS where everyone listens to the episode.

Speaker1:
And it wasn't able to get access to the browser the way we needed it unless I had an open tab.

Speaker1:
And even through Brave, you had to feed it your API keys and login details,

Speaker1:
which was a little scary.

Speaker1:
And there were just a lot of errors and bugs. And then overnight,

Speaker1:
I told it to update itself.

Speaker1:
I woke up in the morning and it was dead. And I had to spend

Speaker1:
an hour reviving it and debugging it so it's it's a

Speaker1:
highly technical process that does have a

Speaker1:
lot of upside but i find that there are still a lot of growing pains with an

Speaker1:
early open source beta software so while these are great use cases and there

Speaker1:
are some good ones there is also generally a lot of pain and troubleshooting

Speaker1:
that comes associated with these prior to i guess eclipsing that threshold in

Speaker1:
which it becomes worth it

Speaker1:
And some of these people like Nat, clearly he's eclipsed that threshold where

Speaker1:
he has learned, he has trained his bot, he has worked with it enough to make

Speaker1:
it proficient and highly skilled and actually deliver value.

Speaker1:
But I think in order to get there requires a lot of persistence and troubleshooting

Speaker1:
and technical ability that maybe a lot of people either don't have or maybe

Speaker1:
just don't want to commit to do.

Speaker0:
I mean, just to engage with this thing in the first place, you need to go through

Speaker0:
an entire setup of understanding what Node.js is, installing that.

Speaker0:
Interacting with the command line interface and a bunch of other different things.

Speaker0:
But there's an additional tier that you can access here, which is archetype

Speaker0:
number three, which is the privacy-conscious individual.

Speaker0:
And the kind of way that I would describe this individual is they want to run

Speaker0:
this AI agent locally at home. They'll buy the hardware and infrastructure.

Speaker0:
The popular case has been the Mac Mini, which is sold out across any kind of

Speaker0:
Apple interface that you can or store that you can buy this from right now.

Speaker0:
I think it goes for about 600 bucks per unit and run it locally at home.

Speaker0:
And the advantage of this is that all your data and tool access is private.

Speaker0:
So the comparison here would be if you gave.

Speaker0:
Google, OpenAI, or Anthropic Access via a same service, they would be able to

Speaker0:
see all your stuff and potentially use that data for something else.

Speaker0:
Now, of course, you sign terms and agreements that says that they won't use

Speaker0:
it, but there's always that risk.

Speaker0:
So for the privacy conscious, for the open source people that want to run things

Speaker0:
locally at home, this tier is for them.

Speaker0:
And it brings up an interesting conversation around this thing called on-prem becoming the new cloud.

Speaker0:
Now, on-prem stands for on-premise, which is basically moving your hardware

Speaker0:
onto your own home ground, where you run and operate your own hardware instead

Speaker0:
of relying on cloud or private instances of cloud,

Speaker0:
which is funny because it kind of sounds like we're going backwards here,

Speaker0:
but it sounds like it's the most important arsenal going forwards into this

Speaker0:
AI future where you probably don't want all your email credentials,

Speaker0:
credit card credentials, or any of that being exposed to bigger corporations.

Speaker0:
So it actually requires you to run this at home.

Speaker1:
Yeah, and there was a great study that I saw from Basecamp, who is,

Speaker1:
they're just a big compute provider.

Speaker1:
And they posted an article saying why we

Speaker1:
left the cloud and the highlight of this article was actually leaving the

Speaker1:
cloud will save them 10 million dollars over five

Speaker1:
years which is a huge amount of savings and not only that but the security features

Speaker1:
are going to be much stronger like you mentioned people who work with something

Speaker1:
that is a little more sensitive than average let's say you're working in legal

Speaker1:
or you're a psychologist and you don't want to break that privacy layer a lot

Speaker1:
of the value from this will come from the fact that it truly is open source

Speaker1:
and it can be run locally on your own machines.

Speaker1:
Even so, you mentioned the Mac Mini, the Mac Studio, which is the level up from

Speaker1:
the Mac Mini, has enough RAM and enough compute power that it can actually run

Speaker1:
these open source Chinese models that have come out recently.

Speaker1:
Locally on a single machine and therefore you

Speaker1:
can run the entire operation local to your machine nothing leaves it's all open

Speaker1:
source code and that's a really high value thing for a lot of these companies

Speaker1:
and when you when you scale that up i mean past the individual user you get

Speaker1:
to large corporations they don't want to leak out this data and creating these

Speaker1:
corporate plans with custom rollouts is very difficult so why not just buy

Speaker1:
a whole bunch of Mac studios and run local models on-prem.

Speaker1:
I mean, it's a really valid argument. And I think it starts with the user level

Speaker1:
now, but I can very clearly see this continuing through these examples like

Speaker1:
Basecamp and many more that they're going to continue to pivot towards more

Speaker1:
on-prem compute. It makes a lot of sense.

Speaker0:
Yeah, and just to be clear, the security implications are a lot bigger and maybe

Speaker0:
understated throughout all the OpenClaw hype.

Speaker0:
Let me present a different question or proposition to you.

Speaker0:
Imagine giving chat GPT that you interact with every single day,

Speaker0:
access to your wallet, your medical records, and allowing it to run loose in

Speaker0:
the world and do whatever it wants independently.

Speaker0:
The difference here is previously you needed to prompt it to do something.

Speaker0:
Now using OpenClaw, it just goes off and does things. You would maybe feel a

Speaker0:
little cautious. I know I do.

Speaker0:
And so some of these security risks are actually real. Like two examples that

Speaker0:
I have here is this guy was using OpenClaw and he noticed that his agent was

Speaker0:
trying to brute force into his own server, which he did not give access to.

Speaker0:
Brute force meaning trying to

Speaker0:
crack his literal password to get it to and overcome his firewall, right?

Speaker0:
It's a Trojan horse. The Trojan horse, exactly. So if you kind of like,

Speaker0:
and this was him running it on a VPS, by the way.

Speaker0:
So if he had been running this locally at home, which someone that I know,

Speaker0:
oh yeah, that's right, it was me, did it the first instance that I set this thing up.

Speaker0:
It could potentially do to certain security complications.

Speaker0:
It's funny, before recording this, Josh, you were describing an instance where

Speaker0:
OpenClaw agents can audit themselves.

Speaker0:
And I remember seeing an example of someone asking their agent to do this and

Speaker0:
it indirectly managed to get the password credentials to someone's credit card,

Speaker0:
to their owner's credit card via doing that.

Speaker0:
And it kind of automated itself and said, hey, I probably shouldn't have done

Speaker0:
this, but just letting you know that I did do this, right?

Speaker0:
So there's all these different kind of prompt injection vectors or hack kind

Speaker0:
of vectors that could lead you to kind of getting maliciously exploited.

Speaker0:
But I want to move away from this and address kind of like the elephant in the room, which is

Speaker0:
If you wanted to run this yourself, what are we looking at here?

Speaker0:
What does the setup look like? Is this something easy that I can do and spin

Speaker0:
up in one click? Or is this something much, much harder?

Speaker1:
Yeah, it depends on your technical abilities, really, and your willingness to

Speaker1:
pursue troubleshooting.

Speaker1:
Because it doesn't always go smooth. And if you aren't familiar with a command

Speaker1:
line interface, it gets a little tricky at times.

Speaker1:
I think one of the important things to note is where we are right now is very

Speaker1:
open-ended and early. So what this is, is very much a...

Speaker1:
An experimental software that is untapped in its potential, but as a result

Speaker1:
has a lot of fuzzy edges that you're going to have to work through in order

Speaker1:
to extract the value that you want.

Speaker1:
What we're seeing is a progression towards more focused versions of this through

Speaker1:
these new deployments like OpenAI, I'm sure it's going to do through the acquisition.

Speaker1:
But if you do want to set it up, you're going to want to get familiar with the

Speaker1:
command line. And there's a lot of great tutorials about it.

Speaker1:
The website that we're showing on screen right now called MyClaw, and

Speaker1:
we'll link it in the description it's a really amazing website that shows

Speaker1:
you specific examples of actual use

Speaker1:
cases that you can have so we have auto flight check-in and

Speaker1:
smart file management and automated grocery ordering and this is an easy way

Speaker1:
to kind of start to build in these integrations as you experiment but i think

Speaker1:
the reality is is that this is for someone who wants to experiment doesn't mind

Speaker1:
troubleshooting is technically adept and in the case that you are not,

Speaker1:
which I assume is actually a majority of the people listening to this,

Speaker1:
the best thing you could do is get that $20 a month Claude subscription,

Speaker1:
get on Claude Cowork, and let it interact with local instances on your computer.

Speaker1:
Because Claude Cowork has the security parameters in place, it works locally

Speaker1:
to specific folders at a time, and it has this amazing agentic ability to control your Chrome browser.

Speaker1:
So it can do all the browsing tasks for you just in a much more constrained

Speaker1:
and focused way that I think is much easier, but also a lot more valuable to

Speaker1:
a lot of people than going through the headaches of getting this sorted and set up.

Speaker0:
I mean, there's trade-offs between those two tools as well, right?

Speaker0:
Like Claude Cowork, we want to get to the ability and capability of OpenClaw, but it's not there yet.

Speaker0:
It's more censored. Think of Claude Cowork as like a censored version right

Speaker0:
now, whereas OpenClaw is kind of uncensored.

Speaker0:
It can run off and do anything it wants. And there's pros and cons, obviously, to both.

Speaker0:
But I think that's the major benefit to OpenAI acquiring OpenClaw.

Speaker0:
In about three to six months, we're going to have Claude Coworker V2 and OpenAI Claw.

Speaker0:
Maybe that's the new version of the bot that we're going to talk about.

Speaker0:
That will be this more curated experience that is more secure.

Speaker0:
It runs within a sandbox. You know exactly what it's doing and it can't run

Speaker0:
off and steal your credit card information.

Speaker0:
Now, if you want to set this up now, if you're listening to this and you're

Speaker0:
still like, I don't want to wait three to six months, I want to try this.

Speaker0:
My advice would be simple.

Speaker0:
Host it on a cloud vps when you set this up

Speaker0:
make sure you set api limits and access so that it doesn't but you don't wake

Speaker0:
up the next day and it's burned like eight hundred dollars worth of your code

Speaker0:
code tokens please don't do that um i've seen many cases of people doing that

Speaker0:
and it's it's not great run it in a docker sandbox so that it's not uh available

Speaker0:
to access any tools that you don't want it to um and the last point that i'll say is

Speaker0:
just start off with one use case. Maybe the morning brief example that Josh

Speaker0:
gave earlier on in this episode or something that can help automate one aspect of your work.

Speaker0:
But just don't give it access to any financials just yet. These things will

Speaker0:
get kind of way more powerful over time.

Speaker0:
And I think I read somewhere that there were 19 releases, so updates for OpenClaw in the last 14 days.

Speaker0:
Think about that. Imagine how often you get iOS security update or like iOS software update.

Speaker0:
Imagine 14 of them, and 19 of them in the last 14 days just insane so this thing

Speaker0:
is improving very quickly.

Speaker1:
Yeah and if you didn't understand some of the words that you

Speaker1:
just was just saying like using docker for instance uh my

Speaker1:
preference and my suggestion would just be hey wait like other

Speaker1:
companies are working so fast to roll this out in fact we have

Speaker1:
two instances already happening this week the first being manis which

Speaker1:
um you might remember that old company that meta

Speaker1:
bought they rolled out manis and um manis is

Speaker1:
essentially meta's version of OpenClaw except it

Speaker1:
has a little bit more rails it has a nice user interface and it's

Speaker1:
it's very easily accessible for people who aren't very technical and

Speaker1:
the second is kimmy claw kimmy actually rolled out their own instance kimmy

Speaker1:
the chinese model that we covered in a previous episode um they rolled out an

Speaker1:
instance that you can actually go and use through again a user interface and

Speaker1:
i'm sure open ai and chat gpt are working very quickly to roll this out and

Speaker1:
integrate this in a way that's approachable so i'd say using OpenClaw today

Speaker1:
is an open source open-ended wild west version but if you just wait a few more weeks,

Speaker1:
there will be plenty of instances in which you have something that exists closer

Speaker1:
in between Claude Co-Work and OpenClaw along that spectrum of close to openness.

Speaker1:
And it might just be worth waiting for that instead. But if any of those examples

Speaker1:
did seem interesting, go try them out.

Speaker1:
It's very cool software. If anything, you'll learn a lot by failing or succeeding.

Speaker1:
And I think that's the really important thing. Again, it's just to stay on top of these things.

Speaker1:
You want to be engaging with them, interacting with them, testing things out

Speaker1:
so you understand how they work and you're kind of better equipped to deal with

Speaker1:
this as things change so quickly.

Speaker0:
My take-home homework, for those of you listening to this, is to try out that

Speaker0:
Kimi K2, that Chinese model extension. They launched or integrated OpenClaw into your browser.

Speaker0:
So it's sandboxed to one environment, and it's super easy to use.

Speaker0:
Try it out on your email, or maybe it saves you $5,000 buying a car,

Speaker0:
or whatever you want to try. Just give it a go.

Speaker0:
Let us know in the comments, actually, what you end up trying out,

Speaker0:
and whether it was actually useful to you, or if this is something that you're

Speaker0:
just going to wait more patiently for.

Speaker0:
And when those new versions come out from the likes of Anthropic and OpenAir

Speaker0:
you can bet that Limitless is going to be the first ones to cover it so make

Speaker0:
sure you guys stay tuned we've been loving the engagement that you've been giving

Speaker0:
on our episodes we released an episode covering the entire acquisition of that

Speaker0:
OpenAI acquired for OpenClaw for billions of dollars,

Speaker0:
absolute banger of an episode definitely go check that out and we're going to

Speaker0:
have a bunch more episodes coming out later this week and over the next couple of weeks so Josh.

Speaker1:
And for the people new here um 85 aren't

Speaker1:
subscribed that watched in the last 30 days so make sure

Speaker1:
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Speaker1:
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Speaker1:
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Speaker1:
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Speaker1:
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Speaker1:
we have a newsletter it comes out twice a week it's really cool uh the week

Speaker1:
the one on friday is the weekly recap the one on wednesday is a thought piece

Speaker1:
covering a topic that we'll eventually cover on the podcast and yeah if you're

Speaker1:
new here don't forget to subscribe thank you so much for joining with us and

Speaker1:
uh yeah we'll see you guys in the next one see you guys