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Speaker 3: now here's your host, Eric Kavanaugh.
Speaker 5: Hello, and welcome to Inside Analysis. I'm your host and
Speaker 5: Yell LeBlanc. On this episode, Eric Kavanaugh is interviewing Alex Galago,
Speaker 5: who's the CEO of Red Panda Data. Together, they will
Speaker 5: talk through the new landscape of LMS and how the
Speaker 5: innovation of AI is affecting companies all around. Stay tuned,
Speaker 5: you're listening to inside Analysis.
Speaker 6: I'd like to dig deeper into Red Panda.
Speaker 7: I mean, I know you've you've basically rewritten the whole
Speaker 7: platform to do streaming, to do it efficiently, because Java
Speaker 7: is not very efficient, right, So you said, well, just
Speaker 7: go to the drawing board and hack this thing out.
Speaker 6: How long did it take you to do that?
Speaker 8: Beca In many ways it feels.
Speaker 9: It felt to me like it was a natural evolution of,
Speaker 9: you know, decades of work. While I was in school,
Speaker 9: I took a job at a Forks trading company and
Speaker 9: I really got sort of introduced to a bunch of
Speaker 9: low latency things and how do markets work? And I
Speaker 9: ended up writing some some trading algorithms in a weird
Speaker 9: language called MQL four. It's like this weird obscure language,
Speaker 9: kind of Jenkie, to be honest, very Python like, but
Speaker 9: not at school. And then I wrote it at a
Speaker 9: bunch of trading apps on this thing called pips, and
Speaker 9: a pip is like whatever, like one hundred dollars or
Speaker 9: something like that, just like some really large spread. But
Speaker 9: you want to deal in terms of bacurns. This long
Speaker 9: story sort, that's where I really started to kind of
Speaker 9: lean into things that I found really fun, which were
Speaker 9: like low latency, high performance things. And so then I
Speaker 9: went to work for an Attech. I guess when I graduated,
Speaker 9: I worked in an embedded database and I worked on
Speaker 9: the greed this is for the BlackBerry phone. We wrote
Speaker 9: this like c parsing code where we were pulling this
Speaker 9: like three dimensional database into a series of doubles because
Speaker 9: it was really efficient to encode for I think open VMS,
Speaker 9: and so we wrote the color coding on the S
Speaker 9: and P five hundred table with like dynamic rendering. That
Speaker 9: was really cool and so long story short, when I
Speaker 9: think about the evolution of red Panda, it felt to
Speaker 9: me like the natural evolution of the ideas that I
Speaker 9: had worked on, which went to be an Attech in
Speaker 9: New York for a company called Yildmo, and then for
Speaker 9: a stream processing company that I wrote the tech for
Speaker 9: in twenty fourteen through twenty sixteen that I sold to Akamai.
Speaker 9: And so anyways, in many ways, it was like, why
Speaker 9: is the world so complicated? Why does it have to
Speaker 9: be so hard? And everyone that has been on call
Speaker 9: and you know, usually your page I goes up at
Speaker 9: three am. I don't know why, but it's like somewhere
Speaker 9: around that time. It's like though, it's when you're like
Speaker 9: writing your deep sleep and you get paid and then
Speaker 9: you wake up and you're like, I don't understand why
Speaker 9: this is so complicated.
Speaker 8: And so.
Speaker 9: It was like, well, why couldn't we do better? Like
Speaker 9: what's what's the mechanical reason? Like, okay, let's remove all
Speaker 9: the hype. I just want to understand fundamentally, what is
Speaker 9: the job that this software is doing that is so
Speaker 9: hard that requires it to be so complicated. It turned
Speaker 9: out not to be that difficult, frankly, and so I
Speaker 9: wrote something from a scratch really for me, and I
Speaker 9: left Akamai I think December twenty eighteen somewhere around there,
Speaker 9: like basically, and I started writing Red Panda in January.
Speaker 9: But I would say I had a like a pretty
Speaker 9: clear goal and so it was just a matter of
Speaker 9: writing the code. And so that part didn't take that long,
Speaker 9: maybe like three months to have a prototype, a couple
Speaker 9: of months to have something well that that works interesting.
Speaker 7: And of course the world has just sped up in
Speaker 7: all directions around us. Now, so we're all kind of
Speaker 7: riding this new wave. And you know, when I think
Speaker 7: about streaming, I've always i mean from the earliest days,
Speaker 7: I was like, oh my goodness, if you do this right,
Speaker 7: you can just you can supersede ancient architectures or what
Speaker 7: should be viewed as ancient architectures. But you have to
Speaker 7: do the whole thing. It's like when you try to
Speaker 7: fuse these two worlds together, that's where the impedance mismatch
Speaker 7: kicks into high gear and you've got to figure out
Speaker 7: some way.
Speaker 6: I mean, you know, I.
Speaker 7: Heard stories of at Vertica where they were taking all
Speaker 7: kinds of streaming dages just like filling it out into
Speaker 7: you know, a traditional database, and you know it's a
Speaker 7: column oriented database of course, but still it's like, Okay,
Speaker 7: you know, do you need all that? Like is that
Speaker 7: really really necessary? And the short answer, of course is no,
Speaker 7: not really. But you know, for for like net new
Speaker 7: use cases, I think you guys would be a dream, right,
Speaker 7: But in traditional architectures do you find that's kind of
Speaker 7: a hindrance is that the behemistic are the big fortune
Speaker 7: two thousand companies They just you know, they're like, oh
Speaker 7: my god, like can we even handle this is that?
Speaker 7: Is that still a hurdle or do you see more
Speaker 7: people figuring it out and just architecting around it.
Speaker 8: Yeah, good question. So on for that that's really our
Speaker 8: sweet spot.
Speaker 9: I know it's counterintuitive because it's like, Okay, well here's
Speaker 9: this you know, startup. But the brands that we work
Speaker 9: with are like, you know, probably drove you to work today.
Speaker 9: I guess if you work from home, maybe not, but
Speaker 9: for those of you that commute to work, like you know,
Speaker 9: whether it's like the largest electric car company, the largest
Speaker 9: city and largest bank in the US, largest like iced
Speaker 9: B in Europe, one of the largest banks in Australia, whatever.
Speaker 9: When you look at the brands that we tend to
Speaker 9: work with there, they tend to be this this fortune
Speaker 9: two thousand and I think fundamentally what happened is twofold
Speaker 9: one AI has definitely accelerated the pace at which people
Speaker 9: are wanting to move towards a more interactive architectures. That's
Speaker 9: why too people realize that really the log is the
Speaker 9: source of truth in an architecture and databases are caches.
Speaker 9: And I think this wasn't my idea was it was
Speaker 9: popularized by Amazon They're like the log is the source
Speaker 9: of truth and everything else is just the cash and
Speaker 9: so uh and I guess you know, third where when
Speaker 9: we started talking about Iceberg and so on, people are like,
Speaker 9: finally I get finally all the blocks. Uh you know,
Speaker 9: queny when you put the final piece on your lego,
Speaker 9: it's just like it looks beautiful and it works. Is
Speaker 9: when you can start to glue analytics, which is the
Speaker 9: look looking behind the present, which is really what Redpana
Speaker 9: is with with Apachi Iceberg as the glue, and then
Speaker 9: the future with autonomous decision making with with agents by
Speaker 9: carrying context. I was like, now I think people understand
Speaker 9: the timeline horizon of how an application works, and so
Speaker 9: we're seeing the opposite. We're seeing actually acceleration in the
Speaker 9: fortune two thousands or five thousand really more more then
Speaker 9: I sell early on. And I don't know if it's
Speaker 9: a function of brand. Maybe we have better marketing, Like
Speaker 9: it's kind of hard to tell, and I don't want
Speaker 9: to like a you know, correlate and or incorrect assumptions here.
Speaker 9: And so I think fundamentally where if you were to
Speaker 9: ask me, what's like your gut, I think is the
Speaker 9: world is thinking about applications differently in large because of
Speaker 9: AI and in that world, red Panda place a critical role.
Speaker 9: And I'm not sure, you know, in the context of
Speaker 9: our by C for example, there there's like a.
Speaker 8: You know, many alternatives.
Speaker 7: You know, you just reminded me of something. And I
Speaker 7: haven't read too much about this. And We've talked about
Speaker 7: streaming for years. I've talked about streaming for many, many years.
Speaker 6: There's MQ series.
Speaker 7: There's all kinds of things in the past that were streaming, right,
Speaker 7: It's like little message cues basically MQ series all kinds
Speaker 7: of different message cues, which is kind of what's streaming.
Speaker 6: Is that that's the category that's in.
Speaker 7: But to your point, there is a maturation now in
Speaker 7: the environment, and I'm thinking to myself, this is a
Speaker 7: real time application architecture, right. That's what it really boils
Speaker 7: down to is like, guys, look, you want an application
Speaker 7: that does something. What is the fuel that's going to
Speaker 7: use to do that something. Is it going to be
Speaker 7: a traditional static database which is the traditional way of
Speaker 7: doing things, in which case, guess what, that database will
Speaker 7: slow you down, Like sooner or later, it's going to
Speaker 7: slow you down either because it's going slowly, or things
Speaker 7: that attached to it are going slowly. But the point
Speaker 7: is that this just ever ever growing ballast. If you
Speaker 7: kind of look at it in a certain way, do
Speaker 7: you really want that? And I think that's your answer
Speaker 7: is no, not really. What you want is for your
Speaker 7: apps to use real time data that is live right now,
Speaker 7: that is interactive with the users or the partners or whatever.
Speaker 7: And that's what you're enabling, right It's a real time
Speaker 7: application architecture.
Speaker 8: Yeah, and so I think two comments.
Speaker 9: The first comment is that I think the reason why
Speaker 9: database is not enough is because you lose data. And
Speaker 9: so set another way, you need the highest fidelity of
Speaker 9: the data to be able to reconstruct a different materialization
Speaker 9: of your data. And so if your data it's in
Speaker 9: a relational database, let me give you the example is
Speaker 9: average sales price and so or like the price of
Speaker 9: the last ticker symbol and ice that that's probably a
Speaker 9: better example. The amount of data that is flown with
Speaker 9: every tick of that symbol as it's being traded is tremendous.
Speaker 9: But when you ask a database, it gives you one
Speaker 9: killer by the data vice like this is the company name,
Speaker 9: this is the ticker symbol whatever. This is some price,
Speaker 9: but the amount of input to the database could be
Speaker 9: a gigaby per second. And so what happens is you
Speaker 9: lose data, right like fundamentally it is it is a
Speaker 9: lossy compression algorithm almost right, like you're sending your data,
Speaker 9: but you're only querying the last point in time. And
Speaker 9: so that's what the databases are. And so I think
Speaker 9: it is limiting less from like particular you know, I
Speaker 9: think through put databases have gotten really good at throughput costly,
Speaker 9: you know, so maybe the other limit is dollars, but
Speaker 9: they I think that now modern databases are pretty good
Speaker 9: at handling the throughput latency not not so good. And
Speaker 9: so but it's more fundamentally it's about like you're losing
Speaker 9: access to the real data. And so what happens is
Speaker 9: it forces the architect and engineer to have like, uh,
Speaker 9: computed all of the ways in which they wanted their
Speaker 9: data before they write an application. And what the log
Speaker 9: gives you is freedom. You're like, well, I want to
Speaker 9: store it in a really cheap storage. They's say S
Speaker 9: three or something like that compatible and then today is
Speaker 9: stick pulling, then tomorrow could be whatever. Elastic could be
Speaker 9: whatever it is, and so that freedom is just key
Speaker 9: I think. For that's where I see like the biggest
Speaker 9: pillar from an architectural perspective.
Speaker 6: That's very interesting.
Speaker 9: And then for AI thought that changes a little bit too.
Speaker 9: But that's about way for a second.
Speaker 6: Yeah, what then what do you mean by that?
Speaker 9: So Okay, Historically, I think we talked about the challenges
Speaker 9: of picking a database. Yes, in old school databases, perhaps scale,
Speaker 9: uh you know, on throughput and latency those for limitation.
Speaker 9: In modern cloud databases, I think throughput is okay. Latency
Speaker 9: is still not great and so maybe for some use
Speaker 9: cases does tend it to be. But we talked about
Speaker 9: like the architectural primitives that you get when you adopt
Speaker 9: the law as the source of truth.
Speaker 8: What happens is when.
Speaker 9: I'm going to define an agent as like an object
Speaker 9: with an L l M right, and so for simplicity
Speaker 9: and when you have these things, uh, what I guess
Speaker 9: the future is going to be about this autonomous decision making.
Speaker 9: And so if you look at a timeline t and
Speaker 9: you say analytics stuff like data breaks and so on.
Speaker 9: They were all about looking at the past, help me
Speaker 9: do my job better. Where during best sellers, what region
Speaker 9: is the best producing region, what product is the best
Speaker 9: producing product? What should be my average sales price?
Speaker 10: Right?
Speaker 9: Like, help me do what I do better by looking
Speaker 9: at the past. That's analytics. Red Panda has been classically
Speaker 9: and we'll talk about NX about like being the best
Speaker 9: operational system in the world. Right, So if you're running
Speaker 9: your system and you depend let's say you're trying to
Speaker 9: protect white house that go from getting taken down by
Speaker 9: North Korea bots or something like that, you need to
Speaker 9: know the movement of IPAs around the world, or maybe
Speaker 9: you're trying to track fraud detection or whatever. So that's
Speaker 9: the present. That's the operational sense. The future is about
Speaker 9: carrying the context of the past and presents so that
Speaker 9: this code can make autonomous decision for you. And so
Speaker 9: let me let me can now loop all the timelines past,
Speaker 9: present and future. And so the past and present are
Speaker 9: going to be bridged by technologies like apati Iceberg, which
Speaker 9: is a way to have a zero shot integrations with
Speaker 9: all the query systems, and so that that's an important
Speaker 9: tig piece and streaming engines will play a second. So
Speaker 9: that's where streaming engines play super critical role. Is just
Speaker 9: exposing the data in a way that is accessible for
Speaker 9: many many tools, right doctor B ClickHouse, right like red
Speaker 9: pand or whatever Pinot trino U. Basically just about every
Speaker 9: data bas is going to speak Iceberg. So that's that's
Speaker 9: a critical road for streaming engines. And then for the future,
Speaker 9: it's about carrying context for the agents to do their job.
Speaker 9: And so let's say that the job of an agent
Speaker 9: is to determine yes or no to a credit card
Speaker 9: transaction or a credit or a crediting period or whatever
Speaker 9: you need to give it cont and so think of
Speaker 9: a self driving car, right, Like you need to carry
Speaker 9: the context, either like the images or the previous five
Speaker 9: transactions or some sort of windows so that the agent
Speaker 9: can make a decision. So as part of the prompt,
Speaker 9: you can tell the agent, Hey, is this or is
Speaker 9: this not a credit card transaction?
Speaker 8: These are Alex's last five.
Speaker 9: Credit card transactions and he just bought a bagel in
Speaker 9: San Francisco. So if it gets transacted in the UK, like,
Speaker 9: obviously market has fraud, and give me the explanation so
Speaker 9: I can render it on the screen. Right, And so
Speaker 9: I think that that's why streaming engines are seeing in acceleration,
Speaker 9: one as a glue to analytics and the present with Iceberg,
Speaker 9: and then second as a way to carry context for
Speaker 9: this autonomous decision making.
Speaker 7: So I'm trying to processes my brain, right. So when
Speaker 7: you have, like with cough gets topics, right, you have
Speaker 7: topics that are streaming and you can choose which ones
Speaker 7: you pull together in any given point in time as
Speaker 7: I recall, and that be comes the fabric or the
Speaker 7: context that you're talking about.
Speaker 6: So here you're.
Speaker 7: Talking about getting context from log files basically from systems
Speaker 7: that are important to this particular potential transaction. And what
Speaker 7: you're talking about is allowing the agent to be a
Speaker 7: much more adaptive and real time semi autonomous entity that
Speaker 7: will absorb context and just make a decision quickly. Yeah,
Speaker 7: Like that's the idea, is that? So you've in a sense,
Speaker 7: you've deconstructed the traditional data flow that goes into an
Speaker 7: application which makes a decision. Because to your point, like
Speaker 7: all the Stata warehousing, that's my background, it's all the
Speaker 7: Stata warehousing stuff, right, you do ELT and et and
Speaker 7: all this stuff.
Speaker 6: You get into this.
Speaker 7: Big central repository, which we did because they figured out
Speaker 7: that you couldn't really query an ERP It's not what
Speaker 7: it was designed for. And besides, what you want to
Speaker 7: do is know how all this stuff relates to each other.
Speaker 7: That's where the magic is, right, is how all these
Speaker 7: different pieces of data relate. And so with red Panda,
Speaker 7: what you've done is expedited the process of feeding the
Speaker 7: important bits from important systems into a real time context,
Speaker 7: which the agents can then grasp as needed.
Speaker 6: Is that right?
Speaker 9: So it's cool about red Panda From an engineer perspective,
Speaker 9: it's basically most of our code is in the open, uh.
Speaker 9: And so we will link an example here of what
Speaker 9: I mean, like a simple concise yamal file so people
Speaker 9: can just like see the whole thing put together and
Speaker 9: it becomes like really becomes something that they can, like
Speaker 9: they can run on their laptop.
Speaker 8: But that's exactly right. And so.
Speaker 9: First of all, I think it's worth it just to
Speaker 9: share like maybe a little bit of redpan So we
Speaker 9: did start red pandas is like you know, high performance
Speaker 9: storage engine. And the idea is like if we adapt
Speaker 9: to how.
Speaker 8: The hardware is. Right, so.
Speaker 9: Most most software just operates on like you know, a
Speaker 9: tremendous amounts of layers of abstraction, and you'll loose performance
Speaker 9: with every layer because you're generalizing things. And so when
Speaker 9: I started a red panel's like, what if we just
Speaker 9: adapt to how hardware works, and if you go all
Speaker 9: the way down to even like core to core coherency protocols,
Speaker 9: so you have you know core they say L zero
Speaker 9: or you know zero to one on a dual core system,
Speaker 9: single socket, right, so single motherboard. We don't have to
Speaker 9: complicate the picture with multiple sockets on a motherboard, but
Speaker 9: one motherboard you have one on one chip and the
Speaker 9: chip has two dual core right when you go down there,
Speaker 9: when you're talking about memory coherency protocol, it's all message passing.
Speaker 9: And the insight here from hardware manufacturing people is that
Speaker 9: you could do a bunch of useful work if you
Speaker 9: don't block, right, And so if you adopt a similar
Speaker 9: software architecture where it's all message passing, you can yield
Speaker 9: at points of blocking and then you can let the
Speaker 9: CPU do a lot more useful work.
Speaker 6: And so that's really interesting. Folks, don't touch it. That
Speaker 6: will be right back.
Speaker 11: You were listening to inside Analysis.
Speaker 3: Welcome back to inside Analysis. Here's your host, Eric tabanac.
Speaker 9: Red pand that runs on a network of ny square
Speaker 9: single producer, single consumer, lock free cues that acts as
Speaker 9: mailboxes for every core. And you can assume that every
Speaker 9: core is like an actor system on a super relatency network.
Speaker 9: And so when you embrace that, by the way, you
Speaker 9: get a simple concurrency I guess software structure and a
Speaker 9: simple parallelism uh deployment and so anyways, performance and this
Speaker 9: is like one of my favorite topics to talk about.
Speaker 9: I want to stop us here because this could be
Speaker 9: a two hour call where we like dig super deep
Speaker 9: into the mechanics of.
Speaker 6: The This is so interesting for lots of different ways.
Speaker 7: I mean, I've been around this business a lot of time,
Speaker 7: so I learned from all sorts of different vendors about
Speaker 7: different things they're doing, and it's always very interesting. But
Speaker 7: in the recent past, meaning six seven months ago, I
Speaker 7: ran into a company called hammer Space.
Speaker 6: Are you familiar with hammer Space that I mentioned those guys.
Speaker 7: You need to look into these guys, I mean, because
Speaker 7: they are thinking in a very similar way that you're thinking.
Speaker 7: They went out what they do. They're a parallel file system.
Speaker 7: So this is big in the HPC world for life
Speaker 7: sciences and training models. They're actually training a LAMA two
Speaker 7: and LAMA three, so they're being used to deliver the
Speaker 7: data to the CPU, and they did something that was
Speaker 7: so damn clever it blew my mind. He talked about
Speaker 7: how in the average AI training architecture, data has to
Speaker 7: make eleven hops to go from where it is to
Speaker 7: the GPU. It's like controller nodes and different things like
Speaker 7: that in the storage environment, the network, all these different
Speaker 7: steps along the way, but still that's eleven freaking hops.
Speaker 6: So they talked.
Speaker 7: About how I think it's vast to cut off one
Speaker 7: hop and cumulo cut off two hops.
Speaker 6: Maybe, but these guys.
Speaker 7: Were able to figure out by affinitizing the processing to
Speaker 7: where the GPU server, which has built in flash memory
Speaker 7: that often gets ignored. They call that stranded capacity basically
Speaker 7: because a lot of times companies will want to just
Speaker 7: have all their governance protocols and security around the storage
Speaker 7: array and they don't want to have to try to
Speaker 7: figure that out on the GPU array as well, right,
Speaker 7: so it's these two separate worlds. Well, they figured out
Speaker 7: how to affinitize that to where when the processor knows
Speaker 7: that the data is on this machine, Well, I don't
Speaker 7: even have to go to the network. I don't have
Speaker 7: to go to a storage around Look anywhere is right here.
Speaker 7: So they've adopted done to four hops from eleven. And
Speaker 7: if we're talking about training billions of parameters on these models, well,
Speaker 7: guess what, like cutting the hops by almost two thirds
Speaker 7: is kind of a big deal, right the governments thing
Speaker 7: you talked about, And they went out and they got
Speaker 7: two of the best developers from the Linux kernel to
Speaker 7: work on it, such that this is native Linux now.
Speaker 7: So in other words, a lot of times these sort
Speaker 7: of accelerators you have to put little agents everywhere in
Speaker 7: order to make that happen. It's about a software not here.
Speaker 7: And what they did is they abstracted all the metadata
Speaker 7: management around file systems into this layer above, thus opening
Speaker 7: up this massive data path underneath, so you can orchestrate
Speaker 7: data wherever you want it to go, and that's all
Speaker 7: handled in this hammer space abstraction layer, such that below
Speaker 7: the data is like just flying as fast as it
Speaker 7: possibly can to be able to train large language models.
Speaker 7: And that's the only company I've come across that has
Speaker 7: gotten as deep as you're talking about, like literally understanding
Speaker 7: the hardware protocols and how these things operate together.
Speaker 6: I just think, I mean, you guys need to talk.
Speaker 9: Is you know what's interesting about that model is very
Speaker 9: similar to how we approach the like at a high level,
Speaker 9: it's a captin space. But if we take that as
Speaker 9: an example, by the way code deployment of the data
Speaker 9: with a bunch of share memory stuff that it's like
Speaker 9: it is exactly how you start to eliminate the things
Speaker 9: that programmers have invented, right, Like in many of the
Speaker 9: bottle legs are just like made up that the things
Speaker 9: things that don't need to be in and like, look,
Speaker 9: at the end of the day, you can't eliminate complexity.
Speaker 9: You can either manage it yourself or you can let
Speaker 9: someone else manage it. And when someone else's manages the complexity,
Speaker 9: then you're onboarding all of their bad decisions.
Speaker 8: As well as the good decisions.
Speaker 9: Right and so an example is a Linux kernel page cash,
Speaker 9: a great general I mean, I wrote the first pass
Speaker 9: of the storage layer, and once I did that, I
Speaker 9: was like, wow, the page cash is like a great
Speaker 9: general algorithm, and it's a terrible purpose built algorithm because
Speaker 9: it does it takes a bunch of locks. It like
Speaker 9: has all these global objects. There is like there's a
Speaker 9: bunch of contection, there's like a bunch of metadata that
Speaker 9: has to get flushed, ultiple cash lines, blah blah blah blah. Yeah,
Speaker 9: like I could do this so much cheaply in like
Speaker 9: this hyper specific context and so very much cut from
Speaker 9: the same thre We have also a bunch of you know,
Speaker 9: either BSD or Linux colonel hackers as part of the
Speaker 9: engineering team.
Speaker 8: I feel like the.
Speaker 9: Nerds that we tend to attract some of my friends,
Speaker 9: they tend to be like the same kind of nerds
Speaker 9: that that would have on HPC systems.
Speaker 7: Right, and that I mean, you know, the thing that
Speaker 7: really blew my mind when I interviewed David Flynn, he's
Speaker 7: the CEO of Hammer Space. This is like September maybe,
Speaker 7: and I was talking to him and I was like, dude,
Speaker 7: you are the first person I've come across. And I've
Speaker 7: interviewed two thousand companies all right in this data and
Speaker 7: I be able the past twenty five years a lot
Speaker 7: a lot a lot of companies, and I always love
Speaker 7: to get as far down as I could possibly go
Speaker 7: to really understand who's tinkering where and what they're doing,
Speaker 7: and you know, I joke to David first of all,
Speaker 7: like shift left all this stuff. I'm like, you cannot
Speaker 7: shift any further left than into the operating system the
Speaker 7: end of the kernel, like that is as.
Speaker 6: Far left as you can shift. It's like, well way
Speaker 6: over there.
Speaker 7: And I said, you're the first company I've come across
Speaker 7: to do something which is you know, I was in
Speaker 7: Austin like fifteen sixteen years ago at an HPC conference
Speaker 7: and at the time I was, I was fifteen years
Speaker 7: ago because I was tracking cloud Dehra in the earliest
Speaker 7: days and Horton works. I know all those guys, I'm
Speaker 7: ra Awadala and friends of them for a long time,
Speaker 7: very very smart guys. Right, cloud Erra was going to
Speaker 7: be the big thing and then you know, long story short,
Speaker 7: cloud pretty.
Speaker 6: Bad, yeah, miss cloud.
Speaker 7: First of all, like my business partner, Robin Belori, he's
Speaker 7: retired now mostly, but he launched Ploor Research in UK,
Speaker 7: and he and I launched Blur group here in the States.
Speaker 7: But he had agree, always makes funny little comments.
Speaker 6: He goes, who put the cloud in cloud Deerra?
Speaker 7: No one, And that's the problem because it works to
Speaker 7: design for the cloud. You're like you called yourself cloud
Speaker 7: dehra and you're an on prem solution, Like, what on
Speaker 7: earth are you doing? But what I told David Flynn
Speaker 7: was you are the first person I've come across to
Speaker 7: fuse HPC with enterprise big data processing because these guys
Speaker 7: never talked. I go to this conference and no one
Speaker 7: from the big data world, analytics, bi none of that stuff.
Speaker 7: None of those people were talking to the HPC folks.
Speaker 7: I'm like, why on earth don't you guys talk? Like
Speaker 7: there must be things they've figured out over here that
Speaker 7: you can use. And I think it's because you know,
Speaker 7: it's the center of gravity, right, HPC is it's heavy
Speaker 7: in life sciences, it's heavy in universities, so all they
Speaker 7: are their own little ecosystem, right, and they don't really
Speaker 7: want to worry about this other stuff. And then in
Speaker 7: the business world that's the cloud eras and the Horton
Speaker 7: works and the Verdicas and all those guys.
Speaker 6: It's like two different worlds. I'm like, why don't you
Speaker 6: guys talk these the.
Speaker 7: First one I can't? He was like, wow, I guess
Speaker 7: no one's ever noticed that before, Like, yeah, yeah, how
Speaker 7: am I the only one to notice this?
Speaker 9: There's a few papers over the years that do you know,
Speaker 9: we obviously read on that was in that first one
Speaker 9: to figure it this out. I just think that they
Speaker 9: were There's just very few systems that are public that
Speaker 9: are this. I know of a couple of proprietary systems
Speaker 9: that have sort of taken this, and but but it's
Speaker 9: it's actually pretty common to get a ten XT thing.
Speaker 9: And the way I'd like to frame it is that
Speaker 9: sometimes you're lucky enough to reinvent the wheel when the
Speaker 9: road changes. And look what changed between the time COPCA
Speaker 9: was what came out and Red Panda is hard drives
Speaker 9: got super super fast and really cheap, right like now
Speaker 9: you have this mbmss D devices that can write a
Speaker 9: page to to like the underliner storage I don't know,
Speaker 9: and like eight sixteen microseconds somewhere around there. Let's say
Speaker 9: like a little contention, like you know, twenty is fine,
Speaker 9: so super fast and if you know, you remember this
Speaker 9: pin and disc, you would right click your seat drive
Speaker 9: and hit the fragment and it would take overnight. It
Speaker 9: would start making this AOL dial up noises and so
Speaker 9: so then you know, hardness got super fast, and yeah, exactly,
Speaker 9: I think everyone in the US nightmares from exactly like
Speaker 9: you've got mail area or like you know, when you're
Speaker 9: when your parents interrupted your your music downloads because they
Speaker 9: picked up the phone.
Speaker 6: That's right, Oh the good old days.
Speaker 8: It's terrible.
Speaker 9: And then and then CPUs uh became like you know, uh,
Speaker 9: you know, MULTIICR but like really as a dominant factor, right,
Speaker 9: you go from like I guess a long time I
Speaker 9: got to single course uh to like whatever ninety six
Speaker 9: core vms on Amazon as the norm.
Speaker 8: Like you know, readily available around the world.
Speaker 9: And so if you were to start from a scratch,
Speaker 9: sometimes you get to do different things differently, and so
Speaker 9: that was the original. Theisis of of of red Panda.
Speaker 6: That's cool.
Speaker 8: Yeah.
Speaker 7: The other company you should talk to is Ocient, you
Speaker 7: know Oscient.
Speaker 8: I've heard of them, but I haven't personally talked to them.
Speaker 7: They're doing hyper scale data warehousing, so they basically they
Speaker 7: saw NVM coming. And by the way, David Flynn, one
Speaker 7: of the other cool things about him is last company
Speaker 7: before hammer Space, they focused on the NVMe E protocol,
Speaker 7: so he was focused on trying to make that a thing,
Speaker 7: and now a handful of companies are really using that
Speaker 7: hammer Space for sure uses the mvm E protocol SODA's oceans,
Speaker 7: and they use it to do trillions of records. I mean,
Speaker 7: just crazy amounts of records being brought in and it's
Speaker 7: a whole new era, you know.
Speaker 6: He jokes.
Speaker 7: He actually lives in a town where I used to be.
Speaker 7: My first job out of school was at the Lamont
Speaker 7: Metropolitan Newspaper in La Mont, Illinois, which is like, it's
Speaker 7: the tiny little corner of Cook County, just outside of Chicago,
Speaker 7: and it's actually older than Chicago. So the I and
Speaker 7: M now goes through Lamont's and that's what sort of
Speaker 7: built the town was. They were building this I in
Speaker 7: M Canal, the Illinois Michigan Canal they call it, to
Speaker 7: get it from Lake Michigan to the Mississippi River so
Speaker 7: you could do transport, right, this is how old it goes.
Speaker 6: But that's where he lives. I'm like, dude, I used
Speaker 6: to do that. I was the editor of the local
Speaker 6: newspaper in that town. Right. Cool, but yeah, it's wild.
Speaker 6: He's such a nice guy too. It's just Chicago, so
Speaker 6: they're they're.
Speaker 7: Pretty humble, hard working, you know, city of big shoulders stuff.
Speaker 6: But they spent two and a half years.
Speaker 7: Their first GA was like version seventeen or something like
Speaker 7: we wrote drivers and everything because they saw, all right,
Speaker 7: we have to rewrite this whole pyramid to get up
Speaker 7: to a point where we can really leverage this stuff
Speaker 7: and just go hyper scale.
Speaker 6: And they did so.
Speaker 7: Now it's just like, good God, Like, how you know,
Speaker 7: how would charity to compete with that?
Speaker 6: You can't.
Speaker 7: I mean this is they have old, old, old technology
Speaker 7: and it's not going.
Speaker 6: To work this new scale.
Speaker 8: You know, That's how technology about.
Speaker 9: It's like, in many ways, is why the world has
Speaker 9: space for our companies like Red Panda to go in
Speaker 9: and challenge basically multi billion dollar companies. It's like, okay,
Speaker 9: well on a product to product, let's compete.
Speaker 8: I'm game.
Speaker 12: Do you know?
Speaker 9: We feel pretty confident of it took it took a while,
Speaker 9: you know, the first two years of the company, it
Speaker 9: was just me and a bunch of friends. We didn't
Speaker 9: work in a GARS, but pretty much in agars. We
Speaker 9: were overmode originally, and we hacked basically day and night
Speaker 9: for the first few years just to get something like
Speaker 9: so It Systems.
Speaker 8: I didn't realize just how.
Speaker 9: Hard it is to do a good job like you
Speaker 9: could do an okay job quickly in a year maybe
Speaker 9: two years. But to do a great job, it is
Speaker 9: just so so so hard.
Speaker 6: So well, so many things to think through.
Speaker 7: I mean, that's the problem is like you know, and
Speaker 7: I think a lot of people who don't know programming
Speaker 7: don't realize that. You know, with programming languages, you're always
Speaker 7: sort of dancing around the thing, right, the thing is
Speaker 7: what you're trying to do, and it's like, huh, you
Speaker 7: can't just go right at it.
Speaker 6: You always have to kind of go around it somehow.
Speaker 7: So it's like what is the what's the tightest circle
Speaker 7: and then the concentric circle around that and around that
Speaker 7: or however you want to view it, layers however you
Speaker 7: want to get there. Still, it's like, what are you
Speaker 7: what are you trying to accomplish first and foremost? And
Speaker 7: how can you build the foundation as strong and alleyable
Speaker 7: as possible to be able to build on that, because
Speaker 7: you know, if you change your mind a year later,
Speaker 7: it's like, oh man, how like I remember I was
Speaker 7: talking to one guy one of the funniest conversations I had.
Speaker 7: This is probably six seven years ago now. We were
Speaker 7: talking about Kubernetes and how it is not is not stateful, right,
Speaker 7: it's it's a stateless environment.
Speaker 6: And I was talking to this guy goes, yeah, I
Speaker 6: think some of those guys are started to think maybe
Speaker 6: we should have thought that. I thought about that.
Speaker 7: Yeah, this whole architecture, like maybe state was kind of important.
Speaker 7: It's not gonna do it somewhere else, right, that's the point,
Speaker 7: like wherever else has to be managing the state.
Speaker 6: And that gets pretty complicated.
Speaker 9: Right, State is is the the problem. That's like the
Speaker 9: PSL resistance. It is like state is the hardest thing.
Speaker 9: I think I will know when AGI gets here, when
Speaker 9: it when it solves all the Kurnetis diplomentations like that's
Speaker 9: that's the test. If I can ask a prompt to
Speaker 9: solve my coronettes. It's just like that's that's a g
Speaker 9: I in my mind.
Speaker 8: That's fun.
Speaker 9: That's at what this means for users. I think to
Speaker 9: pop this sack a little bit of what we're seeing
Speaker 9: in prout, which is pretty cool. And then this is
Speaker 9: a shift you mentioned like, hey, red Panda has been
Speaker 9: classically good for the fortune five thousand Whatever's that's been true,
Speaker 9: But what we're seeing is that the same reasons why
Speaker 9: people that led to the designer space of what became.
Speaker 9: You know, all the messaging systems like Coffka and red
Speaker 9: pant et cetera. Are needed for these agentic marklets. And
Speaker 9: so by that, I mean you want well named communication channels.
Speaker 9: And so in the Kafka parlance that's called a topic.
Speaker 9: In the sequel pipeline partlines, it's called a table, right, Like,
Speaker 9: you basically want something that is well named, Like I
Speaker 9: want to exchange messages on this channel, then that channel
Speaker 9: is going to be called orders, and the other channel
Speaker 9: is going to be called the fraud and the other
Speaker 9: channel whatever. So you want these well named channels. But
Speaker 9: what's interesting is it's like what from an end to
Speaker 9: end perspective, you can about the first channel and the
Speaker 9: oppa channel. So effectively, when a user types in the
Speaker 9: prompt like summarize the tank a documents for a public company,
Speaker 9: say Mango, d B or whatever it is, and then
Speaker 9: it gives you a summary rate. But the in between
Speaker 9: you could always make it smarter by introducing something in
Speaker 9: the middle. So you could always introduce an agent in
Speaker 9: the middle and continue to make it smarter over time
Speaker 9: as long as like the end to end channels continued
Speaker 9: are sort of like exported functions and see if you will.
Speaker 9: The second thing is that people wanted the same primitives
Speaker 9: of microservices. They wanted access control lists, they wanted audiit logging,
Speaker 9: they wanted independent pipelines, they wanted global policies of what
Speaker 9: models you're allowed to use and you're not allowed to use.
Speaker 9: And so in any way, there's this full circle of like, hey,
Speaker 9: AA is a totally different thing in.
Speaker 6: Like whatever, don't touch it. That will be right back.
Speaker 11: You were listening to Inside Analysis.
Speaker 3: Welcome back to Inside Analysis. Here's your host, Eric Tabinat.
Speaker 8: And take this to show.
Speaker 9: So have all the like phenomenal smaller models that are
Speaker 9: produced in a state of the art quality answers, but
Speaker 9: they are small, you know, say sixty four gigs of
Speaker 9: RAM kind of thing, so like relative like you can
Speaker 9: you can run it on a single computer basically, and
Speaker 9: once you could do that, you're going to have these
Speaker 9: networks of small models that are specializing in things. And
Speaker 9: so when you give a flow an into a task
Speaker 9: of like give me a summary, you can have these
Speaker 9: models assume personalities, like one personality is the program that
Speaker 9: is going to optimize the prompt the other personality is
Speaker 9: like the thing that is actually going to perform the task.
Speaker 9: The last personality is going to make sure that it's
Speaker 9: like making sure it's not insulting your customers.
Speaker 8: It's like a safety model, right right, yep, exactly.
Speaker 9: And so guess what when you're trying to deploy this
Speaker 9: to production, they think that is the long poll for
Speaker 9: large enterprises, this fortune five thousand. It's not the model
Speaker 9: because they're not building the model. First of all, they're
Speaker 9: just downloading it from from hugging phase or GitHub or
Speaker 9: something like that. So like that, that does not the
Speaker 9: long poll. The long call is like, that's the CIO
Speaker 9: trusted is the input and output record recorded, hence the
Speaker 9: log are the right things accessing these AUDIIT logs? How
Speaker 9: can I do global secure and policies? So do I
Speaker 9: push it to an open IDA connect like octa to
Speaker 9: make sure that these agents are being like you know,
Speaker 9: I could sort of decommission a fleet of agents in.
Speaker 6: Governance something attacked governance, right, Yeah, So those are.
Speaker 9: The things that I didn't anticipate would be pillars to
Speaker 9: the acceleration of red Panda in the enterprise or you know,
Speaker 9: streaming systems in general.
Speaker 6: Oh, interesting, that's fascinating. I get it. I get. I mean, well,
Speaker 6: because you have multiple steps right in this. I mean
Speaker 6: I look at Mistro. I think that's probably the most
Speaker 6: clever architecture because again, you've got multiple agents. They are
Speaker 6: very good at certain things.
Speaker 7: And this is what I'm hearing from a lot of people,
Speaker 7: like even Variant, which has been around for a good
Speaker 7: long while, a pretty big company.
Speaker 6: They talk about their little robots.
Speaker 7: Because I asked the guy, are you going to try
Speaker 7: to get your agents to do multiple things?
Speaker 6: He goes, no, we want them to do one thing.
Speaker 7: Very very well, and then they'll be called by another
Speaker 7: sort of orchestrating agent. And that makes a whole lot
Speaker 7: of sense. But even still, like you look at some
Speaker 7: of the newer miles like, oh, eighty one percent accurate,
Speaker 7: I'm like, okay, what part of your business can be
Speaker 7: wrong twenty percent of the time? Not much, you know,
Speaker 7: I mean that's a big error for you know, for
Speaker 7: something serious like operations, no fulfillment, no accounting.
Speaker 6: No you know all these things.
Speaker 7: No no, no, no no, but you know in marketing, okay,
Speaker 7: marketing for content creation, things of this nature. But to
Speaker 7: your point, this new AI agent like application fabric if
Speaker 7: you will, needs to have the right mix of data
Speaker 7: at the right time time, and you can't be doing
Speaker 7: these sort of reaches into multiple different databases on demand
Speaker 7: to find out what the history is or something. I mean,
Speaker 7: that's what I think is very interesting. So I love
Speaker 7: your point about how we thought it was going to
Speaker 7: be architectural this way and it's turning out to be
Speaker 7: that way.
Speaker 6: That's it. That's interesting, you know, because it's it's.
Speaker 7: A challenge for the organizations, and you know, I think
Speaker 7: a lot of companies have got to be like, what
Speaker 7: are we going to do?
Speaker 6: Like, how are we going to figure this thing out?
Speaker 13: Man?
Speaker 7: Because it's there's so many moving parts and it keeps
Speaker 7: changing fast too. That's the other part, right, It's like
Speaker 7: it keeps changing.
Speaker 6: I mean, I don't know.
Speaker 7: I look at chat GPT and I think this is
Speaker 7: this deep sea thing. Was great news for just throwing
Speaker 7: down the garlet to these people because they had gone
Speaker 7: down this road. They'd already said, Okay, it costs so
Speaker 7: much money to train models, so just get used to
Speaker 7: the idea. Sam Altman's asking for what was it like
Speaker 7: three trillion dollars or seven trillion dollars to build out
Speaker 7: some whole new inversure're do, what are you talking about?
Speaker 9: Five hundred billion? It's it must be a great one day.
Speaker 9: We hope to be that scale too. But you know,
Speaker 9: the other problem here in enterprise is this idea of
Speaker 9: sovereignty and who has access to your data. That's that's
Speaker 9: really I think the the major heartburn for the CEO
Speaker 9: and the CECL of these large companies. They are like, look,
Speaker 9: the model companies are great, They're going to be here
Speaker 9: for a while. We're going to send some select use
Speaker 9: cases to those people. But I just don't feel it
Speaker 9: doesn't matter what the legal language says, personally, emotionally or whatever.
Speaker 9: I don't think this is the technology think really this
Speaker 9: is like a trust thing at the moment. Maybe it
Speaker 9: changes in the future and everyone is like, I don't
Speaker 9: want to use a vendor, but I'll send I'll run
Speaker 9: all my infrastructure in Amazon anyways, so I don't pay
Speaker 9: for software, but I run my all my code on Amazon.
Speaker 8: I was like, you know, what is this mean?
Speaker 9: So you know where they just they don't feel comfortable
Speaker 9: sending their private data to open AI or Anthropic or
Speaker 9: some of those places, and so what they feel comfortable
Speaker 9: with is running the smaller models inside their infrastructure. And
Speaker 9: so I think through tool calling, et cetera, you're going
Speaker 9: to need a glue system that looks like a log
Speaker 9: with function calling, you know, maybe some support and environment
Speaker 9: like connectivity. Is why we bought a company a few
Speaker 9: months ago around connectors. Where you're going to be able
Speaker 9: you need to bridge internal data. So they say your
Speaker 9: are call database for simplicity with these global expert systems, right,
Speaker 9: So you're going to or this foundation model systems, right.
Speaker 9: And so instead of the interaction being like here's the
Speaker 9: prompt with all of the raw data context, you can
Speaker 9: pass it through first a local model that is great,
Speaker 9: and what you send is you're sending a digest of
Speaker 9: the internal informations. And so an example would be the
Speaker 9: road data would be Alex's last five credit card transactions
Speaker 9: if you're trying to determine fraud, and then you can
Speaker 9: ask this model let's say deep seek or Meta Lama thie,
Speaker 9: so like, hey, I want you to summarize this into signals.
Speaker 9: And so the signals would be like not not the
Speaker 9: credit card information, not where I live, not where the
Speaker 9: but instead would be the high level signals like Alex
Speaker 9: had five credit card transactions in downtown San Francisco with
Speaker 9: the timestamps, and it doesn't really say much. It just
Speaker 9: says like about five things. Right, So maybe like the
Speaker 9: level of sensitivity changes from like here's like the row
Speaker 9: five transactions, which has like Alex's address and his social
Speaker 9: Security number and he's like full credit card data. And
Speaker 9: then you can once it's gone through this prism filter
Speaker 9: of this local model, then you can even use a
Speaker 9: foundation model. And so it's fascinating seeing the world change
Speaker 9: in real time. Pun not intended, where like you know,
Speaker 9: people are like trying to reason about how do I
Speaker 9: think about the world differently? But the metal point here
Speaker 9: is that you need a thing that's going to glue
Speaker 9: the world. And that thing even if people have writed
Speaker 9: themselves it just it doesn't matter if it's right punt
Speaker 9: or not. It's going to look a whole lot like
Speaker 9: red Punda, right, Like you're going to have to write
Speaker 9: these things durably to this, You're going to have to
Speaker 9: figure out how to do AKA, You're going to have
Speaker 9: to do how to do whatever. Accoles and like you know,
Speaker 9: centralized identity management and deployments and multi region and clouds
Speaker 9: and servilis and multi tenancy and blah blah blah blah,
Speaker 9: all of the things that are hard you're going to
Speaker 9: have to do it that, by the way, that are
Speaker 9: not differentiated for people. And so it's been fascinating to
Speaker 9: see the adoption of streaming as like the glue that
Speaker 9: it's going to make the future of agentic workloads.
Speaker 6: So here's what I'd love to do.
Speaker 7: We should try to get you on a show with
Speaker 7: ideally David Flynn, maybe Chris claud when David Flynn is
Speaker 7: hammer spaced Chris is from Ocians, because to really get
Speaker 7: into these things, right because these are deep, deep architectural discussions.
Speaker 6: That's what our audience loves.
Speaker 9: I think people tend to want to hear about the
Speaker 9: details too. This is the fun part, like the stuff
Speaker 9: that we've been talking about.
Speaker 7: Yeah, and we love that. I mean, I don't want
Speaker 7: just the high level conversations. I want to get all
Speaker 7: the way into the weeds. And you mean, you know,
Speaker 7: luckily we recorded all this so I can go through
Speaker 7: and like what was he saying there and like just
Speaker 7: kind of unpack it. But you know, and so when
Speaker 7: you're talking about sovereignty. That's hammer space. That's what they
Speaker 7: solve because you have this parallel file system which basically
Speaker 7: sits over all of your other file systems. So it
Speaker 7: consider of rest three you can sit over you know,
Speaker 7: Google Cloud platform on prem who cares, it doesn't matter.
Speaker 7: Now you can see all of your files and basically
Speaker 7: have this layer of abstraction, single pane of glass for
Speaker 7: managing them. And just the for example, that the copies problem,
Speaker 7: like what is it the average is nine or ten
Speaker 7: copies of a certain document you have like spread all
Speaker 7: around because of different backup strategies over the years and
Speaker 7: migrations or whatever. That's the reality of de facto information
Speaker 7: ARCT textures. Well, they can solve that problem. And then
Speaker 7: you know, by the way, they're also feeding massive amounts
Speaker 7: of data to your GPUs to train these models or
Speaker 7: whatever it is you need done.
Speaker 6: And you know as well as I do, if you don't.
Speaker 7: Have a view over everything, whatever you're not seeing is
Speaker 7: where the problem could possibly be. Right, So data governance
Speaker 7: per se, I mean, you know, until five seven years ago.
Speaker 6: It was kind of a joke. It's like, Okay, you can.
Speaker 7: Control access to a database, you can control ac access
Speaker 7: to an app and that was about it. There wasn't
Speaker 7: a whole lot in between that you could use to
Speaker 7: achieve de facto data governance. And now you can do that.
Speaker 7: Now we can actually pull that stuff off, So you know,
Speaker 7: it's it's kind of amazing how so many developments have happened.
Speaker 7: And to your point, and I think this is the
Speaker 7: hot topic for you guys, is this new real time
Speaker 7: application architecture. How do you feed these agents the context
Speaker 7: they need to make those decisions in a tight window.
Speaker 7: And I love this concept of the sort of intermedia
Speaker 7: model right where it finds the signals that can then
Speaker 7: be used by the foundational model to transact something where
Speaker 7: you're not divulging important enterprise information PII whatever I mean.
Speaker 7: I think that's a very very interesting thread that we
Speaker 7: could build something around. Frankly, we'll be talking to you
Speaker 7: next time. You've been listening to Inside Analysis.
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Speaker 17: KCAA Radio playing the hottest hits in the Coolest Conversations
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Speaker 17: the ray of music, talk, sports, community outreach, and veteran resources.
Speaker 8: The hits from the.
Speaker 17: Sixties, seventies, eighties, nineties, and today's hits. The Farandozier Show
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Speaker 18: I'll be saving yesterday in the dumpu.
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Speaker 19: With sixty years of fascinating facts. This is the man
Speaker 19: from yesterday and back in time. We go to this
Speaker 19: time in nineteen fifty eight. Centr D's rise to fame
Speaker 19: is coming quickly. The fifteen year old began modeling at twelve.
Speaker 19: She was a girl Scout and modeled at one of
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Speaker 18: I'm Sa'na benzon Win, I can't, I'm.
Speaker 19: Sandra And from this time in nineteen sixty two, CBS
Speaker 19: announces that news corresponded to Walter Cronkite will replace Douglas
Speaker 19: Edwards on its early television news program beginning next month
Speaker 19: in April of nineteen sixty two.
Speaker 3: This is the CBS Evening News with Walter Cronkite.
Speaker 1: And that's the way it is.
Speaker 2: Thursday, March seventeenth, nineteen seventy seven, This is Walter grand Guite,
Speaker 2: CBS News, good.
Speaker 19: Night, And from this time in nineteen sixty now playing
Speaker 19: in theaters, Sink the Bismark, Although not in the movie,
Speaker 19: the hit song Sink the Bismarck is moving off the
Speaker 19: charts by Johnny Horton.
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Speaker 21: What's wrong with Neil Gorsuch? His soul I mean? As
Speaker 21: one of the domineering right wing extremists on the Supreme Court,
Speaker 21: Gorsuch routinely supports enthroning plutocracy, autocracy, and his own brand
Speaker 21: of Christian theocracy over people's democratic rights. But he also
Speaker 21: uses his unelected, unchecked judicial position to take power and
Speaker 21: justice away from America's least powerful, most vulnerable people, including
Speaker 21: the homeless. For example, he ruled last month that an
Speaker 21: Oregon city's ban on homeless residents sleeping outdoors was not
Speaker 21: cruel and unusual punishment, never mind that the city provided
Speaker 21: nowhere else for homeless individuals and families to bed down.
Speaker 21: Gorsuch saw no problem with penalizing people who have to
Speaker 21: sleep or camp out in parks, on the street, et cetera.
Speaker 21: After all, he blithely explained, it was not a ban
Speaker 21: on homelessness, but merely on sleeping outdoors. It makes no difference,
Speaker 21: exclaimed his supremeness, whether the violator is homeless or a
Speaker 21: backpacker on vacation, or I suppose he had, say, a
Speaker 21: Supreme Court justice sleeping under a bridge. To punctuate his cluelessness,
Speaker 21: Gorsuch actually asserted that the law applied equally to everyone, except,
Speaker 21: of course, that the homeless can't just go home after
Speaker 21: being kicked out from under the bridge. This at Jim Higtailer,
Speaker 21: saying its rank and justice for Gore, such a child
Speaker 21: of a politically powerful and rich family, product of ivy
Speaker 21: league schools and high dollar law firms, possessor of enormous
Speaker 21: personal wealth and multiple homes to dictate. Let them meet
Speaker 21: cake rules for homeless people who'll never know or understand. Yes,
Speaker 21: homelessness is a complex social scourge, but cavalierly criminalizing its
Speaker 21: victims is itself a crime that solves nothing. Neil is
Speaker 21: not morally fit to judge poor people, So how about
Speaker 21: replacing him with a homeless person who actually knows something
Speaker 21: about real life. The High Tar Radio Lowdown is made
Speaker 21: possible by youth subscribers to Jim Higtar's Lowdown on Substack.
Speaker 21: Find us at Jimhiitar dot substack dot com.
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Speaker 10: NBC News Radio. I'm Chris Garragio, Pope Francis remains in
Speaker 10: critical condition, with blood tests showing mild signs of kidney failure.
Speaker 10: In an update today, the Vatican noted that the Pope
Speaker 10: had a mild renal insufficiency, which is under control. The
Speaker 10: eighty eight year old pontiff is being treated for double
Speaker 10: pneumonia and is receiving oxygen therapy. The Vatican added that
Speaker 10: Pope Francis continues to be vigilant and well oriented. He
Speaker 10: took part in the Holy Mass from the apartment on
Speaker 10: the tenth floor of the Jamelli Hospital this morning. This
Speaker 10: is the third time in his twelve year papacy that
Speaker 10: he has not delivered the Angelus prayer. Ukraine's president Vladimir
Speaker 10: Zelenski says he's willing to resign in extra change for
Speaker 10: peace or a NATO membership. Lisa Carton reports.
Speaker 14: The Ukrainian president made the offer at a news conference Sunday, saying, quote,
Speaker 14: if it is peace for Ukraine and you really want
Speaker 14: me to leave my post, I'm ready. Zelenski also said
Speaker 14: he would also trade his position for immediate NATO membership
Speaker 14: if it means the safety of his country. This follows
Speaker 14: public disputes with President Trump last week after Trump implied
Speaker 14: Zelensky was responsible for Ukraine's war with Russia and called
Speaker 14: the Ukrainian leader a dictator. Zelenski also insisted that he
Speaker 14: does not intend to stay in power for decades.
Speaker 10: Elon Musk is giving federal workers a deadline of midnight
Speaker 10: tomorrow to justify their work or they'll be fired. In
Speaker 10: a social media post yesterday, Musk said federal employees will
Speaker 10: receive an email requesting information about what they worked on
Speaker 10: over the last seven days, and any failure to respond
Speaker 10: will be taken as a resignation. The move comes after
Speaker 10: President Trump said he'd like to see must be more
Speaker 10: aggressive in his efforts to slash the federal workforce. An
Speaker 10: American Airlines flight that took off from New York was
Speaker 10: as awarded by Italian fighter jets after being diverted to
Speaker 10: Rome for security reasons. The flight was on its way
Speaker 10: to New Delhi when it requested a flight diversion to
Speaker 10: Leonardo da Vinci