Speaker 1: Maximum pain relief. I use it all the time because
Speaker 1: I'm always active, playing golf, working out, fixing up my
Speaker 1: place right now, and I put it on in the
Speaker 1: evening around my back and it gives me maximum pain relief.
Speaker 2: It's amazing.
Speaker 3: You have to get it if you're in pain.
Speaker 1: To find out more about ice bod, go to icebodactive
Speaker 1: dot com and get yours today. This week they are
Speaker 1: having a flash sale where you can save twenty five
Speaker 1: percent off by using the promo code KCAA, plus they
Speaker 1: have free shipping. Go to ice bodactive dot com. You're
Speaker 1: gonna be pain free and you're gonna love it.
Speaker 3: Kc AA Radio, Loma Linda, where no listener is ever
Speaker 3: left behind.
Speaker 2: The information economy has a ride. The world is teeming
Speaker 2: with innovation as new business models reinvent every industry industry.
Speaker 2: Inside Analysis is your source of information and insight about
Speaker 2: how to make the most of this exciting new era.
Speaker 2: Learn more and insight Analysis dot Comside Analysis dot com.
Speaker 2: And now here's your host, through Eric Kavanaugh.
Speaker 4: All Right, folks, hello, and welcome back once again to
Speaker 4: the only nationally syndicated radio show all about the information economy.
Speaker 4: It's called Inside Analysis or truly Eric Kavanaugh is here
Speaker 4: and I'm very pleased to have Arun Baradarajohn. He is
Speaker 4: the founder and chief commercial officer for a company called Assendian,
Speaker 4: and we're going to talk about a new development at Paradigm,
Speaker 4: So just to give some context for that. As anyone
Speaker 4: who has worked with professional services firms know, they typically
Speaker 4: make their money by the hour, and if you want
Speaker 4: to do something new, they're like, oh, we need more people,
Speaker 4: more money, got to check out the price, so we'll
Speaker 4: make more money and give you new stuff. And then
Speaker 4: there's the other side of the equation, which is that
Speaker 4: a lot of times if you want to go faster
Speaker 4: or for less money, they're like, well, it's going to
Speaker 4: cost you in terms of quality, So you have to
Speaker 4: give up something either quality or price, money or ideas,
Speaker 4: like even don't even start your project, as maybe you
Speaker 4: can't afford it. And I think Arun has come up
Speaker 4: with a pretty clever way to address that, and it's
Speaker 4: an end to end engineering platform.
Speaker 5: They figured out.
Speaker 4: There are all these friction points one hundred and fifty
Speaker 4: I think is what I heard him say, friction points
Speaker 4: from idea to production. So what are those and how
Speaker 4: do you solve them while we live in a very
Speaker 4: interesting world these days where you can have teams work
Speaker 4: together on projects without holding each other up. I give
Speaker 4: an example all the time about how Google blindsided Microsoft
Speaker 4: with Google Docs allowing multiple people to work on the
Speaker 4: same document at the same time, which was an absolutely
Speaker 4: brilliant innovation. Saves tremendous amounts of time and effort. You
Speaker 4: don't have to throw things over the wall anymore. You
Speaker 4: can also be looking at the same document, typing, editing,
Speaker 4: and the technology tracks who did what. So you have
Speaker 4: all these wonderful guardrails and safeguards built into the technology,
Speaker 4: and that's what you want. And I think they have
Speaker 4: something like that for a development platform. But with that,
Speaker 4: I run, I'll throw it over to you. Tell us
Speaker 4: a bit about ascending and what are these friction points?
Speaker 6: Absolutely so so, Eric, The Cindy On journey started about
Speaker 6: four years ago, and we had a group of us
Speaker 6: who came from the the overall engineering and software industry,
Speaker 6: and we had realized that there were fundamentally three issues
Speaker 6: that clients were facing. There was there was a crisis
Speaker 6: of trust, there was a crisis of speed, and what
Speaker 6: I call is a crisis of capital.
Speaker 5: The crisis of trust was you.
Speaker 6: Know what, these guys will show up at my door
Speaker 6: and say, hey, don't worry, I'll get this done for you.
Speaker 5: This will be the cost.
Speaker 6: I've got these accelerators, I've got these really good talent.
Speaker 5: I can get this work done for you. And then
Speaker 5: they show up at the door and say that.
Speaker 6: And then when the reality you know what do I
Speaker 6: say manifests, they find that, oh my god, I'm being
Speaker 6: charged more than what I was told.
Speaker 5: You know, I'm not.
Speaker 6: I don't have full transparency into how things are being done.
Speaker 6: I don't know what your engineers are doing. It's a
Speaker 6: black box. I have no clue. The second is a
Speaker 6: crisis of speed. Everybody comes and says, yeah, we will
Speaker 6: accelerate you. We will drive more more value to you.
Speaker 6: And then lo behold, when you say I want to accelerate,
Speaker 6: the first thing the service provider says is you need
Speaker 6: to add another hundred people right that cost.
Speaker 5: And then listen.
Speaker 6: One thing I can tell you is we add more
Speaker 6: people into a software engineering project, it actually slows you down.
Speaker 5: It doesn't.
Speaker 6: It doesn't increase your speed because you add more humans
Speaker 6: into the software process, which is very it's very nebulous,
Speaker 6: not superly defined. You will find that there are more
Speaker 6: errors creeping in and you're busy fixing those errors. Right, Yeah,
Speaker 6: So it's a it's actually a it's actually a negative
Speaker 6: move to add more people to projects. The third verse
Speaker 6: crisis of capital. Now, what is the crisis of capital?
Speaker 6: Over the years, because of the first two issues, clients
Speaker 6: have been building software with a lot of technical problems,
Speaker 6: and we call it technical.
Speaker 5: Debt, a lot of debt.
Speaker 6: So what happens is your capital is locked on these
Speaker 6: legacy old code and you're spending a lot of money
Speaker 6: just maintaining it, and you don't have enough money to
Speaker 6: do new things and innovation.
Speaker 5: And I call that the crisis a capital.
Speaker 6: So all this was creating, in my opinion, a logjam
Speaker 6: and a guardian not as I call it. Okay, which
Speaker 6: the cord is yes, And I said, it's time to
Speaker 6: untangle this. So the thesis statement for Cyndion was, let's
Speaker 6: go back and understand what is causing this problem. And
Speaker 6: to me, I went back to the manufacturing world, and
Speaker 6: I saw how lean manufacturing transformed the entire manufacturing world.
Speaker 5: Right.
Speaker 6: Otherwise, earlier, if you had to do a line change,
Speaker 6: or a die change or a product change.
Speaker 5: It took you days to do it.
Speaker 6: You had issues with product quality, you had a whole
Speaker 6: bunch of things with lean manufacturing, where automation and to
Speaker 6: a large extent, AI fundamentally transform manufacturing. And I said,
Speaker 6: if manufacturing can do it, why are we lagging behind?
Speaker 6: Why can't we do the same thing with software. So
Speaker 6: we went back to the drawing board and really looked
Speaker 6: at the entire engineering value chain, right from ideation through
Speaker 6: to production, through to even post production, and said, what
Speaker 6: is causing these what is causing this friction and problem?
Speaker 6: And to a large extent, I'm sorry to say that
Speaker 6: it is the humans.
Speaker 3: Right.
Speaker 6: If you give a human, if you give four humans
Speaker 6: one requirement saying hey, I want to do this very
Speaker 6: simple requirement. It could be a very two to three
Speaker 6: lines of description of what you want the software to do,
Speaker 6: and you give it to four different engineers, you will
Speaker 6: get four different code.
Speaker 5: Bases for the requirement.
Speaker 6: Sure you will also get ten different bugs that each
Speaker 6: of them will will come up with. And I said,
Speaker 6: this is ridiculous. It's time for us to change. So
Speaker 6: that's when Sindion we was started with the notion that we
Speaker 6: are going to build a platform. The platform is fundamentally
Speaker 6: going to have the ability to support the different actors
Speaker 6: in the engineering value chain, use machine learning and AI
Speaker 6: and try high degree of repeatability, standardization, and really get
Speaker 6: the process. See if you look at the software process today,
Speaker 6: it is all in documents. With the platform, what we're
Speaker 6: doing is we're taking the process and instantiating it into
Speaker 6: a physical platform that will drive the process and ensure
Speaker 6: that there is standardization and scale. That is what we
Speaker 6: have and thanks to all the AI work that has
Speaker 6: been going on over the last two to three years,
Speaker 6: we've been able to take this to a different level
Speaker 6: and I'm happy to talk more about it.
Speaker 3: Yeah.
Speaker 4: Well, so standard components, that's one thing you can focus on, right,
Speaker 4: making making solutions modular. Maybe walk through what are some
Speaker 4: of the foundational components in the platform and maybe explain
Speaker 4: how it is. I'm just guessing here that developers can
Speaker 4: work on different parts of the process without causing each
Speaker 4: other trouble and without delaying each other, right, because that's
Speaker 4: one of the huge challenges is when one group has
Speaker 4: to wait for some other group to finish something and
Speaker 4: then they get delayed. So now these guys are off
Speaker 4: the ranch basically just twiddling their thumbs.
Speaker 3: You don't want that.
Speaker 4: You want everybody working at the same time without disrupting
Speaker 4: each other. What are some of the component parts or
Speaker 4: buckets if you will, of development that you've isolated.
Speaker 6: So we have gone even more radical, right, So our
Speaker 6: radical thinking is that why do we even need humans
Speaker 6: to actually do any of this? And we are thinking
Speaker 6: differently actually saying that. So if you look at our industry,
Speaker 6: we do different things for our lives. So sometimes we'll
Speaker 6: come in and build a new platform for a client
Speaker 6: where we have to take it from idea to production.
Speaker 5: As you mentioned.
Speaker 6: Earlier, all the client will say is and I have
Speaker 6: an old platform. I want you to modernize it. You know,
Speaker 6: the old platform could be written on something like Cobol
Speaker 6: or pl one, which is forty fifty years old old language,
Speaker 6: and they want to move it to a modern construct.
Speaker 6: So there are several such asks from our lives and
Speaker 6: my view was why can't I get AI to do
Speaker 6: all of this? So today, if you look at our platform,
Speaker 6: our platform is what is known as an agent tick
Speaker 6: AI platform, So I literally have an agent that can
Speaker 6: do anything you wanted to do, and I can configure
Speaker 6: this agent to say, okay, agent read this. So a
Speaker 6: requirement in our world is called a user story, basically
Speaker 6: saying this is the story that you will present to
Speaker 6: the user. Right this, this is how the user will
Speaker 6: use that use the software.
Speaker 5: So the user story is a very important input.
Speaker 6: Now I have an agent that will read the user
Speaker 6: story and create the code. I have an agent that
Speaker 6: will read the user story and create the testing that
Speaker 6: needs to be done on the code and execute the testing.
Speaker 5: Right.
Speaker 6: So what we are doing is and with this, what
Speaker 6: we're fundamentally doing, Eric is now when you write a
Speaker 6: user story. If I'm a product manager, my job is
Speaker 6: to write a user story.
Speaker 5: That's my role to write user stories.
Speaker 6: But the funny thing is if I again, just like
Speaker 6: I mentioned earlier about developers or engineers, if I give
Speaker 6: somebody an input to write a user story, and the
Speaker 6: input isically typically.
Speaker 5: Called an epic.
Speaker 6: So if I give you an epic and I say,
Speaker 6: please write user stories for this epic, and if I
Speaker 6: give it to four different product managers, I will get
Speaker 6: the user stories written in four different ways and will
Speaker 6: not cover all of the needs of that epic. Because
Speaker 6: the human can only think so much. Right, But what
Speaker 6: I'm doing with AI is I tell the AI A
Speaker 6: here's the epic, generate user stories, and I have live
Speaker 6: examples where I've taken I've done this time and motion
Speaker 6: study and comparison between what the human output is and
Speaker 6: what the AI output is. Right, So when I give
Speaker 6: a user, when I give an EPIC to one of
Speaker 6: my AI agents and say generate user stories, it may
Speaker 6: generate fourteen fifteen user stories that cover all of the
Speaker 6: aspects of what the system needs to do in the
Speaker 6: first iteration, Whereas if I give that to a human,
Speaker 6: I sometimes see them coming up with seven or eight.
Speaker 6: They're not able to think about the edge, the edge conditions,
Speaker 6: the limit. You know, when you think about software, you've
Speaker 6: got to think about all the eventualities and all the
Speaker 6: possibilities that you may have to encounter as a user,
Speaker 6: and many times as a human mind, we have these limitations. Right,
Speaker 6: that gets taken away, and then I can do this
Speaker 6: at scale instead of having you know, each time, let's
Speaker 6: say I need I have tons of epics, and I
Speaker 6: need to attack all these epics. I need to add
Speaker 6: more product managers. Right here, I just add more agents,
Speaker 6: and I still need product managers because I will have
Speaker 6: them eyeball what the agent creates, taking the human away
Speaker 6: from the loop. But I'm fundamentally saying, hey, if you're
Speaker 6: if I had ten product managers doing the job, maybe
Speaker 6: I just need.
Speaker 7: To m hm and more.
Speaker 5: These product mats are really.
Speaker 6: Improving the improving the agent's ability and efficacy to do
Speaker 6: this even better.
Speaker 4: So if you don't mind my asking the the agents
Speaker 4: that you've designed, how what what language did you.
Speaker 3: Use to design them?
Speaker 5: How do they run?
Speaker 4: They do they run in containers? Is this a containerized environment?
Speaker 4: Tell me a bit about that.
Speaker 6: So our platform is completely containerized. Okay, follow all the
Speaker 6: modern methods. It's a platform. So our platform allows you
Speaker 6: to create agents, and I can create an agent for
Speaker 6: different use cases. So somebody may come to me, Like
Speaker 6: one of my clients came to me the other day
Speaker 6: and said, listen, I've got these Pearl scripts. Now peerl
Speaker 6: is again one of the old scripting languages that nobody
Speaker 6: uses anyone, right, So he said, I've got Pearl scripts.
Speaker 6: I really want to convert them into Java. And he said,
Speaker 6: I'm trying to get my engineers to do it. It's
Speaker 6: taking way too much time. And I've got tons of
Speaker 6: Pearl scripts. I've got these four hundred applications running in
Speaker 6: my data center and I want to move them to
Speaker 6: the cloud, and I can't really use PELS scripts if
Speaker 6: I move into the cloud. I want to move it
Speaker 6: to the new language Java. In the old days, what
Speaker 6: we would have done is we would have tried to
Speaker 6: use these code converters that will take the peerl scripts
Speaker 6: and converted into whatever language that is not working for
Speaker 6: us anymore because we want to move to micro services
Speaker 6: and containized component based applications. What we're doing is we
Speaker 6: have changed the process. So I have an agent which
Speaker 6: goes and interestingly I put two of my principal engineers
Speaker 6: with our platform and we were able to get this
Speaker 6: done in weeks for our clients who actually took months
Speaker 6: to try and do this. So what we did was
Speaker 6: we had what is known as a reverse engineering agent
Speaker 6: that came in and read the Pearl scripts and understood.
Speaker 6: And these Pearl scripts can be very messy. They can
Speaker 6: have routines. I mean, I'm not going to get super
Speaker 6: I don't know how technical I can get here, but fundamentally,
Speaker 6: when you have these scripts, these they call each other
Speaker 6: there's a lot of linkages here and there. Right, So
Speaker 6: we actually put an agent that went and read all
Speaker 6: of this and generated what we call is a simple
Speaker 6: ascendion language, that's what we call it, which is just
Speaker 6: English and nim on that describes the Perl script's logic.
Speaker 6: And then I take that, and then I get another
Speaker 6: agent to come in and convert that language, and I
Speaker 6: go and tell the client, does this logic make sense
Speaker 6: to you? Right? Forget about the Pearl scripts, none of
Speaker 6: us know how to read it. But does the logic
Speaker 6: of what is getting done in the Perl script make.
Speaker 5: Sense to you?
Speaker 6: Says yeah, this part makes sense. This one I don't
Speaker 6: even think I use. This part looks good. So I'm
Speaker 6: able to also say, you know what, I don't even
Speaker 6: need to waste money converting these Pearl scripts because they.
Speaker 5: Are no longer used.
Speaker 6: So I'm now a document that is super efficient that
Speaker 6: I reverse engineered, so I can now forward engineer it
Speaker 6: whichever way you want. So I can take this document
Speaker 6: convert them again into what I call as user stories,
Speaker 6: which is the input, as I said, the requirement input
Speaker 6: for development. And once I create the user stories, I
Speaker 6: can I have an agent that converts one user's story
Speaker 6: into Java. I can have one agent that can convert
Speaker 6: it into c SHAP or whatever you want, and moving
Speaker 6: completely into micro services architecture. And while I'm doing that,
Speaker 6: in parallel, I have agents that can create the test
Speaker 6: scripts and I can automate the testing too. So with
Speaker 6: what is happening, Eric is, I'm my teams and I
Speaker 6: we are completely reimagining how.
Speaker 5: Work gets done right. There's no need for rework. Now.
Speaker 5: The biggest I'll.
Speaker 6: Tell you thirty person of project costs is rework because
Speaker 6: the developer didn't understand the user story. The used story
Speaker 6: was not written correctly because the product manager did not
Speaker 6: understand the user needs and did not elaborate the scenario.
Speaker 6: As well, the testers did not test the software correctly.
Speaker 6: These are all all the pitfalls we are eliminating by
Speaker 6: running these agents and bringing in high degree of standardization
Speaker 6: and scale.
Speaker 4: That's very interesting. You know, I understand conceptually what you're
Speaker 4: talking about. Did you use jen Ai for this reverse engineering?
Speaker 4: Is that how you loaded a Pearl script? You said, hey,
Speaker 4: what is this thing actually doing? Was that a Genai application?
Speaker 4: We got one minute left in this segment, Go ahead,
Speaker 4: we use.
Speaker 6: A combination of GENI and other AI. So we use
Speaker 6: machine learning, we use you know, deep learning, and we
Speaker 6: use GENI depending on the type of problems we're.
Speaker 5: Trying to solve.
Speaker 6: With GENAI, we've been able to take this to the
Speaker 6: next level.
Speaker 4: Mm hmm, well I can imagine. I mean I remember
Speaker 4: playing around with it whenever it came out a year
Speaker 4: and a half ago or so, and immediately drawing the
Speaker 4: conclusion that if it can write in French and German
Speaker 4: and Spanish and English, that you can probably write in
Speaker 4: cobal and C sharp and C plus plus and Java
Speaker 4: and these other languages. And guess what it can. You know,
Speaker 4: my understanding, having researched this a good bit is that
Speaker 4: it'll get you eighty percent of the way there typically,
Speaker 4: and then the last twenty percent is the mile you
Speaker 4: have to do fine tuning, manually checking things.
Speaker 5: You do want to make.
Speaker 4: Sure that, as you've already suggested, you have a human
Speaker 4: being monitoring things, making sure that the outputs are accurate.
Speaker 4: But the beautiful thing about software that works is that
Speaker 4: it works, and if it doesn't work, then it doesn't work.
Speaker 4: And you know very clearly in a binary fashion, is
Speaker 4: this accomplishing the task or not. And if it's a no,
Speaker 4: you have to go back to the drawing board. But
Speaker 4: this very interesting stuff, well, don't touch one, folks. We'll
Speaker 4: be right back talking with ascending about a new way
Speaker 4: of doing software. It's a lot faster and probably a
Speaker 4: lot more efficient. Will get into the details in our
Speaker 4: next segment.
Speaker 3: We'll be right back.
Speaker 4: You're listening to Inside Analysis.
Speaker 5: React.
Speaker 2: Welcome back to Inside Analysis. Here's your host, Erica.
Speaker 4: All right, folks, back here on Inside Analysis talking to
Speaker 4: Ruin Varadarajan. He is the CCO and founder of a
Speaker 4: company called Ascendian. It's just like it sounds ascend with
Speaker 4: io n at the end. And they have developed a
Speaker 4: do a platform for software development that is agentic. They
Speaker 4: have all sorts of AI agents out there.
Speaker 7: Now.
Speaker 4: This is the talk of the town in Silicon Valley
Speaker 4: and around the world quite frankly, because if a machine
Speaker 4: can do a job better than a human, let the
Speaker 4: machine do the job. I mean, humans make mistakes. The
Speaker 4: machines make mistakes too, Jennai we've talked about famously makes mistakes.
Speaker 4: It generates things, so you have to do all sorts
Speaker 4: of work to train it and to ground it. Basically,
Speaker 4: but that's not really what we're talking about here with
Speaker 4: this agentic stuff, though, you still want to monitor what
Speaker 4: they do and guide them. And frankly, this is my
Speaker 4: big question about agentic AI is how do we monitor
Speaker 4: their behavior, correct their behavior when they're wrong? How do
Speaker 4: we orchestrate their behavior? Because you've got a bunch of
Speaker 4: little guys out there doing stuff? Now do the overlap?
Speaker 3: Is it log.
Speaker 4: Files that gets spun out of these things? How do
Speaker 4: you actually know what they've done to where you can
Speaker 4: correct them or optimize what they're doing?
Speaker 5: Amazing, amazing question, unbelievable question.
Speaker 6: So what we are when we first of all, let
Speaker 6: me explain to you what is an agent architecture?
Speaker 5: Okay, yeah, complete, Right.
Speaker 6: So what we have done is in our we can
Speaker 6: build agents for anything under the sun. Right, but we
Speaker 6: decided as a company that we're going to focus on
Speaker 6: building this agentic platform around one value chain, which is
Speaker 6: software engineering, because we said we want to be super
Speaker 6: specialized in this area.
Speaker 8: Mm hm.
Speaker 6: So if you really look at an agent, an agent
Speaker 6: has got five elements to it. First element is it
Speaker 6: has what is known as a prompt template. Okay, that's
Speaker 6: where you tell the agent, What, what is your goal?
Speaker 6: What is your purpose? What am I expecting you to do?
Speaker 6: What should you? How should you operate? How should you
Speaker 6: how should you write the code? How should you write
Speaker 6: the user story? I'm telling it a lot of things, right,
Speaker 6: So I've started defining the framework around which it needs
Speaker 6: to operate. The second element of the agent architecture is
Speaker 6: the model. In our platform, I can literally use any model.
Speaker 6: I can use anthropic, I can use you know as
Speaker 6: your open AI, I can use lama from from you Know,
Speaker 6: from meta. We can use different models, and we have
Speaker 6: our own viewpoints on what models work best for what
Speaker 6: type of use cas So our platform allows customers to say, hey,
Speaker 6: I've got a relationship with AWS, so they can use
Speaker 6: aws's bedrock services to choose the model, but we help
Speaker 6: them with model selection. So that was That is a
Speaker 6: second element of the agent. The third element of the
Speaker 6: agent is what I call as its memory and its knowledge.
Speaker 5: Right.
Speaker 6: That is where I feed the agent with all of
Speaker 6: the knowledge it needs. For example, when I'm writing code
Speaker 6: for a client, I may be writing a code on
Speaker 6: authentication the client. My client may already have an authentication
Speaker 6: framework saying that go get the entitlements from this server,
Speaker 6: go get the access rights from this IDP or whatever.
Speaker 6: I can teach all of that to the agent so
Speaker 6: that the agent does not go and generate generic stuff.
Speaker 6: I can also teach the agent on all of the
Speaker 6: technical standards, the kind of language that they use when
Speaker 6: they write something, so it has this what we call
Speaker 6: as in context learning or its knowledge base. That is
Speaker 6: the third part of the agent. The fourth part of
Speaker 6: the agent is where we define guards because that's very important.
Speaker 6: If you're writing, for example, an application we're using things
Speaker 6: like social security number, et cetera. You want to make
Speaker 6: sure that the application does not expose the social security number, right,
Speaker 6: it still maintains the format social security is, you know,
Speaker 6: three four three, So it maintain the format, but I
Speaker 6: don't want it to be exposed. Or you may have
Speaker 6: certain ethical standards. You may say listen, when I design,
Speaker 6: I don't want to think about race, or when I
Speaker 6: think about design, I want to be inclusive. All of
Speaker 6: those things can be put in the guard rails, and
Speaker 6: we use a guardrail framework. And the fifth is every
Speaker 6: agent can be associated with tools, because I may have
Speaker 6: an agent that needs to use a tool to get
Speaker 6: something done right. It may have to use a testing tool,
Speaker 6: or it may have to use a webscraping tool. So
Speaker 6: this is the architecture of an agent. Now what we
Speaker 6: do is we have a we are when I am
Speaker 6: building an agent, we actually have an agent that helps
Speaker 6: me build an agent.
Speaker 3: Nice.
Speaker 6: And we are also starting to use genetic algorithms to
Speaker 6: really improve the efficacy of an agent for a given
Speaker 6: use case.
Speaker 4: And what was that again? You use what genetic algorithms
Speaker 4: or evolution genetic algorithms?
Speaker 6: Yeah, yeah, So what we are doing with genetic algorithms
Speaker 6: is if I have a use case and I have
Speaker 6: two let's say agent candidates, I input it into my
Speaker 6: into my GA algorithm, and it will generate children.
Speaker 5: And you know how GA.
Speaker 6: I don't know if you have presuming that you and
Speaker 6: your audience know how generic algorithm works. But it works
Speaker 6: just like human evolution, right, It creates, it creates multiple
Speaker 6: strains of the of the agents, and multiple versions of
Speaker 6: the agents, multiple generations and children's and whatnot. And what
Speaker 6: we do is we have what is known as a
Speaker 6: fitment function, right, or a fitness function that we have
Speaker 6: defined for that use case, and the fitness function will
Speaker 6: decide which of those children go to the next generation
Speaker 6: literally like you know, evolution, and lead that method to
Speaker 6: come up with a high quality agent candidate for a
Speaker 6: given use case.
Speaker 4: Okay, I mean you're so. Just to give some context
Speaker 4: to the listeners, what you're talking about is kind of
Speaker 4: what they have in the in the database world of
Speaker 4: a query optimizer, right. A query optimizer figures out how
Speaker 4: to do the job better, faster, more accurately, for example,
Speaker 4: pulling data from various systems, and you can do There
Speaker 4: are all kinds of different machine learning algorithms out there,
Speaker 4: so you can do You can use different means to
Speaker 4: determine how efficient is this could be more efficient. To
Speaker 4: what you're saying is that basically these genetic algorithms, you
Speaker 4: have a sort of a fleet of them. You use them,
Speaker 4: see which ones work better than others. The ones that
Speaker 4: work best, you continue that one, you don't continue the others,
Speaker 4: and thus you are incrementally improving these individual agents and
Speaker 4: the algorithms that they use.
Speaker 5: Is that right, correct?
Speaker 6: And this is in the beginning when I'm building an agent, right,
Speaker 6: So when I'm right off the bat, I have a
Speaker 6: very high propensity for being accurate in the agent's capability
Speaker 6: to do something. So right off the bat, we are
Speaker 6: starting with good quality agents through our process. Then we
Speaker 6: also run a whole range of analytics. So every time
Speaker 6: you know, when the agent does something, Let's say the
Speaker 6: agent writes a piece of code for a developer and
Speaker 6: the developer looks at it and says I like it
Speaker 6: and clicks says like that goes back into my platform
Speaker 6: and I'm capturing it. So we call it answer relevance.
Speaker 6: So if an engineer says, test this code for me,
Speaker 6: or optimize this code for me or whatever, and if
Speaker 6: it likes, and if the engineer likes the output of
Speaker 6: the agent, then that goes back as analytics. And I'm
Speaker 6: constantly monitoring the analytics of these agents to.
Speaker 5: Improve either the prompt or.
Speaker 6: Give it more learning and improve its knowledge base, or
Speaker 6: fight you in the guardrails, or create or implement new tools.
Speaker 6: And this is an evolving process that we fall right.
Speaker 4: And that's largely through user provided feedback. Is that correct
Speaker 4: or is some of the feedback also agentic?
Speaker 5: So very good point.
Speaker 6: What we also are doing is because we have an
Speaker 6: so literally what we're doing is we're mimiking the workworld
Speaker 6: in our platform. So we have two types of agents.
Speaker 6: We have a manager agent and we have co worker agents.
Speaker 6: So the manager agent is actually managing all the co workers,
Speaker 6: and he or she the manager she may deside that
Speaker 6: the same piece of work I'm going to give to
Speaker 6: two agents and I'm going to see who comes back
Speaker 6: with a better result. Right, So we can even have
Speaker 6: two agents doing the same piece of work and find
Speaker 6: out which is more effective and take that output too.
Speaker 6: So that is something that we so we call them
Speaker 6: workflows in our in our platform. So I can literally
Speaker 6: create a workflow where I can concatenate multiple agents and
Speaker 6: put a master agent on top and say, okay, go
Speaker 6: build this piece of software. So one so the first
Speaker 6: agent may just take the epic, understand the epic, generate
Speaker 6: the user stories the mask. The manager agent may say,
Speaker 6: I don't like that. I'm going to get another agent
Speaker 6: to come and do the same and see which one
Speaker 6: is better. And then once he or she is happy
Speaker 6: with the work that was done in terms of building
Speaker 6: the user stories, she can invoke and we can use
Speaker 6: she and he interchangeably, because these are you know, yeah, agents,
Speaker 6: I presume, So it can go and then kick off
Speaker 6: the next set of agents. It and if it finds
Speaker 6: that the work needs more agents, our platform allows the
Speaker 6: mask the manager agent, to spawn more agents so that
Speaker 6: more agents can attack the work.
Speaker 5: So this is how the whole thing.
Speaker 6: Can So we foresee at some point in time, as
Speaker 6: we improve the efficacy of solving different problems for different clients,
Speaker 6: we could potentially get to a true factory model like
Speaker 6: you see in a process chemical plant where the process
Speaker 6: is running and there's one guy or two people sitting
Speaker 6: in a in a in a room with a cockpit,
Speaker 6: tuning this and turing that and tuning and looking at
Speaker 6: gauges and letting the factory run the process.
Speaker 4: Sure, sure, no, Well you're reminding me of these transformers, right,
Speaker 4: the whole concept of the transformer. And I actually have
Speaker 4: a thread going with Jurgen schmidi Huber to hopefully come
Speaker 4: on my show someday, and we were talking about this,
Speaker 4: and it's the same kind of thing where you have
Speaker 4: an array of agents or automatons let's call them, and
Speaker 4: there's one that manages them and thinks through. It's like, huh,
Speaker 4: because like if you think about how these large language
Speaker 4: models work, from what I understand, it's still next character
Speaker 4: basically next token, what is most likely based upon the
Speaker 4: prompt of my training, et cetera, which is a very
Speaker 4: linear process. But what the transformers. Now, you've got a
Speaker 4: sort of supervisor who can kind of look over you
Speaker 4: go hmm, I think I'll go with you this time
Speaker 4: and you that time, which I think is very clever.
Speaker 4: And that's I think also reflected in mistral right with
Speaker 4: this mixture of experts, that that whole architecture, yes, is
Speaker 4: very similar, and that you have some agent that is
Speaker 4: basically orchestrated in the behavior of a group of agents
Speaker 4: and they all work as a team. Is that about right?
Speaker 5: Exactly exactly? Now.
Speaker 6: The interesting thing is now think about what I told
Speaker 6: you in the in the initial part and our thesis
Speaker 6: statement of why we started doing this. If my client
Speaker 6: comes to me and says I gave you three files,
Speaker 6: three Cobal files to move to Java, I'm now going
Speaker 6: to give you one thousand files. All I need to
Speaker 6: do is to run parallel workflows.
Speaker 3: Yeah, right, sure, and I do.
Speaker 4: These GPUs are made for right.
Speaker 5: Right, And I don't need to bring in more humans.
Speaker 6: I just need to have, right, you know, maybe ten
Speaker 6: maybe five ten humans who are looking at the process
Speaker 6: so I've told so, if you're a let's say you're
Speaker 6: a you're a developed let's say you're a senior software engineer,
Speaker 6: what does this mean to you, because your role is
Speaker 6: going to change. What I'm telling my senior software engineers
Speaker 6: and my principal engineers is earlier you would work on
Speaker 6: writing the code for your client and optimizing the colde
Speaker 6: and right.
Speaker 5: Right, that's what you were doing.
Speaker 6: Now what I want you to do is to make
Speaker 6: these agents more effective in doing that for your clients.
Speaker 4: Right, Well, I'll throw a theory at you. I think
Speaker 4: you're going to agree with this because I think a
Speaker 4: lot about AI and the impact on workflow, on day
Speaker 4: to day lives, on how we actually build things in
Speaker 4: particular software, and as a general rule, I think we're
Speaker 4: at a massive inflection point. It's an inversion really, because
Speaker 4: in the past we push the machines like think a lawnmower.
Speaker 4: I'm pushing it along the way, using my strength and
Speaker 4: guiding it myself. Well in the now, in the near future,
Speaker 4: it's turned around where the machines are sort of pushing us,
Speaker 4: but we're still guiding them. We're still trying to shepherd them.
Speaker 4: Say no, not this, or yes, more of that. So
Speaker 4: but the point is that the impetus is now coming
Speaker 4: from the machines. They're creating these things, and we're just
Speaker 4: sort of shepherding and guiding them, almost like a gardener
Speaker 4: does in pruning a tree.
Speaker 5: What do you think totally?
Speaker 9: So?
Speaker 6: In fact, I'll give you a very interesting example. There
Speaker 6: is a role in the software engineering world calls it
Speaker 6: a tester or a quality engineer. So his job is
Speaker 6: to test or her job is to test something. So
Speaker 6: think about now, what's going to happen to her role.
Speaker 6: So I've told her now I want you to stop
Speaker 6: thinking yourself as a quality engineer. I want you to
Speaker 6: think yourself as a quality systems engineer. What does that mean?
Speaker 6: You are going to run the quality system. I'm not
Speaker 6: going to do the quality engineering. You're going to run
Speaker 6: the quality system. So you should know how to engineer
Speaker 6: your quality system so that you can ensure and if
Speaker 6: you know, you know, I still go back to manufacturing,
Speaker 6: because these guys have cracked it.
Speaker 5: We've got to go back to manufacturing.
Speaker 6: They have this notion called total quality management where they
Speaker 6: go to your supplier supplier all the way through to
Speaker 6: the raw material to make sure that every part of
Speaker 6: the inbound value chain is bringing in the quality for
Speaker 6: you to build the right kind of ball bearings.
Speaker 5: Or whatever you're doing.
Speaker 10: Right.
Speaker 6: Sure, so with this quality system, So today, most of
Speaker 6: my testers are only trying to test what the developers
Speaker 6: are done. They're not testing to a large extent, what
Speaker 6: the product.
Speaker 5: Managers are writing. You're not how the users are thinking.
Speaker 4: Oh interesting, now this is this is very important because
Speaker 4: what you're speaking to is the criticality of understanding the
Speaker 4: whole process end to end. And we talk a lot
Speaker 4: about this on these various shows. Things like error propagation.
Speaker 4: If it gets all the way upstream, it just spreads
Speaker 4: to the entire environment. And now it's like tainted water.
Speaker 4: For example, if you get some poison in the water,
Speaker 4: if it's in the distributions system, then it's everywhere and
Speaker 4: you've got a huge problem. Well, if you stop it
Speaker 4: at the source, that's when you really get make some Hey,
Speaker 4: but folks, don't touch a dewel.
Speaker 10: Be right back.
Speaker 4: You're listening to the Inside Analysis.
Speaker 2: Welcome back to Inside Analysis. Here's your host, Eric Tabanac.
Speaker 4: All right, folks, back here on a fascinating Inside Analysis episode.
Speaker 4: We're talking to our own Baradarajen. He is the chief
Speaker 4: commercial officer and founder, and they have built an agentic
Speaker 4: platform that allows their clients to leverage the power of
Speaker 4: agentic AI. What is that the little AI agents? There
Speaker 4: are little semi autonomous mini applications that can do various things.
Speaker 4: And I'll throw this over to you. What I've heard
Speaker 4: from lots of companies and who are doing something similar
Speaker 4: to this. Variant for example, has automatons. They have like
Speaker 4: I think twenty of them now and they plan to
Speaker 4: have another twenty soon. And what their CEO told me
Speaker 4: is that they want each agent to do one thing
Speaker 4: very well, because I had asked them, do you want
Speaker 4: the agents to learn to do multiple things? And he
Speaker 4: basically said, not really unless you're talking about the orchestrating agent,
Speaker 4: sort of the manager agent that has the bigger picture
Speaker 4: in mind. And if all this stuff is declarative in nature,
Speaker 4: that's great, right, because you have an end state you
Speaker 4: want the agents to achieve. The manager is watching to
Speaker 4: see how that gets done, is instructing the individual agents
Speaker 4: to do their thing.
Speaker 3: Go get this.
Speaker 4: Data, you process this data, you check the data, you
Speaker 4: double check the data.
Speaker 3: You push it to production.
Speaker 4: All these different agents get their instructions right, and you
Speaker 4: can parallelize that stuff like for go get that data,
Speaker 4: spin up one hundred agents to go pull in a
Speaker 4: big large amount of data and then start analyzing it,
Speaker 4: acting on it, but pushing it production, et cetera. That's
Speaker 4: all declarative, right, And then again my question is how
Speaker 4: do you know, how does the real person manage that stuff?
Speaker 4: What kind of levers can they pull to change what's happening?
Speaker 5: So to me, actually I don't fully agree on that notion.
Speaker 4: Okay, that's fine.
Speaker 6: I actually think while I agree with that, you need
Speaker 6: to have an agent kind of focused on an area.
Speaker 5: I wouldn't use the word task alone a space.
Speaker 6: You want to use the generative capabilities of AI today
Speaker 6: because I can tell you all we want to do
Speaker 6: is to create some boundaries and say, listen, thou shall
Speaker 6: operate in this space and let me give you all
Speaker 6: the knowledge you need to be effective in this space.
Speaker 6: So we expect actually our agents to bring in a
Speaker 6: high degree of creativity and I'll show it. I'll show
Speaker 6: it to you when I show you the platform. Because
Speaker 6: what's happening is when my manager agent tells, hey, you
Speaker 6: agent write the code. When she comes back with the
Speaker 6: code and says, here's my code. I'm ready to ready
Speaker 6: for you to move to the next step, he says, no,
Speaker 6: I don't like some of this.
Speaker 5: Change it.
Speaker 6: You have not addressed some of these things, and it
Speaker 6: goes and explores that and improves it.
Speaker 7: So interesting.
Speaker 5: So what we are.
Speaker 6: Thinking is that these agents need to use reasoning because
Speaker 6: there are decisions that need to be made. Should I
Speaker 6: write it this way versus that way? So we want
Speaker 6: the reasoning abilities, which is what agent Kai does. It
Speaker 6: really leverages the reasoning ability, the reasoning abilities, because you
Speaker 6: need that.
Speaker 7: Mm hmm.
Speaker 4: I mean that's what humans do best, right, I mean,
Speaker 4: right now, that's our I think our leg up on
Speaker 4: the AI agent's at the moment is that we really
Speaker 4: can think through you look at this story about was
Speaker 4: it Apple's new chip? They have a new quantum chip.
Speaker 4: I think it was that solved some problem like a
Speaker 4: like a million times fast than it ever would have
Speaker 4: been solved before. And it's because you have sort of
Speaker 4: multiple layers of things, right. I mean, I've given the
Speaker 4: example in the past of Carl Sagan when I was
Speaker 4: a kid, blew my mind because he talked about two
Speaker 4: dimensional characters and a two dimensional world and how they
Speaker 4: can't see past each other. But if someone came along
Speaker 4: and picked one of them up into the third dimension,
Speaker 4: now they can look down and see everything. And that
Speaker 4: is huge. I mean, think about getting through a labyrinth.
Speaker 4: If you can get up in the air and look
Speaker 4: down at the labyrinth, now you can figure out exactly
Speaker 4: how to get out of there, whereas before it would
Speaker 4: have been a very painful trial and error type environment.
Speaker 4: And I think what you're hinting at here, or even
Speaker 4: openly articulating, is that you know, if you have the
Speaker 4: right array of agents and the right architecture, then they
Speaker 4: can all sort of check and balance each other and
Speaker 4: much more strategically solve problems. Is that right?
Speaker 5: Very true?
Speaker 6: We have an agent's In fact, we have the pure
Speaker 6: agents talking to each other already, and the manager agent
Speaker 6: comes in and just kind of draws some boundaries and says, hey, guys,
Speaker 6: you know, let's not go crazier, right, say within this back.
Speaker 6: If I find that you're not giving me the output
Speaker 6: that I want, I want you to go back and
Speaker 6: nerate again. Now the thing is as we are, so
Speaker 6: in fact, when I sit down with my team. We
Speaker 6: are not trying to solve the simple problems that you
Speaker 6: know these RPA type of companies are doing right, because
Speaker 6: robotic process automation is very deterministic in go fetch data,
Speaker 6: my agents can do that, but I'm not really I
Speaker 6: want to solve the tougher problems. So for example, if
Speaker 6: you take I'll go back to testing one of the
Speaker 6: starting points, and testing is I will go to the
Speaker 6: to the development team and say, hey, guys, what are
Speaker 6: you going to be building in the next round? They
Speaker 6: call it sprints. You know this agile world. By the way,
Speaker 6: Agile is also going to go away with all of this.
Speaker 6: I will tell you at another you know, it's called
Speaker 6: the point. It's going to die.
Speaker 5: Now.
Speaker 6: Now what's going to happen is I'm going to go
Speaker 6: as a test manager or whatever and ask the development
Speaker 6: team what are you going to build in the next round?
Speaker 6: Because why is that important? Because I need to now
Speaker 6: figure out what should I test? Where should I test? Right,
Speaker 6: it's important because I may be building some things that
Speaker 6: are new, I may be changing things that are already
Speaker 6: in existence.
Speaker 5: Right, so I'm getting it.
Speaker 6: I actually have an agent now where I can get
Speaker 6: your your sprint plan. I can get you know, what
Speaker 6: are you planning to do? I can get your schedule
Speaker 6: that you have. I can get a whole bunch of things,
Speaker 6: and then the agent is able to generate saying this
Speaker 6: is the testing you need to do. That's not like
Speaker 6: going and fetching a piece of data. That is the
Speaker 6: reasoning power.
Speaker 7: Right.
Speaker 6: I'm saying over time that things like design architecture. So
Speaker 6: when I used to when we used to build systems,
Speaker 6: there's a lot of time we used to spend around
Speaker 6: architecture and design because there are a lot of decisions
Speaker 6: that need to be made that have implications on various aspects.
Speaker 6: It could be performance, it could be you know, user experience,
Speaker 6: it could bee hundred things. And for the human mind
Speaker 6: to do to really do trade offs beyond four or
Speaker 6: five dimensions is not possible do right, I think like
Speaker 6: that we're not some of us are lateral thinkers, but
Speaker 6: we may miss some of these things. So those areas
Speaker 6: where you really need to think about design and and
Speaker 6: really come up with what works best.
Speaker 5: I think these agents are going to do better than us.
Speaker 4: Wow, No, I can. I can say that because when
Speaker 4: I think about how computer systems are working now. I mean,
Speaker 4: first of all, we have just wild, crazy innovation in
Speaker 4: all directions. I mean these deep learning modules like you
Speaker 4: see Gemini and Claude and all these various things. I
Speaker 4: try to explain to people there is no limit to
Speaker 4: the number of permutations for how these things can be built,
Speaker 4: and they can even be dynamic and change over time
Speaker 4: and sort of readjust you know, dynamically adjusted software defined
Speaker 4: software development. That's kind of what it falls down to, right,
Speaker 4: And that's what you're what you're getting at. And because
Speaker 4: so whenever we talk next, I want to get into
Speaker 4: the details of the reasoning of these things and how
Speaker 4: you score and manage the scores of all the agents
Speaker 4: in certain environments for certain tasks. Because if you are
Speaker 4: incrementally inching closer to more and more efficient design, that's
Speaker 4: very very very interesting. I mean, right now, if you
Speaker 4: I could just make a blanket statement and I'll give
Speaker 4: you ninety seconds to comment done, and if you think
Speaker 4: about the compute that happens in any large organization. I
Speaker 4: mean VMware came along and optimized that to a large extent, right,
Speaker 4: optimize the use of CPU and things of this nature,
Speaker 4: which is important, but all in all, if you think
Speaker 4: about all the unnecessary crunching of data and processes that
Speaker 4: are not generating value, not even needed, we're probably at
Speaker 4: like twenty percent efficiency, I think. And so if you
Speaker 4: get this right, you can save Like we're talking trillions
Speaker 4: of dollars at scale, we've got a minute, thirty seconds.
Speaker 4: What are your closing dots?
Speaker 5: A one hundred percent?
Speaker 6: Let me tell you the first market I want to
Speaker 6: disrupt is a there's a low hanging fruit. And I
Speaker 6: know I'm going to piss off some of my peers
Speaker 6: in the industry, which I'm fine doing because that's why
Speaker 6: we're here for. They are making oodles of money and literally,
Speaker 6: you know, taking the clients to ransom around testing.
Speaker 5: You will be amazed.
Speaker 6: These companies have thousands of people testing software e to
Speaker 6: day and some of these companies, I mean, I don't
Speaker 6: want to name them because I'm not here to necessarily
Speaker 6: shame them, but they have to really reflect some of
Speaker 6: these large firms and you know the names. I don't
Speaker 6: need to tell you. They make billions of dollars in testing.
Speaker 6: And you know how do they do the testing? They
Speaker 6: just put butts on seat butts on, butts on, butts
Speaker 6: on seat.
Speaker 5: That's all going to do.
Speaker 6: And let me tell you with this model, I think
Speaker 6: in fact, when I started essenting on, my notion to
Speaker 6: the team was I think CIOs are spending let's say
Speaker 6: Seattle's are spending one hundred dollars. I don't think they
Speaker 6: should spend more than fifteen dollars.
Speaker 4: Yeah, I'm with you. I mean, I totally see this,
Speaker 4: and we're going to pick this conversation up at a
Speaker 4: future show. I am sure, wo folks, look these folks
Speaker 4: up online. Ascending in I think is ascendant. I'm pretty
Speaker 4: sure about that. They certainly have the right idea because
Speaker 4: you can optimize the freaking daylights out of what is
Speaker 4: being done in the world of computing these days. I'm
Speaker 4: talking absolutely massive gobsmacking savings if you do it right,
Speaker 4: and it'll be vastly more efficient.
Speaker 5: We'll talk to you next time.
Speaker 4: Folks, you've been listening to Inside Analysis.
Speaker 3: We supply the words you paint the picture KSEAA.
Speaker 11: And these times sometimes you have to take away the stress.
Speaker 11: Are dressed to impress. Lux Transportation has just a ticket
Speaker 11: for you and your niedes to relax, kick your feet
Speaker 11: up and style and makes new fund memories looking for
Speaker 11: a hassle free, comfortable ride experience the ultimate luxury transportation
Speaker 11: and as sleek luxurious black on black suv private Discrete
Speaker 11: Rise Airport, runs, sightseeing, proms, weddings and Romanta date nights
Speaker 11: are all ahead in your future with Lux Transportation, karaoke,
Speaker 11: disability friendly and no problem. We also senior citizen friendly too.
Speaker 11: Call it text mister Holland is to take away the
Speaker 11: stress at nine five to one three nine nine five
Speaker 11: four two five. That's nine five one three nine nine
Speaker 11: five four to two five Lux Transportation. When you need
Speaker 11: the absolute best?
Speaker 3: Are you looking for a good union job? The Inland
Speaker 3: Empires fourteen thousand members strong Teamsters Local nineteen thirty two
Speaker 3: has opened a training center to get working people trained
Speaker 3: and placed in open positions in public service, clerical work
Speaker 3: and in jobs in the logistics industry. This is a
Speaker 3: new opportunity to advance your career and raise standards across
Speaker 3: the region. Visit nineteen thirty two Training Center dot org
Speaker 3: to enroll today. That's nineteen thirty two Training Center dot org.
Speaker 11: And these times sometimes you have to take away the
Speaker 11: stress or dress to impress. Lux Transportation has just the
Speaker 11: ticket for you. And your niches to relax, kick your
Speaker 11: feet up in style and makes new fund memories. Looking
Speaker 11: for a hassle free, comfortable ride experience, the ultimate luxury
Speaker 11: transportation and a sleek, luxurious black on black suv. Private
Speaker 11: discrete rides, airport runs, sightseeing, proms, weddings and romantic date
Speaker 11: nights are all ahead in your future with lux transportation, Kriok,
Speaker 11: disability friendly and no problem. We're also senior citizen friendly too.
Speaker 11: Call it text mister Holland is to take away the
Speaker 11: stress at nine five to one three nine nine five
Speaker 11: four two five. That's nine five one three nine nine
Speaker 11: five four to two five lux transportation when you need
Speaker 11: the absolute best.
Speaker 12: This important, time sensitive message is brought to you by
Speaker 12: this station's sponsor, George Ltzfield Associates, who has important Medicare
Speaker 12: information for all current and future Medicare recipients about some
Speaker 12: big changes happening Medicare Clarified. Medicare is a nonprofit consumer
Speaker 12: service organization.
Speaker 13: It's more important than ever to review your Medicare plan
Speaker 13: for twenty twenty five from October fifteenth through December seventh
Speaker 13: to find out if you're in the right plan for you.
Speaker 13: People are calling nine five one seven six nine zero
Speaker 13: zero zero five nine five one seven six nine zero
Speaker 13: zero zero five A popular and local Medicare plan is improving.
Speaker 13: Others are raising copays and adding deductibles, biggest changes in
Speaker 13: the Medicare drug program in fifteen years.
Speaker 12: We thank George Letzfield and Letsfield Insurance for their generous
Speaker 12: support of this radio station.
Speaker 14: Hi, I'm Lannie swood Wote and I'm back on KCAA
Speaker 14: ten fifty eight and Express one oh six point five
Speaker 14: FM every Tuesday at eight pm. My show is beyond
Speaker 14: common sense. It's Lanny Sense, featuring me Lanni Swardlow, kcia's
Speaker 14: resident gay, Jewish liberal, potsmoking, race mixing, left handed atheist,
Speaker 14: an evangelical, fundamentalist, Christian nationalist, worst nightmare with subjects that
Speaker 14: no one else will touch in quite the same way.
Speaker 14: Every Tuesday at apm on Express one oh six point
Speaker 14: five FM, the Legacy ten fifty AM, and live streaming
Speaker 14: on Kcaradio dot com.
Speaker 15: KCAA Radio has openings for one hour talk shows. If
Speaker 15: you want to host a radio show, now is the time.
Speaker 15: Make KCIA your flangship station. Our rates are affordable and
Speaker 15: our services are second to none. We broadcast to a
Speaker 15: population of five million people plus. We stream and podcast
Speaker 15: on all major online audio and video systems. If you've
Speaker 15: been thinking about broadcasting a weekly read program on real
Speaker 15: radio plus the internet, contact our ceo at two eight
Speaker 15: one five nine nine ninety eight hundred two eight one
Speaker 15: five nine nine ninety eight hundred. You can skype your
Speaker 15: show from your home to our Redlands, California studio, where
Speaker 15: our live producers and engineers are ready to work with
Speaker 15: you personally. A radio program on KCAA is the perfect
Speaker 15: work from home advocation in these stressful times. Just type
Speaker 15: KCAA radio dot com into your browser to learn more
Speaker 15: about hosting a show on the best station in the nation,
Speaker 15: or call our CEO for details to eight one five
Speaker 15: nine nine ninety eight hundred.
Speaker 16: What does it take to take on Alzheimer's? Awareness that
Speaker 16: nearly two thirds of those diagnosed to women, including black women,
Speaker 16: dedication to lwering your risk by eating healthy and monitoring
Speaker 16: blood pressure, and confidence to talk to your healthcare provider
Speaker 16: about screening and early detection. You have what it takes
Speaker 16: to take on Alzheimer's. Learn about sciences ringing at take
Speaker 16: on alz dot com. Brought to you by the California
Speaker 16: Department of Public Health.
Speaker 17: Be responsible, don't drink and drive, choose a designated driver.
Speaker 17: Our sponsor, MVP Router is family owned serving the San
Speaker 17: Diego in the Riverside areas with quality and pride for
Speaker 17: all your plumbing and drain means, whether it's residential or commercial.
Speaker 17: Called the pros at nine by one nine zero zero
Speaker 17: eight six eight seven for Riverside or sixty one nine
Speaker 17: six three eight eight six eight seven for San Diego.
Speaker 17: To learn more, visit mbprooter dot com. Mbp Router, a
Speaker 17: BBB accredited company reminding us to never drink and drive.
Speaker 18: Challenging conventional wisdom can advance society's understanding of truth good.
Speaker 18: Arrogantly challenging the complex balance of nature, however, can go
Speaker 18: kabluey very bad. In recent times, there's been an unfortunate
Speaker 18: tendency for some scientific hotshots to send society off on
Speaker 18: techno tangents to quote remake nature promising miracles. About seventy
Speaker 18: years ago, for example, a so called agg science genius
Speaker 18: promised that dumping synthetic pesticides on monoculture crops across the
Speaker 18: globe would end hunger chemical giants and governments rushed to
Speaker 18: do the dump, but the fix ultimately resulted in the
Speaker 18: ongoing poisoning of Earth's land, water, food, and people, while
Speaker 18: enriching agricultural monopolis and allowing hunger to rage. Unfortunately, insistence
Speaker 18: by technologists and profiteers that they can outsmart and overwhelm
Speaker 18: nature is now being pushed with cosmic vengeance. A covey
Speaker 18: of arrogant academics and billionaire backers are saying, trust us,
Speaker 18: we can handle that little global warming issue. One is
Speaker 18: named David Keith, running one hundred million dollar quote stratospheric
Speaker 18: solar geoengineering scheme named scopex. Keith proposes to solve global
Speaker 18: warming by get this, dispensing volumes of sulfured oxide into
Speaker 18: the Earth's stratosphere to quote regulate the amount and location
Speaker 18: of sunlight around the globe. Gosh, what could go wrong
Speaker 18: with that? Never mind the unknown consequences of tampering with
Speaker 18: basic nature, argues Keith, for his bold techno fix to
Speaker 18: global warming bypasses the political difficulty of ending our fossil
Speaker 18: fuel addiction, So we should just do it. This is
Speaker 18: Jim Hijar saying Keith does admit that he can be
Speaker 18: inappropriately forceful. I'm intense, he says. Well, then let's all
Speaker 18: chip in for some therapy sessions to help him overcome
Speaker 18: his megalomania before he makes an irreversible mess of the
Speaker 18: only planet we have that sustains life. The High Tar
Speaker 18: Radio Lowdown is made possible by youth subscribers to Jim
Speaker 18: Hiitar's Lowdown on Substack. Find us at Jimhiitar dot substack
Speaker 18: dot com.
Speaker 7: Hey y'all, Burl here, good news for once. My neighbors
Speaker 7: is jealous of me. You want to know why, because
Speaker 7: my grass is growing and looking green, and I can
Speaker 7: seal my i out in front yard and I don't
Speaker 7: even have to overwater it anymore. You know how I
Speaker 7: did it. I listened to damn water boys on the
Speaker 7: water Zone every Thursday night on KCIA. Well, I got
Speaker 7: me a smart controller and now at Water's at night
Speaker 7: art looks darn tooting. No more sneaking around and hooking
Speaker 7: up my horse to my neighbors pigott in the middle
Speaker 7: of the night, and his dog won't bite me anymore.
Speaker 7: And you can do it too. Listening is easier than ever.
Speaker 7: Kcia is now screaming online it's streaming.
Speaker 3: What it's streaming, you don't.
Speaker 7: Well, I don't know much about streaming, but they doing
Speaker 7: it apparently at KCA radio dot com. So AnyWho listen
Speaker 7: to the water zone and fix your yacht up right
Speaker 7: right here at KCIA, the station that leaves no listener behind.
Speaker 10: Bob Vila here with my home improvement tip of the day.
Speaker 10: When I talk to homeowners about safety, it often centers
Speaker 10: around using tools, ladders, and so forth. But there are
Speaker 10: a lot of other ways that you may be in
Speaker 10: in your home. One of them is by mixing the
Speaker 10: wrong chemicals. You've probably heard you shouldn't mix bleach with ammonia.
Speaker 10: That's true. It produces vapors that can damage your lungs
Speaker 10: and even kill you. Also on the don't mix list
Speaker 10: bleach with vinegar. When combined, they give off a chlorine
Speaker 10: vapor that's similar to the poison gas used against Allied
Speaker 10: troops in World War One. Bleach shouldn't be combined with
Speaker 10: toilet bowl cleaners either, since they too can produce toxic fumes.
Speaker 10: Also steer clear from combining highly acidic products with products
Speaker 10: they're highly alkaline. They can cause serious chemical burns if
Speaker 10: they come into contact with your skin. Before using any
Speaker 10: household product, it's best to check the label. Potentially harmful
Speaker 10: interactions are often listed there. Get more info at bobila
Speaker 10: dot com and right here at home with me Bobila. Hey,
Speaker 10: this is Gary Garb.
Speaker 1: If you work out like I do, or have a
Speaker 1: job where you sit all day and your back hurts
Speaker 1: and you're in pain and you don't know what to do,
Speaker 1: I have the perfect solution for you. It's ice bod.
Speaker 1: Icepot Active is form fitted compression where with pockets that
Speaker 1: fit ice cold gelpacks called flexpods. These flexpods fit around
Speaker 1: your joints ensuring maximum pain relief.
Speaker 2: I use it all the time because I'm.
Speaker 1: Always active, playing golf, working out, fixing up my place
Speaker 1: right now, and I put it on in the evening
Speaker 1: around my back and it gives me maximum pain relief.
Speaker 2: Laker's legend.
Speaker 1: James Worthy is a founder of this company and really
Speaker 1: believes in it. To find out more about ice bod,
Speaker 1: go to ice bodactive dot com and get yours today.
Speaker 1: That's ic bodactive dot com. This week they are having
Speaker 1: a flash sale where you can save twenty five percent
Speaker 1: off by using the promo code KCAA go to ice
Speaker 1: bodactive dot com.
Speaker 19: What is your plan for your beneficiaries to manage your
Speaker 19: final expenses when you pass away?
Speaker 5: Life?
Speaker 19: Insurance, annuity, bank accounts, investment accounts all required deficitivity which
Speaker 19: takes ten days based on the national average.
Speaker 7: Which means no money is immediately available.
Speaker 19: And this causes stress and arguments. Simple solution the beneficiary
Speaker 19: liquidity clan use money you already have no need to
Speaker 19: come up with additional funds. The funds wrote tax deferred
Speaker 19: and pass tax free to your name beneficiary. The death
Speaker 19: benefit is paid out in twenty four to forty eight
Speaker 19: hours out a deficitary your every money out a deficity
Speaker 19: call us at one eight hundred three zero six fifty eighty.
Speaker 7: Six, Pilastina CACAA Loma Linda at one O six point
Speaker 7: five FM K two ninety three c F Burrito.
Speaker 3: Valley, Located in the heart of San Bernardino, California, The
Speaker 3: Teamsters Local nineteen thirty two Training Center is designed to
Speaker 3: train workers for high demand, good paying jobs and various
Speaker 3: industries throughout the Inland Empire. If you want a pathway
Speaker 3: to a high paying job and the respect that comes
Speaker 3: with a union contract. Visit nineteen thirty two Trainingcenter dot
Speaker 3: org to enroll today. That's nineteen thirty two Trainingcenter dot org.
Speaker 9: NBC News Radio. I'm Lisa Carton. Jimmy Carter, the thirty
Speaker 9: ninth President of the United States, is dead at the
Speaker 9: age of one hundred. Carter's death came after a February
Speaker 9: twenty twenty three announcement that he had decided to enter
Speaker 9: hospice care and spend his remaining time at home with
Speaker 9: family after a series of short hospital stays. Carter served
Speaker 9: a single tumultuous term and was defeated by Republican Ronald
Speaker 9: Reagan in nineteen eighty a landslide loss that ultimately paved
Speaker 9: the way for his decades of global advocacy for democracy,
Speaker 9: public health, and human rights. Former President and Missus Carter
Speaker 9: worked with Habitat for Humanity in communities throughout Georgia and
Speaker 9: globally for nearly forty years. More from Liz Kennedy.
Speaker 20: In nineteen eighty four, Jimmy and Roselind Carter created the
Speaker 20: Carter Work Project, working alongside volunteers with Habitat for Humanity,
Speaker 20: building and avocating for affordable housing. It was an experience
Speaker 20: that fulfilled the couple.
Speaker 8: Every time we've ever been out as volunteers leading a project,
Speaker 8: no matter where it's been any in this structure oin
Speaker 8: around the rest of the world. At the end of
Speaker 8: the Habitat project, we always feel that Rose and I
Speaker 8: got more out of it than we put into it.
Speaker 20: The former President One said, Habitat provides a simple but
Speaker 20: powerful avenue for people of different backgrounds to come together
Speaker 20: to achieve those most meaningful things in life. A decent home,
Speaker 20: but also a genuine bond with our fellow human beings.
Speaker 20: I'm Liz Kennedy. Carter is widely revered for his champion
Speaker 20: of human rights. His brokering of the Camp David Accords
Speaker 20: with Egyptian President and War Sadat and Israeli Prime Minister
Speaker 20: menach And began nineteen seventy eight, remains central to his legacy.
Speaker 20: Carter also received the Nobel Peace Prize in two thousand
Speaker 20: two for his efforts to push for peace across the globe.