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The information economy as a rod. The world is teeming with innovation as new business models reinvent.
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Inside Analysis is your source of information and insights about how to make the most of this exciting new era. Learn more at inside analysis dot comsideanalysis dot com. And now here's your host, Eric Kavanaugh.
Hello, and welcome to Inside Analysis. I'm your host and Yell LeBlanc. On this episode, Eric Kavanaugh is interviewing Alex Galago, who's the CEO of Red Panda Data. Together, they will talk through the new landscape of LMS and how the innovation of AI is affecting companies all around. Stay tuned, you're listening to inside Analysis.
I'd like to dig deeper into Red Panda.
I mean, I know you've you've basically rewritten the whole platform to do streaming, to do it efficiently, because Java is not very efficient, right, So you said, well, just go to the drawing board and hack this thing out.
How long did it take you to do that?
Beca In many ways it feels.
It felt to me like it was a natural evolution of, you know, decades of work. While I was in school, I took a job at a Forks trading company and I really got sort of introduced to a bunch of low latency things and how do markets work? And I ended up writing some some trading algorithms in a weird language called MQL four. It's like this weird obscure language, kind of Jenkie, to be honest, very Python like, but
not at school. And then I wrote it at a bunch of trading apps on this thing called pips, and a pip is like whatever, like one hundred dollars or something like that, just like some really large spread. But you want to deal in terms of bacurns. This long story sort, that's where I really started to kind of lean into things that I found really fun, which were like low latency, high performance things. And so then I
went to work for an Attech. I guess when I graduated, I worked in an embedded database and I worked on the greed this is for the BlackBerry phone. We wrote this like c parsing code where we were pulling this like three dimensional database into a series of doubles because it was really efficient to encode for I think open VMS, and so we wrote the color coding on the S
and P five hundred table with like dynamic rendering. That was really cool and so long story short, when I think about the evolution of red Panda, it felt to me like the natural evolution of the ideas that I had worked on, which went to be an Attech in New York for a company called Yildmo, and then for a stream processing company that I wrote the tech for in twenty fourteen through twenty sixteen that I sold to Akamai. And so anyways, in many ways, it was like, why
is the world so complicated? Why does it have to be so hard? And everyone that has been on call and you know, usually your page I goes up at three am. I don't know why, but it's like somewhere around that time. It's like though, it's when you're like writing your deep sleep and you get paid and then you wake up and you're like, I don't understand why this is so complicated.
And so.
It was like, well, why couldn't we do better? Like what's what's the mechanical reason? Like, okay, let's remove all the hype. I just want to understand fundamentally, what is the job that this software is doing that is so
hard that requires it to be so complicated. It turned out not to be that difficult, frankly, and so I wrote something from a scratch really for me, and I left Akamai I think December twenty eighteen somewhere around there, like basically, and I started writing Red Panda in January. But I would say I had a like a pretty clear goal and so it was just a matter of
writing the code. And so that part didn't take that long, maybe like three months to have a prototype, a couple of months to have something well that that works interesting.
And of course the world has just sped up in all directions around us. Now, so we're all kind of riding this new wave. And you know, when I think about streaming, I've always i mean from the earliest days, I was like, oh my goodness, if you do this right, you can just you can supersede ancient architectures or what should be viewed as ancient architectures. But you have to
do the whole thing. It's like when you try to fuse these two worlds together, that's where the impedance mismatch kicks into high gear and you've got to figure out some way.
I mean, you know, I.
Heard stories of at Vertica where they were taking all kinds of streaming dages just like filling it out into you know, a traditional database, and you know it's a column oriented database of course, but still it's like, Okay, you know, do you need all that? Like is that really really necessary? And the short answer, of course is no,
not really. But you know, for for like net new use cases, I think you guys would be a dream, right, But in traditional architectures do you find that's kind of a hindrance is that the behemistic are the big fortune two thousand companies They just you know, they're like, oh my god, like can we even handle this is that? Is that still a hurdle or do you see more people figuring it out and just architecting around it.
Yeah, good question. So on for that that's really our sweet spot.
I know it's counterintuitive because it's like, Okay, well here's this you know, startup. But the brands that we work with are like, you know, probably drove you to work today. I guess if you work from home, maybe not, but for those of you that commute to work, like you know, whether it's like the largest electric car company, the largest city and largest bank in the US, largest like iced B in Europe, one of the largest banks in Australia, whatever.
When you look at the brands that we tend to work with there, they tend to be this this fortune two thousand and I think fundamentally what happened is twofold one AI has definitely accelerated the pace at which people are wanting to move towards a more interactive architectures. That's why too people realize that really the log is the source of truth in an architecture and databases are caches.
And I think this wasn't my idea was it was popularized by Amazon They're like the log is the source of truth and everything else is just the cash and so uh and I guess you know, third where when we started talking about Iceberg and so on, people are like, finally I get finally all the blocks. Uh you know, queny when you put the final piece on your lego,
it's just like it looks beautiful and it works. Is when you can start to glue analytics, which is the look looking behind the present, which is really what Redpana is with with Apachi Iceberg as the glue, and then the future with autonomous decision making with with agents by carrying context. I was like, now I think people understand the timeline horizon of how an application works, and so
we're seeing the opposite. We're seeing actually acceleration in the fortune two thousands or five thousand really more more then I sell early on. And I don't know if it's a function of brand. Maybe we have better marketing, Like it's kind of hard to tell, and I don't want to like a you know, correlate and or incorrect assumptions here.
And so I think fundamentally where if you were to ask me, what's like your gut, I think is the world is thinking about applications differently in large because of AI and in that world, red Panda place a critical role. And I'm not sure, you know, in the context of our by C for example, there there's like a.
You know, many alternatives.
You know, you just reminded me of something. And I haven't read too much about this. And We've talked about streaming for years. I've talked about streaming for many, many years.
There's MQ series.
There's all kinds of things in the past that were streaming, right, It's like little message cues basically MQ series all kinds of different message cues, which is kind of what's streaming.
Is that that's the category that's in.
But to your point, there is a maturation now in the environment, and I'm thinking to myself, this is a real time application architecture, right. That's what it really boils down to is like, guys, look, you want an application that does something. What is the fuel that's going to
use to do that something. Is it going to be a traditional static database which is the traditional way of doing things, in which case, guess what, that database will slow you down, Like sooner or later, it's going to slow you down either because it's going slowly, or things that attached to it are going slowly. But the point is that this just ever ever growing ballast. If you kind of look at it in a certain way, do you really want that? And I think that's your answer
is no, not really. What you want is for your apps to use real time data that is live right now, that is interactive with the users or the partners or whatever. And that's what you're enabling, right It's a real time application architecture.
Yeah, and so I think two comments.
The first comment is that I think the reason why database is not enough is because you lose data. And so set another way, you need the highest fidelity of the data to be able to reconstruct a different materialization of your data. And so if your data it's in a relational database, let me give you the example is average sales price and so or like the price of the last ticker symbol and ice that that's probably a
better example. The amount of data that is flown with every tick of that symbol as it's being traded is tremendous. But when you ask a database, it gives you one killer by the data vice like this is the company name, this is the ticker symbol whatever. This is some price, but the amount of input to the database could be a gigaby per second. And so what happens is you lose data, right like fundamentally it is it is a lossy compression algorithm almost right, like you're sending your data,
but you're only querying the last point in time. And so that's what the databases are. And so I think it is limiting less from like particular you know, I think through put databases have gotten really good at throughput costly, you know, so maybe the other limit is dollars, but they I think that now modern databases are pretty good at handling the throughput latency not not so good. And so but it's more fundamentally it's about like you're losing
access to the real data. And so what happens is it forces the architect and engineer to have like, uh, computed all of the ways in which they wanted their data before they write an application. And what the log gives you is freedom. You're like, well, I want to store it in a really cheap storage. They's say S three or something like that compatible and then today is stick pulling, then tomorrow could be whatever. Elastic could be whatever it is, and so that freedom is just key
I think. For that's where I see like the biggest pillar from an architectural perspective.
That's very interesting.
And then for AI thought that changes a little bit too. But that's about way for a second.
Yeah, what then what do you mean by that?
So Okay, Historically, I think we talked about the challenges of picking a database. Yes, in old school databases, perhaps scale, uh you know, on throughput and latency those for limitation. In modern cloud databases, I think throughput is okay. Latency is still not great and so maybe for some use cases does tend it to be. But we talked about like the architectural primitives that you get when you adopt the law as the source of truth.
What happens is when.
I'm going to define an agent as like an object with an L l M right, and so for simplicity and when you have these things, uh, what I guess the future is going to be about this autonomous decision making. And so if you look at a timeline t and you say analytics stuff like data breaks and so on. They were all about looking at the past, help me do my job better. Where during best sellers, what region is the best producing region, what product is the best
producing product? What should be my average sales price?
Right?
Like, help me do what I do better by looking at the past. That's analytics. Red Panda has been classically and we'll talk about NX about like being the best operational system in the world. Right, So if you're running your system and you depend let's say you're trying to protect white house that go from getting taken down by North Korea bots or something like that, you need to know the movement of IPAs around the world, or maybe you're trying to track fraud detection or whatever. So that's
the present. That's the operational sense. The future is about carrying the context of the past and presents so that this code can make autonomous decision for you. And so let me let me can now loop all the timelines past, present and future. And so the past and present are going to be bridged by technologies like apati Iceberg, which is a way to have a zero shot integrations with all the query systems, and so that that's an important tig piece and streaming engines will play a second. So
that's where streaming engines play super critical role. Is just exposing the data in a way that is accessible for many many tools, right doctor B ClickHouse, right like red pand or whatever Pinot trino U. Basically just about every data bas is going to speak Iceberg. So that's that's a critical road for streaming engines. And then for the future, it's about carrying context for the agents to do their job.
And so let's say that the job of an agent is to determine yes or no to a credit card transaction or a credit or a crediting period or whatever you need to give it cont and so think of a self driving car, right, Like you need to carry the context, either like the images or the previous five transactions or some sort of windows so that the agent can make a decision. So as part of the prompt, you can tell the agent, Hey, is this or is this not a credit card transaction?
These are Alex's last five.
Credit card transactions and he just bought a bagel in San Francisco. So if it gets transacted in the UK, like, obviously market has fraud, and give me the explanation so I can render it on the screen. Right, And so I think that that's why streaming engines are seeing in acceleration, one as a glue to analytics and the present with Iceberg, and then second as a way to carry context for this autonomous decision making.
So I'm trying to processes my brain, right. So when you have, like with cough gets topics, right, you have topics that are streaming and you can choose which ones you pull together in any given point in time as I recall, and that be comes the fabric or the context that you're talking about.
So here you're.
Talking about getting context from log files basically from systems that are important to this particular potential transaction. And what you're talking about is allowing the agent to be a much more adaptive and real time semi autonomous entity that will absorb context and just make a decision quickly. Yeah, Like that's the idea, is that? So you've in a sense, you've deconstructed the traditional data flow that goes into an
application which makes a decision. Because to your point, like all the Stata warehousing, that's my background, it's all the Stata warehousing stuff, right, you do ELT and et and all this stuff.
You get into this.
Big central repository, which we did because they figured out that you couldn't really query an ERP It's not what it was designed for. And besides, what you want to do is know how all this stuff relates to each other. That's where the magic is, right, is how all these different pieces of data relate. And so with red Panda, what you've done is expedited the process of feeding the important bits from important systems into a real time context, which the agents can then grasp as needed.
Is that right?
So it's cool about red Panda From an engineer perspective, it's basically most of our code is in the open, uh. And so we will link an example here of what I mean, like a simple concise yamal file so people can just like see the whole thing put together and it becomes like really becomes something that they can, like they can run on their laptop.
But that's exactly right. And so.
First of all, I think it's worth it just to share like maybe a little bit of redpan So we did start red pandas is like you know, high performance storage engine. And the idea is like if we adapt to how.
The hardware is. Right, so.
Most most software just operates on like you know, a tremendous amounts of layers of abstraction, and you'll loose performance with every layer because you're generalizing things. And so when I started a red panel's like, what if we just adapt to how hardware works, and if you go all the way down to even like core to core coherency protocols, so you have you know core they say L zero or you know zero to one on a dual core system,
single socket, right, so single motherboard. We don't have to complicate the picture with multiple sockets on a motherboard, but one motherboard you have one on one chip and the chip has two dual core right when you go down there, when you're talking about memory coherency protocol, it's all message passing.
And the insight here from hardware manufacturing people is that you could do a bunch of useful work if you don't block, right, And so if you adopt a similar software architecture where it's all message passing, you can yield at points of blocking and then you can let the CPU do a lot more useful work.
And so that's really interesting. Folks, don't touch it. That will be right back.
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Welcome back to inside Analysis. Here's your host, Eric tabanac.
Red pand that runs on a network of ny square single producer, single consumer, lock free cues that acts as mailboxes for every core. And you can assume that every core is like an actor system on a super relatency network. And so when you embrace that, by the way, you get a simple concurrency I guess software structure and a simple parallelism uh deployment and so anyways, performance and this is like one of my favorite topics to talk about.
I want to stop us here because this could be a two hour call where we like dig super deep into the mechanics of.
The This is so interesting for lots of different ways.
I mean, I've been around this business a lot of time, so I learned from all sorts of different vendors about different things they're doing, and it's always very interesting. But in the recent past, meaning six seven months ago, I ran into a company called hammer Space.
Are you familiar with hammer Space that I mentioned those guys.
You need to look into these guys, I mean, because they are thinking in a very similar way that you're thinking. They went out what they do. They're a parallel file system. So this is big in the HPC world for life sciences and training models. They're actually training a LAMA two and LAMA three, so they're being used to deliver the data to the CPU, and they did something that was
so damn clever it blew my mind. He talked about how in the average AI training architecture, data has to make eleven hops to go from where it is to the GPU. It's like controller nodes and different things like that in the storage environment, the network, all these different steps along the way, but still that's eleven freaking hops.
So they talked.
About how I think it's vast to cut off one hop and cumulo cut off two hops.
Maybe, but these guys.
Were able to figure out by affinitizing the processing to where the GPU server, which has built in flash memory that often gets ignored. They call that stranded capacity basically because a lot of times companies will want to just have all their governance protocols and security around the storage array and they don't want to have to try to figure that out on the GPU array as well, right,
so it's these two separate worlds. Well, they figured out how to affinitize that to where when the processor knows that the data is on this machine, Well, I don't even have to go to the network. I don't have to go to a storage around Look anywhere is right here.
So they've adopted done to four hops from eleven. And if we're talking about training billions of parameters on these models, well, guess what, like cutting the hops by almost two thirds is kind of a big deal, right the governments thing you talked about, And they went out and they got two of the best developers from the Linux kernel to work on it, such that this is native Linux now. So in other words, a lot of times these sort of accelerators you have to put little agents everywhere in
order to make that happen. It's about a software not here. And what they did is they abstracted all the metadata management around file systems into this layer above, thus opening up this massive data path underneath, so you can orchestrate data wherever you want it to go, and that's all handled in this hammer space abstraction layer, such that below the data is like just flying as fast as it possibly can to be able to train large language models.
And that's the only company I've come across that has gotten as deep as you're talking about, like literally understanding the hardware protocols and how these things operate together.
I just think, I mean, you guys need to talk.
Is you know what's interesting about that model is very similar to how we approach the like at a high level,
it's a captin space. But if we take that as an example, by the way code deployment of the data with a bunch of share memory stuff that it's like it is exactly how you start to eliminate the things that programmers have invented, right, Like in many of the bottle legs are just like made up that the things things that don't need to be in and like, look, at the end of the day, you can't eliminate complexity. You can either manage it yourself or you can let
someone else manage it. And when someone else's manages the complexity, then you're onboarding all of their bad decisions.
As well as the good decisions.
Right and so an example is a Linux kernel page cash, a great general I mean, I wrote the first pass of the storage layer, and once I did that, I was like, wow, the page cash is like a great general algorithm, and it's a terrible purpose built algorithm because it does it takes a bunch of locks. It like has all these global objects. There is like there's a bunch of contection, there's like a bunch of metadata that
has to get flushed, ultiple cash lines, blah blah blah blah. Yeah, like I could do this so much cheaply in like this hyper specific context and so very much cut from the same thre We have also a bunch of you know, either BSD or Linux colonel hackers as part of the engineering team.
I feel like the.
Nerds that we tend to attract some of my friends, they tend to be like the same kind of nerds that that would have on HPC systems.
Right, and that I mean, you know, the thing that really blew my mind when I interviewed David Flynn, he's the CEO of Hammer Space. This is like September maybe, and I was talking to him and I was like, dude,
you are the first person I've come across. And I've interviewed two thousand companies all right in this data and I be able the past twenty five years a lot a lot a lot of companies, and I always love to get as far down as I could possibly go to really understand who's tinkering where and what they're doing, and you know, I joke to David first of all, like shift left all this stuff. I'm like, you cannot shift any further left than into the operating system the end of the kernel, like that is as.
Far left as you can shift. It's like, well way over there.
And I said, you're the first company I've come across to do something which is you know, I was in Austin like fifteen sixteen years ago at an HPC conference and at the time I was, I was fifteen years ago because I was tracking cloud Dehra in the earliest days and Horton works. I know all those guys, I'm ra Awadala and friends of them for a long time, very very smart guys. Right, cloud Erra was going to be the big thing and then you know, long story short, cloud pretty.
Bad, yeah, miss cloud.
First of all, like my business partner, Robin Belori, he's retired now mostly, but he launched Ploor Research in UK, and he and I launched Blur group here in the States. But he had agree, always makes funny little comments.
He goes, who put the cloud in cloud Deerra?
No one, And that's the problem because it works to design for the cloud. You're like you called yourself cloud dehra and you're an on prem solution, Like, what on earth are you doing? But what I told David Flynn was you are the first person I've come across to fuse HPC with enterprise big data processing because these guys never talked. I go to this conference and no one from the big data world, analytics, bi none of that stuff. None of those people were talking to the HPC folks.
I'm like, why on earth don't you guys talk? Like there must be things they've figured out over here that you can use. And I think it's because you know, it's the center of gravity, right, HPC is it's heavy in life sciences, it's heavy in universities, so all they are their own little ecosystem, right, and they don't really want to worry about this other stuff. And then in the business world that's the cloud eras and the Horton works and the Verdicas and all those guys.
It's like two different worlds. I'm like, why don't you guys talk these the.
First one I can't? He was like, wow, I guess no one's ever noticed that before, Like, yeah, yeah, how am I the only one to notice this?
There's a few papers over the years that do you know, we obviously read on that was in that first one to figure it this out. I just think that they were There's just very few systems that are public that are this. I know of a couple of proprietary systems that have sort of taken this, and but but it's it's actually pretty common to get a ten XT thing. And the way I'd like to frame it is that sometimes you're lucky enough to reinvent the wheel when the
road changes. And look what changed between the time COPCA was what came out and Red Panda is hard drives got super super fast and really cheap, right like now you have this mbmss D devices that can write a page to to like the underliner storage I don't know, and like eight sixteen microseconds somewhere around there. Let's say like a little contention, like you know, twenty is fine, so super fast and if you know, you remember this pin and disc, you would right click your seat drive
and hit the fragment and it would take overnight. It would start making this AOL dial up noises and so so then you know, hardness got super fast, and yeah, exactly, I think everyone in the US nightmares from exactly like you've got mail area or like you know, when you're when your parents interrupted your your music downloads because they picked up the phone.
That's right, Oh the good old days.
It's terrible.
And then and then CPUs uh became like you know, uh, you know, MULTIICR but like really as a dominant factor, right, you go from like I guess a long time I got to single course uh to like whatever ninety six core vms on Amazon as the norm.
Like you know, readily available around the world.
And so if you were to start from a scratch, sometimes you get to do different things differently, and so that was the original. Theisis of of of red Panda.
That's cool.
Yeah.
The other company you should talk to is Ocient, you know Oscient.
I've heard of them, but I haven't personally talked to them.
They're doing hyper scale data warehousing, so they basically they saw NVM coming. And by the way, David Flynn, one of the other cool things about him is last company before hammer Space, they focused on the NVMe E protocol, so he was focused on trying to make that a thing, and now a handful of companies are really using that hammer Space for sure uses the mvm E protocol SODA's oceans,
and they use it to do trillions of records. I mean, just crazy amounts of records being brought in and it's a whole new era, you know.
He jokes.
He actually lives in a town where I used to be. My first job out of school was at the Lamont Metropolitan Newspaper in La Mont, Illinois, which is like, it's the tiny little corner of Cook County, just outside of Chicago, and it's actually older than Chicago. So the I and M now goes through Lamont's and that's what sort of
built the town was. They were building this I in M Canal, the Illinois Michigan Canal they call it, to get it from Lake Michigan to the Mississippi River so you could do transport, right, this is how old it goes.
But that's where he lives. I'm like, dude, I used to do that. I was the editor of the local newspaper in that town. Right. Cool, but yeah, it's wild. He's such a nice guy too. It's just Chicago, so they're they're.
Pretty humble, hard working, you know, city of big shoulders stuff.
But they spent two and a half years.
Their first GA was like version seventeen or something like we wrote drivers and everything because they saw, all right, we have to rewrite this whole pyramid to get up to a point where we can really leverage this stuff and just go hyper scale.
And they did so.
Now it's just like, good God, Like, how you know, how would charity to compete with that?
You can't.
I mean this is they have old, old, old technology and it's not going.
To work this new scale.
You know, That's how technology about.
It's like, in many ways, is why the world has space for our companies like Red Panda to go in and challenge basically multi billion dollar companies. It's like, okay, well on a product to product, let's compete.
I'm game.
Do you know?
We feel pretty confident of it took it took a while, you know, the first two years of the company, it was just me and a bunch of friends. We didn't work in a GARS, but pretty much in agars. We were overmode originally, and we hacked basically day and night for the first few years just to get something like so It Systems.
I didn't realize just how.
Hard it is to do a good job like you could do an okay job quickly in a year maybe two years. But to do a great job, it is just so so so hard.
So well, so many things to think through.
I mean, that's the problem is like you know, and I think a lot of people who don't know programming don't realize that. You know, with programming languages, you're always sort of dancing around the thing, right, the thing is what you're trying to do, and it's like, huh, you can't just go right at it.
You always have to kind of go around it somehow.
So it's like what is the what's the tightest circle and then the concentric circle around that and around that or however you want to view it, layers however you want to get there. Still, it's like, what are you
what are you trying to accomplish first and foremost? And how can you build the foundation as strong and alleyable as possible to be able to build on that, because you know, if you change your mind a year later, it's like, oh man, how like I remember I was talking to one guy one of the funniest conversations I had. This is probably six seven years ago now. We were talking about Kubernetes and how it is not is not stateful, right, it's it's a stateless environment.
And I was talking to this guy goes, yeah, I think some of those guys are started to think maybe we should have thought that. I thought about that.
Yeah, this whole architecture, like maybe state was kind of important. It's not gonna do it somewhere else, right, that's the point, like wherever else has to be managing the state.
And that gets pretty complicated.
Right, State is is the the problem. That's like the PSL resistance. It is like state is the hardest thing. I think I will know when AGI gets here, when it when it solves all the Kurnetis diplomentations like that's that's the test. If I can ask a prompt to solve my coronettes. It's just like that's that's a g I in my mind.
That's fun.
That's at what this means for users. I think to pop this sack a little bit of what we're seeing in prout, which is pretty cool. And then this is a shift you mentioned like, hey, red Panda has been classically good for the fortune five thousand Whatever's that's been true, But what we're seeing is that the same reasons why people that led to the designer space of what became. You know, all the messaging systems like Coffka and red
pant et cetera. Are needed for these agentic marklets. And so by that, I mean you want well named communication channels. And so in the Kafka parlance that's called a topic. In the sequel pipeline partlines, it's called a table, right, Like, you basically want something that is well named, Like I want to exchange messages on this channel, then that channel is going to be called orders, and the other channel is going to be called the fraud and the other
channel whatever. So you want these well named channels. But what's interesting is it's like what from an end to end perspective, you can about the first channel and the oppa channel. So effectively, when a user types in the prompt like summarize the tank a documents for a public company, say Mango, d B or whatever it is, and then it gives you a summary rate. But the in between you could always make it smarter by introducing something in
the middle. So you could always introduce an agent in the middle and continue to make it smarter over time as long as like the end to end channels continued are sort of like exported functions and see if you will. The second thing is that people wanted the same primitives of microservices. They wanted access control lists, they wanted audiit logging, they wanted independent pipelines, they wanted global policies of what models you're allowed to use and you're not allowed to use.
And so in any way, there's this full circle of like, hey, AA is a totally different thing in.
Like whatever, don't touch it. That will be right back.
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Welcome back to Inside Analysis. Here's your host, Eric Tabinat.
And take this to show.
So have all the like phenomenal smaller models that are produced in a state of the art quality answers, but they are small, you know, say sixty four gigs of RAM kind of thing, so like relative like you can you can run it on a single computer basically, and once you could do that, you're going to have these
networks of small models that are specializing in things. And so when you give a flow an into a task of like give me a summary, you can have these models assume personalities, like one personality is the program that is going to optimize the prompt the other personality is like the thing that is actually going to perform the task. The last personality is going to make sure that it's like making sure it's not insulting your customers.
It's like a safety model, right right, yep, exactly.
And so guess what when you're trying to deploy this to production, they think that is the long poll for large enterprises, this fortune five thousand. It's not the model because they're not building the model. First of all, they're just downloading it from from hugging phase or GitHub or something like that. So like that, that does not the long poll. The long call is like, that's the CIO trusted is the input and output record recorded, hence the
log are the right things accessing these AUDIIT logs? How can I do global secure and policies? So do I push it to an open IDA connect like octa to make sure that these agents are being like you know, I could sort of decommission a fleet of agents in.
Governance something attacked governance, right, Yeah, So those are.
The things that I didn't anticipate would be pillars to the acceleration of red Panda in the enterprise or you know, streaming systems in general.
Oh, interesting, that's fascinating. I get it. I get. I mean, well, because you have multiple steps right in this. I mean I look at Mistro. I think that's probably the most clever architecture because again, you've got multiple agents. They are very good at certain things.
And this is what I'm hearing from a lot of people, like even Variant, which has been around for a good long while, a pretty big company.
They talk about their little robots.
Because I asked the guy, are you going to try to get your agents to do multiple things?
He goes, no, we want them to do one thing.
Very very well, and then they'll be called by another sort of orchestrating agent. And that makes a whole lot of sense. But even still, like you look at some of the newer miles like, oh, eighty one percent accurate, I'm like, okay, what part of your business can be wrong twenty percent of the time? Not much, you know, I mean that's a big error for you know, for something serious like operations, no fulfillment, no accounting.
No you know all these things.
No no, no, no no, but you know in marketing, okay, marketing for content creation, things of this nature. But to your point, this new AI agent like application fabric if you will, needs to have the right mix of data at the right time time, and you can't be doing these sort of reaches into multiple different databases on demand to find out what the history is or something. I mean,
that's what I think is very interesting. So I love your point about how we thought it was going to be architectural this way and it's turning out to be that way.
That's it. That's interesting, you know, because it's it's.
A challenge for the organizations, and you know, I think a lot of companies have got to be like, what are we going to do?
Like, how are we going to figure this thing out?
Man?
Because it's there's so many moving parts and it keeps changing fast too. That's the other part, right, It's like it keeps changing.
I mean, I don't know.
I look at chat GPT and I think this is this deep sea thing. Was great news for just throwing down the garlet to these people because they had gone down this road. They'd already said, Okay, it costs so much money to train models, so just get used to the idea. Sam Altman's asking for what was it like three trillion dollars or seven trillion dollars to build out some whole new inversure're do, what are you talking about?
Five hundred billion? It's it must be a great one day. We hope to be that scale too. But you know, the other problem here in enterprise is this idea of sovereignty and who has access to your data. That's that's really I think the the major heartburn for the CEO and the CECL of these large companies. They are like, look, the model companies are great, They're going to be here for a while. We're going to send some select use
cases to those people. But I just don't feel it doesn't matter what the legal language says, personally, emotionally or whatever. I don't think this is the technology think really this is like a trust thing at the moment. Maybe it changes in the future and everyone is like, I don't want to use a vendor, but I'll send I'll run all my infrastructure in Amazon anyways, so I don't pay for software, but I run my all my code on Amazon.
I was like, you know, what is this mean?
So you know where they just they don't feel comfortable sending their private data to open AI or Anthropic or some of those places, and so what they feel comfortable with is running the smaller models inside their infrastructure. And so I think through tool calling, et cetera, you're going to need a glue system that looks like a log with function calling, you know, maybe some support and environment like connectivity. Is why we bought a company a few
months ago around connectors. Where you're going to be able you need to bridge internal data. So they say your are call database for simplicity with these global expert systems, right, So you're going to or this foundation model systems, right. And so instead of the interaction being like here's the prompt with all of the raw data context, you can pass it through first a local model that is great, and what you send is you're sending a digest of
the internal informations. And so an example would be the road data would be Alex's last five credit card transactions if you're trying to determine fraud, and then you can ask this model let's say deep seek or Meta Lama thie, so like, hey, I want you to summarize this into signals.
And so the signals would be like not not the credit card information, not where I live, not where the but instead would be the high level signals like Alex had five credit card transactions in downtown San Francisco with the timestamps, and it doesn't really say much. It just says like about five things. Right, So maybe like the level of sensitivity changes from like here's like the row five transactions, which has like Alex's address and his social
Security number and he's like full credit card data. And then you can once it's gone through this prism filter of this local model, then you can even use a foundation model. And so it's fascinating seeing the world change in real time. Pun not intended, where like you know, people are like trying to reason about how do I think about the world differently? But the metal point here is that you need a thing that's going to glue
the world. And that thing even if people have writed themselves it just it doesn't matter if it's right punt or not. It's going to look a whole lot like red Punda, right, Like you're going to have to write these things durably to this, You're going to have to figure out how to do AKA, You're going to have
to do how to do whatever. Accoles and like you know, centralized identity management and deployments and multi region and clouds and servilis and multi tenancy and blah blah blah blah, all of the things that are hard you're going to have to do it that, by the way, that are not differentiated for people. And so it's been fascinating to see the adoption of streaming as like the glue that it's going to make the future of agentic workloads.
So here's what I'd love to do.
We should try to get you on a show with ideally David Flynn, maybe Chris claud when David Flynn is hammer spaced Chris is from Ocians, because to really get into these things, right because these are deep, deep architectural discussions.
That's what our audience loves.
I think people tend to want to hear about the details too. This is the fun part, like the stuff that we've been talking about.
Yeah, and we love that. I mean, I don't want just the high level conversations. I want to get all the way into the weeds. And you mean, you know, luckily we recorded all this so I can go through and like what was he saying there and like just kind of unpack it. But you know, and so when you're talking about sovereignty. That's hammer space. That's what they solve because you have this parallel file system which basically
sits over all of your other file systems. So it consider of rest three you can sit over you know, Google Cloud platform on prem who cares, it doesn't matter. Now you can see all of your files and basically have this layer of abstraction, single pane of glass for managing them. And just the for example, that the copies problem, like what is it the average is nine or ten copies of a certain document you have like spread all around because of different backup strategies over the years and
migrations or whatever. That's the reality of de facto information ARCT textures. Well, they can solve that problem. And then you know, by the way, they're also feeding massive amounts of data to your GPUs to train these models or whatever it is you need done.
And you know as well as I do, if you don't.
Have a view over everything, whatever you're not seeing is where the problem could possibly be. Right, So data governance per se, I mean, you know, until five seven years ago.
It was kind of a joke. It's like, Okay, you can.
Control access to a database, you can control ac access to an app and that was about it. There wasn't a whole lot in between that you could use to achieve de facto data governance. And now you can do that. Now we can actually pull that stuff off, So you know, it's it's kind of amazing how so many developments have happened. And to your point, and I think this is the hot topic for you guys, is this new real time
application architecture. How do you feed these agents the context they need to make those decisions in a tight window. And I love this concept of the sort of intermedia model right where it finds the signals that can then be used by the foundational model to transact something where you're not divulging important enterprise information PII whatever I mean. I think that's a very very interesting thread that we could build something around. Frankly, we'll be talking to you
next time. You've been listening to Inside Analysis.
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From the Bureau of Economic Geology. This is Earth Date. Mark your calendars and buy some eye protection because on August twenty first, twenty seventeen, the US will experience a total eclipse of the sun. A total eclipse happens when the Moon passes perfectly in front of the Sun, blocking its view from the Earth. Only a small path across the US will see the Sun completely obscure. But that path of totality stretches from Oregon to South Carolina, passing
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Earth Date is produced by the Bureau of Economic Geology at the University of Texas at Austin. Earth Date is researched by Julie Hunnings, written by Harry Lynch, and distributed by Mark Blunt and Casey Walker. For more stories, follow us on Facebook or visit earth Date dot oorg.
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What's wrong with Neil Gorsuch? His soul I mean? As one of the domineering right wing extremists on the Supreme Court, Gorsuch routinely supports enthroning plutocracy, autocracy, and his own brand of Christian theocracy over people's democratic rights. But he also uses his unelected, unchecked judicial position to take power and justice away from America's least powerful, most vulnerable people, including
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on homelessness, but merely on sleeping outdoors. It makes no difference, exclaimed his supremeness, whether the violator is homeless or a backpacker on vacation, or I suppose he had, say, a Supreme Court justice sleeping under a bridge. To punctuate his cluelessness, Gorsuch actually asserted that the law applied equally to everyone, except, of course, that the homeless can't just go home after
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NBC News Radio. I'm Chris Garragio, Pope Francis remains in critical condition, with blood tests showing mild signs of kidney failure. In an update today, the Vatican noted that the Pope had a mild renal insufficiency, which is under control. The eighty eight year old pontiff is being treated for double pneumonia and is receiving oxygen therapy. The Vatican added that Pope Francis continues to be vigilant and well oriented. He took part in the Holy Mass from the apartment on
the tenth floor of the Jamelli Hospital this morning. This is the third time in his twelve year papacy that he has not delivered the Angelus prayer. Ukraine's president Vladimir Zelenski says he's willing to resign in extra change for peace or a NATO membership. Lisa Carton reports.
The Ukrainian president made the offer at a news conference Sunday, saying, quote, if it is peace for Ukraine and you really want me to leave my post, I'm ready. Zelenski also said he would also trade his position for immediate NATO membership if it means the safety of his country. This follows public disputes with President Trump last week after Trump implied Zelensky was responsible for Ukraine's war with Russia and called
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Elon Musk is giving federal workers a deadline of midnight tomorrow to justify their work or they'll be fired. In a social media post yesterday, Musk said federal employees will receive an email requesting information about what they worked on over the last seven days, and any failure to respond will be taken as a resignation. The move comes after President Trump said he'd like to see must be more
aggressive in his efforts to slash the federal workforce. An American Airlines flight that took off from New York was as awarded by Italian fighter jets after being diverted to Rome for security reasons. The flight was on its way to New Delhi when it requested a flight diversion to Leonardo da Vinci