# OpenClaw 2.0: Multiplayer AI and Shared Context

**Podcast:** Dev Interrupted
**Published:** 2026-10-20

## Transcript

Welcome back to Dev Interrupted, brought to you by LinearV.
Your engineers are faster with agents, but passing work between those agents still hits barriers, even things like copy-paste.
My guest today, Vincent Koch, chief architect of the OpenClaw Foundation, shows us a better path, multiplayer AI.
His team built the multi-user OpenClaw experience with OpenClaw itself, using it as a collaborative surface.
And in doing so, they discovered how teammates and their agents can work side by side.
Their agents stay aware of everything happening across the project, but Vincent says the missing piece is always still the why.
Feeding that kind of context into agentic work and measuring what agents ship is a big part of what we build here at Linear B and Dev Interrupted.
But do you know where your why lives?
Let's find out in my conversation with Vincent.
Today, we're going to open the hood on the capabilities of OpenClaw, but also zoom in on the implications this has for have for leaders within pre-existing, pretty mature, large engineering teams, engineering teams that have engineers, which, by the way, are heavily leveraging and utilizing OpenClaw and OpenClaw-like technology.
What are the kinds of things that they need to be understanding to stay on top of the way that their engineering org is evolving?
And I can't think of anyone better to dive into this with because you have so much insight into how folks are using this tool as such a major driver of it yourself.
So, you know, Vincent, welcome to Dev Interrupted.
We're really excited to have you here.
Likewise.
Thanks for having me on the show.
Amazing.
Well, I want to start off by first off congratulating you on OpenClaw 2.0, a major improvement and achievement as well to the entire code base in terms of the features that have been available.
We've talked about it a bit on the show because a really exciting part about OpenClaw, at least for us here at DevInterrupted and some of the folks that have chimed in, is really about the shared collaboration-driven experience.
This has been the real hunger, I think.
of engineers that have been experimenting with AI since the beginning of the year, since Gastown, you know, let the world on fire and realize that.
Yeah, be OK, great.
Well, you're saying beads.
My producer might as well come off mute and just be like, this conversation is over because I say beads in every conversation and everyone thinks I'm a crazy person.
I actually was just recently at Lead Dev.
I was giving the I was giving a talk there and the talk was about why Miss Frizzle would be a great SRE.
And really, it was actually just like a hidden lesson in how to use beads to just make everything possible over time.
So beads are a fundamental part of what makes this possible.
They're primitive, right?
S.E.B.
I get in our articles has talked about this extensively.
Chris Sells is out there building with this as well.
And but OpenClaw.
we've argued on the show, many have talked about is also its own kind of primitive.
That's what's so exciting about the OpenClaw experience and the way that it grows and it conforms to your domain expertise.
So, you know, what is, what has that been like for you to like develop and build something that, that, that honestly has such wide use usage.
Like obviously it has such a big perspective.
I think like for my personal experience, It's been kind of interesting because I've worked on large scale enterprise work from like airlines, banking, like I'm talking huge reach.
But this just felt different.
You know, I've worked on open source as well.
But it's just the velocity of everything.
I think I remember one stage, we're at GTC NVIDIA last year or this year.
This year, it feels like last year.
That's what I'm saying.
Like even I'm getting it twisted, like just a few months ago.
and jensen is saying oh this is like the biggest thing since you know the internet or computing i can't remember the exact words don't quote me on it but it's just the levels of insanity just kept increasing increasing i had like rap artists talking about this stuff on x i had you know uh c-suites of like major fortune 100 companies like sending me messages or like you know issues they're having it's just like it just kind of exploded and like it was definitely a hype moment there i think there was definitely a hype cycle but for the first time it felt like agents and ai had like escaped the containment of like the tech ecosphere and the tech pro culture and all the rest of that that comes within and for the first time it was part of the zeitgeist and you had like your uber driver talking about this claw ai thing i remember i was like in a bar in new york in my hotel just like trying to take a breather before this clock on event that we just had i was like People had like walked for, the queue was like three blocks long.
And the guy said, have you heard about this like AI thing?
It's like this lobster.
And I'm just like, my God, what is happening?
What is happening?
There's like echo chamber of a world.
Yeah, it's escaped, right?
And I think what we've seen post that, like what I like to call the post-claw era, is like this like explosion of agentic AI.
That's one sort of like token usage has gone through the roof everywhere.
but also like individual AI agents and things that people are building that are like somewhat like actually useful and like built around taking action and making change and doing something that's for you or for your company or for your team.
And just seeing that explosion in the industry and seeing that all come alive has been amazing as well.
I think it's great that we were sort of part of that story or that next chapter of what AI might look like for everyone.
You're so right about it, escaping containment.
It was out in the wider world.
On the show, when we talked about OpenClaw and its meteoric GitHub stars back when we could see those and just how many people were obsessed with the project and also just how many people had downloaded it.
Early OpenClaw was just so massively downloaded and people were experimenting with how to run it themselves.
And for the first time.
It felt like folks could have just a little slice of the AI fun for themselves because that's part of what makes OpenClaw really appealing.
Everyone is interacting with OpenClaw, downloading it, experimenting with it.
You get a surge of interest.
And this is something I actually really want to zoom in on because.
We've been here on Dev Interrupted trying to crack the puzzle around what open source contributions and open source world looks like in an agentically enabled developer world.
Because in an agentically enabled developer world, the interactions between maintainers and contributors with ideas is totally different.
than it was when, you know, everyone was submitting code and that was the main contribution at play.
So as like in this case, you know, the chief architect of the project, how do you think about organizing the way that people can come in and engage with the project and without, you know, that way you can kind of keep it on rails as a builder?
I don't think there's any like perfect source to this.
It's like an ongoing thing when it comes to open source projects and work.
And I don't say, I don't think it's like a...
fundamentally like something I am personally like driving.
I think it's a concerted team effort.
But I would say like, yeah, think about a project like this and you think about open source.
Everyone wants to help.
So you do end up this like this team of individuals that would, you know, essentially maintain the project.
But then on top of that, you have that group of people working with the community and the individuals to figure out what's right.
And I think that's where.
we've been always listening to the community.
We have like a Discord of like, I think 170,000, almost 200,000 people.
And I think we spend a lot of time on there.
You know, Peter himself is continually on X messaging and responding to everyone all hours of the day.
And I think we just engage.
And I think the figuring out what people want, figuring out how people want to help and how we can swap people into that mix is something we kind of review on a continual basis.
And it's like a concerted effort.
And in some ways, the community helps us decide what that looks like.
Yeah, it's about sourcing the signals.
Like what you're calling out is really what I think stands out from a lot of like the heavy hitter, the large open source successful projects right now, the ones that are drawing attention to them from folks is really about encouraging people to create the space for ideas to come together and share what works and create a space to collaborate.
Because when I think of just the.
the widespread success in adoption and just mental share of OpenClaw within the world.
So much of that, I think, has to do with the fact that there was a while where you could pivot left or right and find somewhere with somebody that you could pair up or duo up and figure out and learn OpenClaw together.
And that's where I think magic, that's like the magic of open source, I think, is when people just spontaneously create this learning environment.
And I think that happened.
around OpenClaw especially because it represented such an opportunity to experiment with what building and owning these things would look like, which also brings me to another interesting reality is that for our listeners definitely are within engineering teams where having something like OpenClaw...
maybe introduces a lot of risk and questions.
And it's not a tactic that they can feel really like they can pull out of their tool belt.
Instead, there's learnings from OpenClaw, they get applied into how workflows are structured, into how we think about sharing context, and specifically with OpenClaw 2.0, the multiplayer aspect, because that becomes the most important thing that we're all seeking is, I'm agentically enabled, you're agentically enabled.
How does my gas town call your gas town?
We have all sorts of fun versions of this right now, like claw tag and things where people are collaborating in Slack.
I really think that Open Claw also becomes a place like this.
So I'm curious, like, what are, what has it been like for you to interact with larger teams or teams of people that are using Open Claw?
I might just touch on a couple of things that you raised there.
So you mentioned risk.
You mentioned, like, multiplayer and stuff like that.
So I'll try and unpack that the best I can.
I think when it comes to, like, risk, there has been this, like, at the start when Open Claw kind of came about.
It was never designed for enterprise use.
It was never designed for anything but like a couple of developers tinkering on a side project, right?
So obviously it had inherent security risks, like your channels and messages weren't entirely safe.
And then everyone decided to kind of use our project and the hype as a little bit of a punching bag.
Every security researcher, everyone's like, oh my God, look, I did another security vulnerability or something like that.
And like, I think like 90% of it was completely slop.
So we've...
if you go on the openclaw.ai website, there's a security page.
It explains like all the vulnerabilities that were the volume, where they're coming from, what they're responsible for, things like that.
And like majority of that has been a big headache for us.
And it's like this constant story that we have to kill 10 people.
Like, I don't think this is any more unsafe or safer than another coding agent that's out there.
Like we have an insane volume of security researchers from some of the top research organizations.
and top tech companies that are working with us as maintainers.
And yeah, we've done a lot of work.
So we've actually been contributing to security around skills more publicly.
So we've been like releasing data sets and releasing research that's helping the community.
Like how do we make agents safer and things like that as well?
So that's the trust aspect.
I think that's something that's like core to us and something that we deeply understand.
But also on the flip side, one of the missions of the OpenCore Foundation is to make this thing accessible.
and universal and how can I say like yours, right?
So I think having a solution that's open source.
and that you control and own and you can use any model, you can use any channel provider, you own the data is very important to us.
And I think having that differentiation in market is going to be very, very good for people.
I think from a, you know, I just want to know that the agent that I built and the data that I've collected is like in my control.
And I decide which model I want to use.
I decide where I want to send this data.
And then when we kind of take that into an enterprise setting, going into things like multiplayer, One of the things I want to sort of backtrack and sort of explain a little bit is that when we're building OpenClaw ourselves internally, everything that we've sort of started to feed into OpenClaw and build around OpenClaw was just because of our own internal frustration.
We talked about, you know, Steve Yeager's Gastown and Beads.
If you don't know what that is, do have a look.
It's like the early versions of looping and all that methodology and things that we've spoken about.
We're essentially doing that.
We were essentially looping and going full 100% on agentic engineering, but then quickly found out there was issues.
For example, how do we test this stuff?
Okay, we don't have sufficient CI testing infrastructure.
What's out there?
Well, nothing's kind of out there, so we're going to have to build it.
So we built this thing called Crabbox that can use any of our cloud providers and build testing infrastructure.
So we started building these things around the frustrations.
But then very recently, what ended up happening is we kind of had OpenClaw at a certain point.
But internally, we were like, quote unquote, meat proxying the workaround.
So we're like, you know, my agents are working on it.
I'm passing it to you.
I started experimenting on what multiplayer could look like.
Peter was definitely sort of experimenting.
Different people were working around this.
One of the demos I did a little while back was like, I could have a codec session and it would work a bit like a reverse tunnel.
And you would get like a web link or a QR code.
You can scan and you can remotely like take over my session.
And there was this idea like, can I control into other people's sessions?
Can my agents control other people's sessions on the team?
And then we quickly realized, actually, no, this should live inside of OpenClaw.
We should just all be able to like work on an agent in a multiplayer environment.
So what we have now is OpenClaw itself is able to work in sort of this multiplayer environment.
But what we've been doing is internally doing this.
A lot of our workloads are running on an OpenClaw server itself.
And we're using that to build OpenClaw.
And that server, or different OpenClaw servers, shall I say, are basically, we can all jump in as a team and coordinate and take over other people's work.
So if someone goes to sleep or there's an issue somewhere, someone goes away, we can go in or someone wants commentary.
they can tag us and we can kind of jump in and give feedback.
So what's happening here is like the way we manage work completely changes.
It's like the levels of ownership and trust and how we work as a team completely shift.
This also means that like agents that are part of the team as well, like other claws can also potentially participate and do this.
So for example, they might move work from one box to another or they might, you know, do certain things around that, like help us with certain things.
But what we get, what we've essentially done as a team is started building OpenClaw with OpenClaw itself.
We've essentially moved all our engineering effort so that the engineering work happens within OpenClaw itself rather than like a traditional coding harness.
And this has kind of allowed us to really elevate our game.
And this is why we kind of spent two months or a month and a half for our last release.
They're kind of nothing really.
People were like, what's going on here?
We just kind of felt like the experience could have been like really good.
And we just wanted to make sure everything was good.
I think it was like something close to like 70 or 80% or 50%, like the largest proportion of our latest PRs and commits were in this one release, something close to like 20,000, 30,000 PRs, an obscene number that sort of contributed to this.
And that's why we subsequently ended up calling it this sort of 2.0, but really the story was around multiplayer.
The story was around enabling people and enterprises to be able to sort of work at this like higher level with agents.
Using OpenClaw to build OpenClaw makes sense as a building primitive, as something you're going to pass back and forth.
And what you describe is really fascinating because it does change the ownership narrative.
Now the ownership becomes almost like this baton toss.
It's almost like no one, hot potato, no one drop it.
Like we're going to pass it around in a circle and make it better and better and better.
And that's a whole different experience than how we think about developing the code now where I have my cold pool of the code.
I have my work trees and forks I push back into thing.
In this case, we're actually thinking about handing off the live hot interactivity of the artifacts and the things that are getting built and being responsible for molding that context together.
It goes back to like the conundrum that I struggle with of like, how does my gas town call your gas town of what you're saying of like, how do I hop into someone else's sessions and control something else that fundamentally needs to get addressed that essentially move from gas town in my mind to like what I would call gas city.
I haven't read the Steve Vegas stuff in a while.
So I'm not sure if he's actually talked about gas city.
But it's like we have Lobsterville, essentially.
And in there, there's little towns.
Like you might have your own neighborhood in there, but that's fine.
But I can come visit your neighborhood.
You can visit mine.
We're in the same place, right?
So it's essentially for those that follow the law.
Yeah, that's kind of what it's going.
Don't worry, you're in the house of Yege.
So everyone that's listening is probably pretty well familiar.
We've covered this stuff pretty extensively here.
And I completely agree.
And that's what's really interesting to hear from that.
Y'all too, is using it to develop it itself.
I mean, it sounds like a virtuous way of continuing to develop the technology.
But I'm curious as well about knowing like...
In that feedback loop, it goes really quickly.
I'm curious, what are strategies that you have for, like, I'm curious about the artifacts that leaves along the way.
You're passing this hot context that's doing things like shipping code and getting things out there hot and ready.
But we as humans, we still need these artifacts and these ways of glimpsing in and having a shared understanding of the state.
What strategies do you have for folks leveraging this kind of?
hot potato agentic technology, I guess.
There's a couple of multiplayer experience to stay on the same page.
There's a couple of things we built over time that sort of feed into this.
So like the first one, this was prior to even us having like all the levels of automation and systems.
Like we had a point where we had, I don't know, 20,000 PRs were open on the repo and we had rate limiting issues.
So one of the things we had is this thing called Git crawl.
which essentially is like a distributed network of everything that's current within our GitHub database, clustered, embedded, rinsed by LLMs.
That's like a shared distributed understanding network and like lives on this little Cloudflare worker also works as a static sort of database.
So our agents are aware of like all the work that's going on.
from that we have something similar for our discord community so our agents are all aware of like what's being discussed inside a discord at any given time also between maintainers so we have like these like essentially like these tools or these like ways of collecting information and sharing information in an agentic manner that we've built over time and being able to share mind you all this is open source by the way on the mit license so you're welcome to go into our repo we have over 100 repositories by the way that solve different parts of this puzzle problem so do take a look And we were kind of like layering those up.
And that's what's kind of enabled our agents to be aware of each other's work really, really well and be kind of mindful of like what's current and up to date.
And then we can kind of like lay that on with like this high agency, high throughput workload, which is like, if I'm working on something and it's taking me a whole day, but someone else is already halfway through it and have solved another part of the puzzle, it might just take over my work.
And that's kind of okay.
And I'm fine with that, right?
Or vice versa.
And kind of creating this like working open, high speed, you know, your work trees are on a shared infrastructure kind of changes that.
I think it's less about moving the baton.
You still take ownership of your work and you're still kind of making sure that that thing that you were trying to achieve was done or that feature or whatever it is.
Like you might see other people's agents sort of come in and out of your work or other people might come in and out of your work.
But it kind of, it kind of.
increases the output speed and changes the way we operate quite a bit.
And from an artifact perspective, like everything is contained in the traces.
I still have like, I don't know, my last six months of traces still on my machines and it's kind of worrying.
I do need to move it off because it's like getting to a point where I don't have the storage to keep up with it.
But in a team setting, we might find that certain changes are breaking things.
Well, okay, let's learn from that.
Well, guess what?
We have all the traces so the agents can identify back.
We can build memory off these.
We can do some interesting sort of conceptual things to create like a shared understanding of what that looks like.
Or if there's a new issue, we can kind of get feedback.
Like one of the things I'm toying with at the moment, which is going to terrify people, is that like we have a team box, sure.
But what happens if like one of the threads can be made public?
What happens if I can invite?
people from the outside to be able to come in and like leave comments and like give feedback or like look at something.
So instead of me taking that from someone and then copy and paste it into that, like, again, like this reduced the friction as much as possible.
So this is where I've given examples and working towards like, let's say I'm working on a fix for something that you've requested publicly on X.
I can just give you a link and you can watch it happen in real time.
And you can give me feedback as well.
Like, no, this is not what I meant.
Oh no, this is exactly what I meant.
And I think that's changing the way we think and when we operate.
And I think it's more about reducing the friction for that information exchange and getting the information to and from those individuals as quickly as possible, where there's less people in between that as it needs to be.
If it doesn't need to evolve the humans and it just needs agent to agent in there, let's make that happen.
Let's facilitate that if we know what the information needs to be exchanged.
Your engineers used to write the code.
Now they review everything your software factory creates instead.
On October 29th at 10 a.m.
Pacific, I'm hosting LaunchDarkly's head of AI, Mark Pollux, and Kate Huston, author of O'Reilly's The Engineering Leader, for a workshop on where the software factory is heading and what it asks of the engineers who design and govern it, and how engineering health signals in Linear B can distribute that review load.
The link's in the show notes.
I'll see you there.
Yes, big plus one to I don't want us all just end up being the meat proxy.
As you said earlier, I think this is actually was driven so many to experiment with the technology because they find themselves in this copy-pate pivot position where they want someone wants access to whatever agentic riches they're perceived to be sitting on top of.
And so I think that that's like a really big thing to call out.
I also.
personally resonate with it.
It's why I've used OpenClaw inside of like my own kinds of projects to make it easier for folks to just engage and drop in themselves.
And it also actually what I've learned is it sharpens the ask.
Because when folks come to a collaborative space, there is a sense of shared responsibility.
And like we're managing now this experience, this project, this outcome together that allows somebody to sharpen what they really want and be precise.
There becomes a skill involved also in contributing and new expectations for like how you bring your ideas to the group.
And that's what I think is really, really interesting.
Just like when you talk about in general, a multiplayer AI experience.
Like a little background on me is like even before I worked at Linear B and my first one of my first jobs in tech was at Mattermost as an open source communication company.
It's like self-hosted chat, basically.
And a big part of what I was experimenting there when ChatGPT first came out was what does it look like when you put an LLM in a self-hosted chat channel where everyone is there together and you actually all create a shared context?
It's a really fascinating experience.
of just like you then become responsible for crafting that kind of experience together.
It makes also everyone a builder.
And I know that you've encountered this of where you're probably bumping into people who come from a totally untraditional builder backgrounds who are using the technology that you've built to do some really incredible things.
So what opportunities do you see like in a broader world setting?
for folks to use OpenClaw as a collaborative way to share and form and sharpen and deliver ideas, because that's, I think, the power of it is that you can use it so well to even develop itself.
You can truly use it to sharpen and develop any idea.
This becomes a really powerful thing for folks to leverage, even just in a collaborative way.
So what opportunities do you see just like for the broader world?
I think it will do for like communities, for artists.
Like even if you think about like an association or a village community or whatever it may be, multiple people need to share information.
But if something has that shared consciousness or that shared understanding of a given space or a problem, it can definitely help propel us forward.
I keep going back to this concept of wiki.
There's a really good book that kind of talks about wikinomics and how we solve problems with like the way of wiki, which is essentially this whole idea that...
If we have a shared distributed understanding of a problem and we open up that information, we can actually solve problems a lot faster and a lot better with individuals.
And this sort of concept sort of applies really well because it's like actually we're kind of elevating that process with agents and AI and we're kind of allowing that sort of shared consciousness or that shared understanding of a given issue to sort of come to life.
So really the use cases are like as large as it can be, but it's more a case of like how do you bring these individuals on board?
What I'm seeing right now is like individuals building claw-like technology, but like being really opinionated about the user interface and like targeting a specific demographic of user.
And that becomes like that version of what that looks like.
I definitely see a world where individuals are going to have their own AI personal agents.
How does that look and shape is still to come.
But you still have to think like a huge proportion of the population globally.
has yet to sort of interact with AI on like a deep personal level, like we're talking about.
So we're still talking about at least like, I don't quote me on the numbers, but I would assume like huge percentage or a great magnitude of the world's population is still yet to sort of experience what we're experiencing with artificial intelligence.
I think that's just like the real opportunity.
And I think, frankly, they're going to walk through the doors of Open Claw first.
And I think that's going to be a really interesting thing to see.
I'm really happy that you brought up Wiki because that was really where my mind was racing to next is the Wiki-ification of it all.
I love a good Wiki.
I love organizing information.
I'm a classics major by training.
So there's nothing I love more than reading a bunch of stuff, labeling it, coming up with a hypothesis and having a bunch of little sticky notes out of a super old book.
So like I'm really on board with the idea of having the wiki and using that as a collaborative format.
And I think that when you combine it with an agentic world.
when agents crawl across, create this wiki, make it up to date, connections appear where they weren't, it becomes a really amazing representation of just like the vector space even that they live in and in a way that touches really closely on what we can read and navigate as humans.
Like I.
I myself, I have like a really fastidious journaling process.
I use it.
I do a dot journal that I've kept at Obsidian long before that was in vogue with to do with agents.
I did this like five years ago and I've just done it every day.
And I use this as like a note taking system in combination with what's called a Zettelkasten, which is like a note taking system that's super flat.
All of my notes are interconnected with each other.
They're not sorted by any kind of topic.
So I already think in this massive sea of slightly connected ideas and arcs that have accumulated over time, pinned by this whole timeline of documents.
And so honestly, this becomes almost like a vectorized representation of how I think over time.
Using that as my mindset of how I come in and I use AI to collaborate and build as a GTM engineer with my team doing this, everything behind the scenes here at Dev Interrupted, which is highly agentic and runs with a whole bunch of pipes behind the scene, right?
Like applying that kind of organization method to get other collaborators involved, it creates a semantic language.
That's the power of the wiki is it allows you and agents and workflows to all express in a shared understanding this web of knowledge.
And OpenClaw, I think, is really what pierces into that and makes it then something that people are chatting with collaboratively, which is.
a really exciting thing to see.
Yeah, I have something similar I've been using for, geez, like a long, long, long time using just text files, like really simple, very similar to what you were saying.
Exactly.
I just think what's the most, what's the most future compatible way of holding my ideas over time?
I just write them down in Markdown files and I have for very many, many years.
The thing that's missing with a lot of that.
It's obviously easy because you have the history of the markdown files.
But if we take something like a memory in a markdown or something like that, what's missing is that like, how did this come about?
Like, what's the intent behind it?
What's the thinking?
And like, that's constantly missing.
And I think we need to sort of think about like, how do we capture that?
How do we capture the why I did this thing?
And a lot of that is based on my personal experience of that day, that week, that month, that project, the individuals I'm working with.
I need to capture a lot of information to try and like make sense of that.
But I would say like that's the thing that we need to sort of capture is our essence of why.
Because I think as soon as machines can understand why we're trying to do something or there's like multiple individuals and there's a lot of different whys, I think we're then able to sort of like calibrate a little bit better or at least achieve a better outcome.
So that's it.
an interesting space that I'm sort of messing around with.
Can I infer the why?
I had another side project.
I did this sort of like example.
It's called Tideclaw.
It's on my personal GitHub org.
And this basically looks through like all your messages and your codex logs and tries to infer like your personality and like why you do certain things.
And it tries to capture that and sort of mark down outputs.
And then you can use that as like steering.
or like instructions to agents to say, okay, this is my preferences for like when I work on a project, when I communicate with other people and vice versa.
So I think trying to understand intent becomes quite interesting as well for like the preference aspect and like how can agents infer that for you without even having to ask you using other types of information that you might already have on say your machine or your messages and things like that.
You know, you bring up the question of why this becomes a really important thing for collaborators to know.
And that's...
Where does why live?
A why is, can be attached to a lot of different decisions along the way, all which we need their own kind of why.
And I, I think like an important one is like you, you said maybe you make a code decision or something is implemented in this way and you want to know why we made that decision.
Maybe it was something that was part of the coding session.
Maybe it was part of the planning session.
Maybe the Y lives in when the code was created, but maybe the Y was back wherever the specs live.
Maybe the Y is actually a larger question than the specs even see.
So in this like agentic experience where people are all kind of coming in from different advantages, where do you think Y lives and how do you think that that survives?
In our team, in our case, our why lives inside of all of the messages and conversations we're having that's recorded and transcribed and fed into these databases that I'm talking about and then coupled that with the team sessions that have access to those same information.
So then as we start to steer them, it has this, not in a clearly set defined why, but it has the ability to go and search out and seek information that will tell how this thing originated.
which I think is interesting for the way that we've set up our workflow.
And I think that's why we're getting some really interesting results internally.
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Absolutely.
So you're able to really have a loop that's able to then pin conversations and intent and alignment into the actual work that's getting done.
That's pretty cool.
That's pretty cool.
I've done some experimenting around trying to do some of this as a solo person using like beads.
which you mentioned earlier and track doing task tracking through beads and having that as, as a clear record of why for certain things.
And it's interesting when in a multiplayer way of that, of being able to be something that is like encoded and shared with other people.
And really it speaks to the, the real, I think the, the biggest transformation.
especially for folks that are maybe like on a more like go to market side as well.
A big transformation for folks is that now all of the work is just around alignment.
Before it was like, oh, I'd have to need this timeline to get this asset done, to get this across the finish line, to do these blog posts and get all these social things done.
And then these designs, oh, there's all these timelines and everything has this window.
But since the compression of that window is so small now.
Because so much of that can be run in parallel.
And actually so much of that can be done on the instant that we know what we want.
Now all of the work is in the discussing, in the aligning, in making the decisions as a group.
Which is why then speaking to what you're saying, it's important that that happens alongside the agents.
So they can understand the why.
They can pack it into mechanical checks and balances that they can leverage.
Otherwise, you know, it's just all going to escape as like vapor.
And it's not going to be useful.
I agree.
It's kind of like you're not then truly capturing the back pressure.
Like when we had Jeffrey Hauntley on the show, back when the Ralph loop was transforming the world, you know, what simpler times that was.
It really was exciting to really hear from how he used that to capture the back pressure.
And this is talking about like his famous experiments around doing this.
We're back when like Claude was particularly squirrely.
Not super good at using tools.
Yeah, it was quite devious actually going through the cursed repo and seeing all of the scaffolding that he had done to make that language come into being.
It truly is a feat.
I took a look at that back in the day and I was like, this is so bad.
Why does it work?
I was so inspired by it.
I made a DSL server.
I know why it works.
It's devious.
It's devious.
the models weren't quite there, but people were getting value out of it.
There was a lot of this looping.
I think there was like other things.
I think Rookcode was another one, which was like a harness that kind of had this built into it, which would like try to build a loop mechanism into it.
But then I think at one point the models got good and it was around the time that like really open Claude took off.
And I think that's kind of part of the puzzle where, you know, the models were good enough to actually looping actually kind of started making sense.
I think right now it kind of, it's like a no brainer.
Of course.
I just couldn't see any reason why you would not do it, right?
Exactly.
But that was the first taste of it.
That was the first taste of all of us being like, oh, actually, this loop thing is starting to work.
Even to your point, like Anthropik even fully leaned in.
They had an official loop plug-in.
They had the Claude or the like the Ralph plug-in.
All it was that was loop, right?
And now that's an expectation.
Now we call that the long-running, the task-running agents.
Now it's a whole classification of benchmarks.
We don't even think about it that way.
And so it's fascinating to see how far everything.
has developed this year, OpenClaw included.
I'm curious, Vincent, you have a pretty impressive background and you've been at a lot of places and you've seen a lot of things, but most importantly, you've also been in and out of the classroom and thinking about how do I bring the understandings that I have about the data world into the next opportunity of learners.
And that actually goes back to, I think, so many of the deep rooted elements of what is going to make OpenClaw continue to be more relevant is that it's very well adopted among newer and learning engineers and people that are learning how to build.
I think that really leans into your instinct as a teacher.
And so how do you bring that into building and running OpenClaw and thinking about it?
I think a lot of the trick to a lot of teaching, actually, funnily enough, is less about me telling people what they should or shouldn't do, but it's actually listening to what people are not understanding and then like how do I fill that void and how do I take them on the journey for everyone's context I teach at universities um mostly postgraduate around agentic AI and artificial intelligence everything from deep learning to like agents I remember even I used to have classes in the early days of like LLM's like oh my god let's fine-tune a small model like let's let's build a rag and it was like fascinating right now this is just like everyone kind of understands how that stuff works even if you don't need to like go into the code, but you conceptually can understand it.
But I think it's just a misunderstanding or like getting a level head, a level footing in terms of like what's happening.
So I think a lot of teaching is about sort of taking people on the journey and like helping them see where the gaps are, but you kind of need to be able to listen and understand that.
So I think the key for myself and the development work that I do in OpenCore is really about like really dialing into like what are people like not understanding here?
Like where are people getting stuck?
Where is it like?
not making sense.
I think sometimes it's about crafting the narrative.
Like let's call it multiplayer.
Let's call it 2.0 and suddenly people are like, oh wow, this thing's changed.
It's different.
Or it's like more about like, how do we change the experience and how do I put myself in the shoes of the users?
And I think this is just like a constant battle of like trying to adapt.
And sometimes what you're going to find out is going to be things that you don't want to hear.
It's going to be bad and ugly, or it's going to be things that kind of challenge your thinking about what you think is the truth.
But I think sometimes it's kind of healthy and refreshing.
Easy for us as an organization or as a team, because we are quite small and we're moving really fast, that we have this sort of license to be able to adapt and break things and just kind of break things in the sense of not break if you're users, but like change the way we do things.
I can see how that might be more challenging in a much larger organization that's been around for quite some time and it's used to a certain pace or it's used to working in a certain way.
And then you kind of come along and go, let's just use agents to do everything.
Let's slash loop on this and go Ralph loop and beads and everything else.
So I think that's where I don't have the answer.
It's like, how do you help an organization mature?
And in the past, when I've done digital transformation, which just basically means change, has been like, find a team that is like so willing to like, dog food this thing and start small find a project start small and just like grow from there don't try to like convince your whole entire company to like start looping everything don't convince like everyone to like embrace ai like just start one place fix the frustration solve the problem show the ability and the capability what can do and just go from there i think this also applies to people trying to do ai in their personal lives with personal agents like they're throwing everything under the sun at this thing but actually like if you can just get it to like summarize your emails every morning That's a win, right?
And then just layer things on bit by bit.
Don't try to solve everything at once.
And I think that's where you're going to start getting some value out of a lot of this technology.
That's my two cents on this as an answer to your question.
No, it's great advice.
starting with realistic expectations, iterating quickly.
It's really like what you said, doing small passes to add things one bit at a time.
And that also then means, you know, making sure that you understand the things that you're adding and that it's making sure that it is aligned with what you want and stopping, you know, when and realigning it when you need to.
It's kind of like the real craft of it is I've alluded it recently as like you put it on a potter's wheel and then you're shaping it.
And that comes down to how often.
are you running the workflow?
How often are you running the, how often are you encountering this agent or using this skill?
That's what's going to be your interval for like how, how much can you shape it?
And it's about, you know, bringing the right amount each time, not too much, or you're just going to make a little bit of a mess.
And it's been a really amazing Vincent to get inside your head about how you think about building OpenClaw, how you think others.
should think about collaborating via OpenClaw.
I think there's a lot of opportunities that you showed us today just for teams to get on the same page, to implement workflows that allow Y to live alongside the work that gets delivered, which I think is the holy grail for a lot of folks trying to be agentically enabled.
And I know you're constantly pushing out a lot of new research and blogs and discussions around what you're building.
And so do you have any you know, parting last bits for our listeners for where they can go to stay tuned for the latest for you or keep in mind for some upcoming things that you're excited about?
Yeah, I think just users should just kind of keep an eye out on announcements.
It could be on X, on Reddit or just even on our website as well.
But yeah, we're definitely going to start punching out some really interesting work over the next.
next few weeks and next few months that I think is going to get people really, really excited.
And if you haven't used OpenCore in a while and used it before and it wasn't the best experience, I'd say give it another shot.
It's a very different experience.
I think we're moving away from messaging channels.
We're moving away to this like native web app sort of ecosystem where it's like, wait, why do I need to be inside a Telegram?
I think it's good when I'm out drinking coffee with a mate and I'll send the agent something.
But otherwise, I want to be able to see my work in one place in this sort of like...
So we're kind of shifting away from terminals and other sort of things into like more purpose-built user interfaces.
One last thing I wanted to add, and we haven't had a chance to get into a demo given the time that we had, and maybe we can attach some screenshots or something to this later, but is Dashboard, for example, the OpenClaw web sort of interface that we're working from?
now has the ability, like you can have plugins that can change the entire user experience, but also your OpenClaw can do that as well.
So we've got one internally, for example, creates a newspaper for us every day of like what's changed on OpenClaw or things like that.
So you can basically like reimagine what your interface should look and feel like, and it will build that for you.
So there's people on the team that have been building some really interesting things around like, show me like my day that's coming up and like use visual blocks and things like that.
So we're kind of thinking about like this whole, smart home sort of like devices that you have kind of experience but the AI is sort of driving that for you based on what you want so and then we're thinking about how do we then bring this into like mobile devices and things like that so your agent is around you and able to kind of share that information with you.
with you and interact with you as well.
So I think there's going to be some interesting developments, but honestly, at the same time, who knows what's going to happen in two weeks or three weeks time in AI space.
There's always some interesting developments.
And for our team, especially, it's like how quickly can we react and implement that?
and sort of be on the edge with something like OpenClaw.
Yeah, that sounds really exciting.
Even what you described is almost like the building blocks of like a personal factory or a bigger factory.
This idea of being able to arrange the parts that you need and really thinking about owning.
the interface by which you interact with your data and your context of what matters to you.
I think that becomes the new fundamental builder responsibility of everyone who really leans in in the moment.
And it's been really exciting to see what that's going to look like.
And to our listeners, you know, we're going to include links in our show notes where you can go and check out all of this technology, including the latest features available in OpenClaw 2.0, where you can dive into more and get a closer look at exactly what Vincent is talking about.
And to those as well that were intrigued.
our conversation will include links to the foundation.
So that way you can get reacquainted with open cloth.
Maybe it's been a hot minute.
And if you listen to the conversation all the way to this point, then you clearly had a blast and you're a total, either Stevie, a gay nerd or an open call nerd or both.
So wherever you're listening or watching this, you should give us a thumbs up or subscribe and come find Vincent and I on socials.
Just come bother us about anything that we said today.
Take a side.
We don't care.
Just pick a side, join the conversation.
And we want to hear from you.
And thanks again for listening to Dev Interrupted.
And Vincent, thanks for joining us.
It was a joy chatting with you today.
Likewise.
Pleasure.
Thanks for having me on the show.