Stop Fearing Change: Protecting Mission-Critical Applications in a Modern Enterprise | Agents of Dev Ep. 10

Control. This is agent dev. I'm in position.

Copy that. Dev. Stand by for, go Standing by.

Hey, welcome everybody. You have joined another great episode of Agents of Dev, the podcast where myself, Mitch Ashley, and my co-host Brad Shimmin, both of us from the future room group, analysts in our respective areas, always talking about software development and what's happening, what we're working on, but also mostly what the vendor community is working on. Brad, good to see you again.

How's it going today? It's good to see you, Mitch, enjoying this glorious spring oh eight. We're a little ahead of that.

The presumed coming of spring in a month or two. Yep. Just doing good.

Spring wasn't very springy yet, so we'll have to wait for some more spring to arrive. It's a bit of an arctic blast, as Some would say. Oh my gosh.

That is for sure. That is for sure. I think we're getting some more, more snow this weekend or something they're talking about.

We'll see what happens. Nothing like what you've seen, of course. So that's crazy.

You know, this is, I think we should do a podcast about every other day, maybe going to every day, because it's just that freaking insane. I mean, there's that much stuff happening and, uh, you had reason, reason why I kind of set it up that way is you have a new segment you wanna introduce, um, which kind of Oh, I do speaks to this. So tell us about what you wanna do.

Yeah, we, we want to try to corra all of what's happening that we think is important and useful, uh, in the marketplace. And to do that, as Mitch says, we would be doing this every hour on the hour. Uh, but because we, we don't have the ability to do that, and we wouldn't test your patients with that.

We instead have decided to go with, with a new section we call the callouts. And this, this is a segment that will be at the beginning of our podcast where we'll just take a moment to talk about something that we see a vendor, uh, in the markets that we cover, uh, doing something we, we think is useful. And, uh, I'll, I'll, uh, I'll start us off if you don't mind, Mitch.

Great. Absolutely. Please do.

Okay. So, um, as some of you who follow the, the research practice that I, that I'm, uh, working in, uh, we're in, I, for example, am talking a lot about the semantic layer right now and the importance of that across the board, not just for BI tool, you know, tooling and data scientists, but for everybody. Um, we have been following this idea of, you know, the semantic layer as being needing to, and it did start within BI solutions as a means of saying, what does this business term mean and what does it mean to different people?

And that's important because you kind of have to have consensus. And as we talk about on our podcast, you know, having that ability to have that ground truth of meaning, uh, is critical for h agentic development. So, uh, I I say that as a starting point to give a bit of a clap for, for Microsoft.

They just released two ga, uh, the, uh, uh, a sort of evolution of an existing tool they built a while back called Semantic Link. This was built basically to, you know, sit inside of Jupyter Notebooks and allow them to Yeah, my, my favorite place. Uh, and, and allow them to, to basically, you know, build and work with, uh, these semantic models or semantic layers, I should say.

Uh, and coordinate with Power bi. So you didn't have to like, treat them as two separate environments. Well, with this update, they've actually now sort of conj, not sort of, they have conjoined this within their entire fabric, uh, platform.

So whether I'm a data scientist, a bi professional, or a data engineer, or a software engineer or an agent, uh, I, I will be able to, you know, work with these semantic models and change them to make, you know, uh, create new models to create new data products, uh, if you will. And to have those available to, and reflect it across, you know, the, the Power BI group, the fabric group, and my own group that I'm working in if I'm building out software. So, uh, I think that's terrific.

I'm, I'm glad that Microsoft is evolving the technology that they have and not just trying to reinvent it. Kinda what's old is new again. Hey, there's nothing wrong with that.

You know, Hey, take what, what's working and extend it into this new environment. Yeah, We we're gonna talk about that a lot today, actually. We're, Yeah, the theme coming on, um, I, I have a couple, one tiny one, a tiny, but one short one, um, Atlassian, uh, ga there, I think it was into the last month or first part of this month, their n CCP server for robo agents.

Basically, they've opened up through robo, whether it's a queryable interface or for the agents, all the information that's in Atlassian's applications, primarily Confluence source. A lot of it's what we use. So it can be for operations, it can be for development.

Um, and of course I think that would benefit from a semantic layer, something they, that they've talked a little bit about. Um, but I think the, the thing of it is, it kind of reminds me of this, I was on this project called Project Alexandria, and it was all about the, the library of Alexandria. Did you have a library?

Yes. Library. No, no.

We were gonna assemble it. Well, it didn't quite work out. But anyway, um, you know, it, it's kind of what, what we're doing across the board.

You know, Google has made available, uh, all their documentation to agents to be able to use for technical documentation, user information, data for the business. So, huge trend, I'm sure, you know, not news to you. Uh, but that seems to be more and more sort of a cost of doing businesses.

Opening that up. Yeah. The second one, just to not take up too much air oxygen outta the room is, uh, kind of, kind of hit the airwaves pretty big, uh, is entire this new company that, uh, was formed by the CEO, former CEO of GitHub, uh, Thomas Donkey.

And everybody was kind of wondering what did, what, what did he lead to, to do? You were a little Worried when he left. Yeah.

We know. Like, what's gonna happen. Well, guess what, again, something you'll love, and I think all of us appreciate, is you think about software development and all this interaction we do with models to create it, and what, what gets lost?

What's the exhaust? It's all the interaction, the prompts and everything that we talk with, whether it's through scripts or, or, you know, predefined prompts or interactively, that stuff just kind of falls by the wayside. And we have an artifact, but we don't quite know how it got created or why it got created.

And that's essentially what he started out with, uh, this entire, uh, a open source project called checkpoints, which basically you connect into your GitHub and it tracks everything that you do in terms of capturing. You have to install something locally too, uh, which your prompts are to whichever mo whatever model that you use. And a, it's a great idea.

B, that's just the tip of the iceberg. I call it the plumbing of what he really says he wants to do. And I, and I believe this is true also, is if we are gonna generate massive amounts of code, just like we can't scan it all for vulnerabilities, we can't, as humans sit there and review all of it.

That's just impossible. We don't even know why we're reviewing what we're reviewing. So this kind of semantic layer is not just for humans, it's also for agents.

So it knows what got created, why, how that ties back to what session, who, what the requirements were when we defined that spec or whatever was that drove it, drove it, or the idea came up the internet, what, whatever it was. Now we've got some traceability back to that, but I think it's a lot more, I think the code windows will of our ides will eventually fold into agent interfaces. I could described it as, you know, more, more like StarCraft kind of game interface of managing all the agents, doing all these, Yes, you have me at StarCraft.

I hope it's like that. Something like that. So anyway, good stuff.

I mean, a again, center your world semantic data knowledge, it's, uh, that's the fuel of AI and what we're doing. Indeed. It is.

Yeah. And it's actually funny. Um, this topic is my, uh, relates to what I wanna talk about in the drop at the end of our show, uh, with, with, uh, agent context to Decoherence.

So, uh, yes. Yeah, it is, it is timely to me. Very good.

Well, let's, let's get into our, our main section or our main topic. We wanted to talk about anthropic, uh, published a, I didn't wanna call it experiment. I didn't wanna call it research, even though it was a research, a company blog post.

We could call it marketing, we can call it whatever. But it's a, by a technical person And a repo. And a repo.

Don't forget there is Oh yeah. Repo. It must be legit.

Right? That is our measure. Got a T-shirt, we got a repo.

Okay, we got a blog post, we're legit. We got a company. Let's rock and roll.

So anyway, um, they did this, I'll say, I'll say the word experiment. It's not experiments experi in a classic science. I mean, it's not independently tested.

It's not isolated. Yeah. It's, you know, they try, they said, what, what could we do?

Let's solve a really hard problem. Let's pick something like, could we create a c compiler? Now, that's interesting problem, because it isn't an unknown unknown, and we know what a c compiler does.

Right? Right. It's very, been around for a long time.

It's a herculean task. Is it? Not Something, but it is a massive task.

A human, Human endeavor. Like going to the moon, building a compiler is not that far apart, Except this has been done. This is like going back to the moon, right?

Oh, yeah. Yeah. Right, right.

And the, and the, the the, I mean, I could cite the numbers of what, how much code was generated. You know, it was a massive amount of code. And they used long running, uh, multiple agents that they coordinated through Yeah.

Through Quad. Just 16, no, thousands of agents. Swarms, as we've been told, would happen.

That's more than I can manage. 'cause I only have 10 fingers, and my toe toes are too far down to, to use to count, So, right, right. Yeah.

Um, interest entering, uh, interesting territory there. But one of the real, real telling things was how much human and how much human direction and intervention and engagement it took to get there. So it wasn't a, here's a really well-defined spec, which you could do with a C compiler.

Go for it. Go build it. Now.

It took a lot of direction and, and not, that's not a negative, it's just the state of where we are of you still. This is a guided process. You still have to engineer a solution with ai.

It's not gonna totally do itself. Yeah. Maybe it can create the snake game.

So what, that's not, that's not interesting Problem. A problem. We have a million snake games running around on the Internet.

Indeed. Um, but from a social people engineering perspective, I'm curious what your reaction was to that thought experiment, whatever you wanna call it. I, I, I think that they know how to market quite well.

They, they understand the things that we care about. The, you know, practitioners care about practice, things in practice, I should say. And so what we're getting from them is, in this case, actually something that's a long running experiment.

This isn't just a, I decided last weekend to do it as we had with Claude Cowork. Yeah. Over The weekend.

We had a, we had a great discussion about that. But this is different. This is something that this fellow who's been famous for doing a lot of adversarial red teaming against AI, has been running as a long standing experiment to see where Claude is in terms of being able to, to handle these very complex, long running tasks.

And what I found interesting about this is, and as you just said, uh, it is very much a human led endeavor. And, and, you know, we hit at fu you know, uh, basically when we did our 2026 predictions, we said, you know, know, we feel, feel that for most endeavors, uh, for me, for example, data professionals or data engineers and such, that they're not going away. They're instead transitioning into becoming what I would term as an AI shepherd, uh, a individual who has deep domain expertise, great skill as you might see with like a business analyst to define problems.

And to, in what we're seeing in this case, is to create a sort of set of tests and a harness to run the test within and the development work itself, and to sort of manage that using the tool chains that we know so well, you know, vis-a-vis gets in this case. And mm-hmm. It, it was, it was fascinating to me that, um, what it, what it showed us, or it showed me anyway, was that yes, it is still human led, but the, the thing that's interesting is that it's human led, uh, for agents.

Meaning the, the person isn't trying to do things the way humans did it. The person is trying to create this infrastructure that does things the way agents do them. And so, for, for example, but to that point, Yeah.

Able give an example, because they didn't tell it how to do things. They said, do the following, and it figured it out. Right.

Well, you're right. But what they found, for example, is that agents all tackled the same problem repeatedly over and over again. You did.

Um, and, and so to, you know, sort of do two things. First was to give them, uh, the ability to say, I, I called shotgun. I got dibs.

They built a locking, he built a locking mechanism. It's just a very simple text file. It's like a library card to check in and out the books.

And the second thing, which is really, you know, a testament to how all of our technology is built upon the shoulders of giants and the shoulders of the giants that came before them. And that is to use GCC itself mm-hmm. As a reference point to basically say, okay, the things we actually need to worry about that are broken, you know, this is what you need to focus on.

The rest, forget about it because it's compiling under GCC. So don't try to fix that. Uh, that's like, did we do that before for engineering?

I don't think so. That's a different way of looking at it. You know, it, it's, there, there is an approach.

It's a very unique situation there with, with ecc, but there is approach of, there's some things that I'm gonna assume, right? I'm not gonna worry about the job runtime engine. Right.

That that's pretty much a given. I'm gonna worry about these things. Um, but listen, let's don't boil the ocean, ocean and try to go reinvent a bunch of stuff.

I'll use open source, I'll do whatever that that kind of goes out the, the window with ai, because AI would love to generate a Java runtime engine For you. It would prefer to Yes. Prefer To.

You know, the, the thought I had about the agents kind of doing multiple things is I think they forgot to introduce the agent to this thing called the Kanban board in sprints, where everybody goes to the board and says, I'll take that, that feature, that story, and I'll implement that. That's the shotgun version of it. So we kinda need to teach agents that, you know, there is a way of managing your work, um, which I think is a, a whole big area of, it isn't just orchestrating, defining linear process.

It's just like you have to teach ai how is it gonna work with it with itself? And sometimes those airlines aren't gonna be the same vendors. It's gonna be other, other systems, Right.

And with interdependencies, you know, some in real time, some asynchronous, and you need, you know, tooling to, to actually manage that flow, uh, in parallel. And Kanban boards are a great way to do that. And is it not a tool that's available on, on GI now from GitHub now, uh, to do that?

And, and so I, I would presume that as we go forward, whether it's, you know, the CEO creating his own system, or it's gi you know, GitHub, uh, evolving, uh, on its own or Code Berg or all of these repository management platforms, they're all going to be, I think, tooling for both the human and the agents or the ai, uh, as two, you know, combined, but some somewhat different tracks. Mm-hmm. Well, you know, I imagine I was, I wasn't joking when I said the UI of the future.

I hope it looks like StarCraft, but imagine in that UI of the future, it's, it's some souped up version of a Kanban board that you're watching as AI is doing the work, picking off things, going through the process. This is, this is one, this is a human one where Mitch is gonna do that. Whatever that process For later looks like.

Yes, exactly. Don't, don't, don't go down that, that alley. That's a dark alley.

Let's go back here. You're gonna get mugged. Don't go down there.

Yeah. Yeah. There's danger down there.

You don't wanna mess. Um, but something like that where, where you can interactively as well as autonomously, see what's happening, and also direct where it needs direct in real time. And then take, if you, if you take the, uh, kind of the, uh, entire idea, all the semantics of what happened and why can then be applied to learning to how to work better, how to perform that work better.

We started here and we ended up with this. How did we get from A to Z? Well, it's actually Q why did we end up at Q?

We were going for Z. Um, but there, there's so much to take. And we try to learn that as humans, we can't learn at all, but we have to help the eye.

But that is something that we humans, I think are very unique at. And that is being able to, to pick out the wheat from the chaff, the, to split the two, I should say, and to find what's most important in looking at a lot of information. And it's ironic a bit that, you know, as, as LLMs are much better than humans at scanning and keeping in context, you know, the much more information.

And yet they really suck at trying to pick out what's important. And, and I think with this, you know, uh, anthropic example here with a compiler that we, you know, he discovered that as a big problem. And so he changed the tooling so that it wasn't just publishing the entirety of a, um, an error, uh, or even the log file.

It was just, okay, let's build another, I call them epicycles, uh, because for planets mm-hmm. Planetary motions, how do you explain retrograde? You just draw into their circle this Escape philosophy in retrograde.

Exactly. It'll all work out. Newton would be happy.

Um, so it, it's, it's basically saying that, okay, we need to have another layer that, that sort of modifies the app, the artifacts that are created as a part of this process of building software so that we can better work with those assets in a more efficient and reliable and consistent manner. And that's what I think this, this points out with that example, uh, that he discovered, is that, okay, you can't just throw everything in a 1 million context window and and hope for the best Or 16 of them. Yes.

Right. To 16 million tokens running around simultaneously. You know, so this leads me, I wanted to get your opinion about this.

You know, it's only February, right? I remember we're into the second month of the year, but looking back, last year very much was a big theme was MCP and A two A and a ccp, and a lot of, a lot of open standards if you open source open standards. Yeah.

There, there's still very much that vibe that's happening concurrently. But I think we've advanced to the next phase of evolution of how we're creating software, but we're seeing different kinds of innova innovation and levels of innovation that standalone still. So for example, you know how Claude manages work, right?

It's not tied into some open standard that everybody's agreed how to do that, right? Everybody has said, Microsoft, here's our agent hub GitHub, and, uh, you can bring your agents to our environment, right? So we're very much creating these ecosystems of work and development.

Not saying they won't be able to work together, but we haven't come to the Oh wow. But we're all sitting at the same lunch table at school. We should like talk to each other.

Let's decide how we're working On the same Problem, how to change this all of us. Yeah. But we're kind of evolving to, well, will we suddenly all go off?

And, you know, speaking of your planetary, well, we all kind of, you know, lose lose our, lose gravity and be thrown off into the galaxy or, or what's gonna bring things back together. Certainly customers will, but I'm just thinking through those kinds of constraints because when we change constraints, things change drastically. They do, don't they?

And I, I think, you know, just as we've discovered with LLMs, they, they, you know, whenever their training cutoff is they really like to run home to mama. And, uh, we as humans do the same. And I think, think we saw that in this experiment from Anthropic, for example, and that, you know, he could have, you know, set up some new MCP to, to sort of do be that harness, but instead the harness is just a freaking bash file.

You know, that, that just runs till, you know, Ralph Ralph's it till, uh, it it's done. And um, then that is a verb. Now, by the way, yes, Ralph, that was something different in college, but in, in ai, railing is a different Thing.

Yes. And right there, there are different pronunciations too you need to stay far away from. Um, but, but the, the, the point is that I think, you know, this is a testament to the whole canoe Linux, you know, mentality or the, as we currently would call it, the suckus mentality, which I mm-hmm.

I'm not saying that jokingly, that's an actual thing. Mm-hmm. Um, that you build a piece of software that does one thing really well, and we see that with tooling like Arsy and, you know, show me a server that's running the internet that is not using our sync, you Know?

Oh yeah. Just, Just for, you know, backups and synchrony synchronizing file changes, and why would we reinvent that wheel? Why would you go out and, you know, create a standard to, to solve a problem that, you know, our engineering minds 40 some odd years ago solved.

Mm-hmm. And it's still, you know, humming along today just as, just kinda like the semantic model with Microsoft, for example, earlier. Right.

Like, it worked well for that list and, you know, apply it to this problem our sink has lived on and on and on. Yep. Yep.

It's like building a space station to get to the moon and then use the moon to get to Mars. We'll have to do a, we'll have to do an episode on space. Maybe we can get, we can get, um, it's his name Tyson, the guy at the Planet.

Oh yes. Neil Degra Tyson. Yeah.

The grass. Tyson, I'm sure. Yeah.

He, he follows us. I'm, I'm confident. I I bet there's an email waiting for me.

He's asking if it can be. We'll see, we'll see. Anyway, well, I think we've probably done enough damage on these topics so we can, we can go to our, um, our last segment for the podcast episode called The Drop.

Alright. My Drop this week. Yes, yes.

Sorry, Mitch, go Ahead. Um, I, I'm, I'm going first because, uh, I'm, I'm really frustrated by this and it is this idea of agent context to Decoherence and I, I've mentioned, I think when we've chatted before and in the projects I'm working on, I feel that the larger the code base becomes, the, the more likely you are to fall off what I call the edge of the plateau, the of happiness that you're on. Mm-hmm.

You know, where, where when you make a change, the change is enacted and it, and it works the way it does and it doesn't create any other problems or downstream issues you're unaware of. And, um, I, I still feel that is a big thing, but what really, you know, kind of ca captured my attention recently is just how that applies to all the artifacts that we work with, not not just the code base. And so, you know, I, I think as, as the, you know, XCEO of, of Git is showing us that we need some smart systems for managing those artifacts.

And for me, one that I, you know, didn't, didn't really pay attention to because I, it's just so natural to say, make a memory, you know, in working in whatever CLI you're working in age agent CLI to say, make a memory of this so you're remember for later. And what I ended up doing was, uh, something called context poisoning. I poisoned myself, unbeknownst to myself, uh, about how to do, uh, GI committing, you know, do GI commits.

'cause I had made a skill for that and uh, then made a memory on how to implement it for different projects. Um, so I could use it consistently across projects. 'cause that's what you do because we're lazy, uh, Make file kind of, That is exactly it, right?

That's the point of that. Um, and, and so, uh, I, I didn't, so lurking hidden within not just the skill I made, but the, the memory, uh, so the agents md or whatever you guys are using, uh, what were was an instruction that the model was, was like, oh, wait a minute, Brad said to do this in the skill for committing. But earlier you said in the, the broader, you know, agent MD file that this is what I need to do.

And it went all Hal 9,000 on me. Uh, that was, that was not The repository you're looking for. I'm sorry, Dave.

I can't, I can't open that repository. Yeah. So, so, um, so how did you poison it?

You have to tell us. You, you can't leave us hanging. What did you do?

Did you drop a little, you know, arsenic in there and like, oops, that wasn't supposed to go. I literally literal Pulled, pulled out the grip or frep and uh, just found the, what I was, you know, the poison pill and removed the pill. Yeah.

Well, Unless you knew what the source was, you found what it was like because Right. Because they're only, I only have Like five things combined. Suddenly it's hallucinating, Right?

Yeah, exactly. One something. You know, these are hierarchical systems and so what you're working across different projects, you have your root agent file, uh, or if we're, if we're in open claw mode, it would be your soul file.

Then you have Yeah. Pro, you know, files for each of the projects you're working on. Um, and I only had like four of 'em, but, uh, it, it was, you know, a testament to the, to the need to, to curate and maintain that memory that your models have along with your skills, along with your extensions in concerts.

Because the minute you introduce conflicting truths within that, the models are, are gonna get confused 'cause that's how they work. Yep. Very much so.

Well, cool. That, that's interesting problem. And I think all of us are experiencing it, you know, multiple times, sometimes more severe than others, right?

Than, Yep. Um, so yeah, this wasn't in an rm, you know, removal file system. Yeah.

That was, it was not that. Make sure you have your permission set appropriately and no, pseudo is not a good thing to answer. The, um, pseudo, sorry.

Um, Sudoku, no different thing. Uh, so my, my drop is, and this is coming your way by the way, so this is a little bit of a heads up that, uh, you're gonna get to repair, review this for me, but I've been working on a kind of my layer cake framework of agent controls, uh, frameworks, agent control planes. What do we mean by that?

Because it may not be end up being called that at some point, but that's a lot of what are the innovations that's happening around, whether it's managing agents, whether it's directing work of agents, whether it's security guardrails, compliance agent behavior, observability. Yep. And I think one of the very cool things that's happening is a lot of things we would've done later, like security and like observability and all that, because we're in this age of AI can be easily integrated into the development process, to the kind of interim, it's not quite ready for primetime into production.

So a lot of things are combining into this one phase of development in this one quote unquote platform. So I've got a reference framework, um, I'll run it by you and you can rip it apart and say like that, get rid of that. This is stupid.

I, that's kind of, okay. This will make it better. And, uh, I appreciate your input.

Yeah, I'd love, love that. And you're right, the the whole control playing idea, so critical. Um, and more, more so than it was, we used to have a lot of control planes, right.

You know, so like in the ml lops space, you, you know, you're always striving for a control plane that, that wrapped those different, you know, guards and, and rails and, and controls and audits into, you know, four specific areas of interest. And right now, the systems that we're building, I, I think are, are, you know, much more multifaceted than that. Mm-hmm.

And, and you can no longer think of a control plane as being specific to a given area of work. It has to be specific to the solution itself. Mm-hmm.

And broadly the company itself. Yeah. It's not like one business domain.

Like we're developing machine learning algorithms And data Exactly. Following. That's the data scientist problem they'll deal with Yeah.

Let them solve that. But you're, you're, you're creating it for a system that's also evolving, right? Um, yeah.

So how agents work and what they do and what the protocols and what the open standards are, the work we're gonna have to do could be very different in six, 12 months or maybe a month from now. So it's, it's, uh, it's an interesting problem to solve, but I think there's some really good ideas out there, what vendors are doing. Agreed.

And a lot, a lot of them built back in 1982 in, in, uh, cultural uh, terms. But yeah, I mean the, the old ideas as we were saying are never truly old. They, they become new again.

You know, we, we either like engineer our way into something new with them, or we find ourselves with, you know, enough compute horsepower, for example, to do the things we couldn't do before and mm-hmm. So you can never turn your back on the past. You, you have to honor it.

You have to look to the past to create the future. Look, you can see so many examples, think of line analytics, analytics and early work has spawned a whole set of great things. So, so often the re there was a reason why those were good things, good decisions made back then maybe constraints and, and situations have changed Totally.

But there's still things we can take from it, maybe verbatim, maybe not of those good things. Yeah. Database management systems as an example, that those were like, the reason why they were created was to get around the constraints of, of performance and scale and the lessons we learned about things like, you know, acid compliance to, to be able to make sure that when you write something it gets written.

You know, little things like that, um, are still true today. I think we should still be using i a files, but that's a whole nother topic. So anyway.

No, no, I'm with you on the tv. Wow. Yeah.

Throwing one at you there. Okay. And re-indexing everything all the time.

Indeed. Well, we better, we better stop before we get down one of those dark alleys that we can't get out. So, um, look forward to, uh, more great things.

Please check out Brad's work that he's doing at rum. You go to Rum Group bradman, Correct. And everything you know, that we do that's freely available.

You can download there. There's some things that are part of our internal or our client system that we use called the, you know, future Intelligence system. Um, there's a lot of good things there of course, too, but please check it out.

Mine is the same, you know, group com slash mitch Ashley, and you know, we're kind of publishing like madman. It's a lot, so many things going on we wanna talk about and not just report the news, but understand. No, we're trying to think about 'em.

Why is it news? Right, right, Right. Put them in context, wrap, wrap them in our, our decades of cynicism, um, and try to come up with something helpful.

That's what we try to do. Exactly. Well thank you so much everybody for listening, watching, um, and we, we appreciate comments with appreciate folks that have reached out to us and we hope you'll of course share this with your friends and follow and do all that we can.

We have folks that we're gonna be bringing on, we're talking different people. Yes. Brad and I have gotta think a good enough rhythm going.

We could, uh, you know, keep ourselves on track with a third party, third party involved. I'm not, I'm not sure about that, but I, I think I'm willing to experiment. Hope Springs eternal indeed.

So we'll doing that. So thanks again for, uh, for being part of Agents of Dev. We appreciate being part of the community with everybody Control.

This is agent dev. I'm in position Copy dev. Go standing by.