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Speaker 1: Carl and Richard here with your twenty twenty four NDC schedule.
Speaker 2: Will be at as many NDC conferences as possible this year,
Speaker 2: and you should consider attending no matter what. The Copenhagen
Speaker 2: Developers Festival happens August twenty sixth through the thirtieth. Tickets
Speaker 2: at Cphdevfest dot com.
Speaker 1: NDC Porto is happening October fourteenth through the eighteenth. The
Speaker 1: early bird discount ends June fourteenth. Tickets at Ndcporto dot com.
Speaker 2: And we'll see you there, we hope.
Speaker 1: Hey, guess what it's dot net rocks again. I'm Carl
Speaker 1: Franklin and I'm Richard Campbell, and this is episode nineteen nineteen.
Speaker 1: And Buddy, I went and I looked up you know
Speaker 1: what happened in this year in history? So some significant
Speaker 1: events included the collapse of the Habsburg and German empires.
Speaker 2: Yes, the end of World War One, end of World War.
Speaker 1: One, the redrawing of the map of Europe at the
Speaker 1: Paris Peace Conference, the influence of Woodrow Wilson's fourteen points.
Speaker 2: Interesting they call it the Paris Peace Conference. It was
Speaker 2: the Versiah Cords, and they were not They were not very.
Speaker 1: Peaceful, not very peaceful.
Speaker 2: Yeah, they were a very good setup for World War two, yep.
Speaker 1: And sowing the seeds of World War two, growing demands
Speaker 1: for self determination by smaller nations, the growing involvement of
Speaker 1: the United States and European politics and trade all that. Yeah.
Speaker 2: Yeah, one would argue this is the ess of the
Speaker 2: nation stage. Yeah, basically the model we've been functioned ever since.
Speaker 1: I bet you didn't know Richard Campbell knew so much
Speaker 1: about history. O. Who am I kidding? You knew you knew?
Speaker 1: Go ahead and roll the music, because I got a
Speaker 1: pretty good better no framework, awesome? All right? What do
Speaker 1: you got? So you know, I'm cutting down on travel
Speaker 1: to conferences. At the end of the year, I'm not
Speaker 1: going outside the country. I'm not even sure that there
Speaker 1: are any more domestic conferences that I'm going to at
Speaker 1: the end of this year, but I still want to
Speaker 1: do some Blazer hands on training.
Speaker 2: Right.
Speaker 1: So what I've done is if you go to APPVNX
Speaker 1: dot com, slash training or nineteen nineteen dot po dot me,
Speaker 1: you'll see the training page which links to a form
Speaker 1: that you can fill out. I've decided I want to
Speaker 1: do these online classes on Mondays. Oh yeah, mostly Mondays
Speaker 1: from now until the end of the year, most of
Speaker 1: them except for you know, some week off here and there.
Speaker 1: And this is a form where you can select the
Speaker 1: dates that you want to attend. Each Monday, we'll have
Speaker 1: one starting at nine am Eastern and nine am Pacific,
Speaker 1: so that you know, if more people want to do
Speaker 1: in a Pacific time blah blah blah. But the deal
Speaker 1: is that once we hit that magic number of ten,
Speaker 1: then the class is going to happen, Right, So you
Speaker 1: should select all of the dates that you can possibly make,
Speaker 1: and then there's a greater chance that one of them.
Speaker 2: Will will take But online classes.
Speaker 1: Will online classes, but you know it's interactive. You'll be
Speaker 1: able to do the hands on work and even though
Speaker 1: we won't get to all the materials in one day,
Speaker 1: you still get all the materials right so you can
Speaker 1: go back on your own time.
Speaker 2: Well in the class size sounds like assuming nice as
Speaker 2: small too, so people go be able to interact a lot.
Speaker 1: Well, ten is the minimum. I haven't really set a
Speaker 1: maximum yet, but of course if worry about that problem
Speaker 1: when it happened, I'll worry about that problem. So that's
Speaker 1: what it is at Phoenix dot com slash training. Who's
Speaker 1: talking to us Richard crawbd to.
Speaker 2: Comment on top of show nineteen sixteen, So just last
Speaker 2: one published. To be honest, this is the show we
Speaker 2: did with Mark Rendalls. We called how Simple is as
Speaker 2: Simple as Possible? And in finding conversation with Mark, as
Speaker 2: always he's a thoughtful guy as well as very funny.
Speaker 2: I think we spent most of the show laughing. And
Speaker 2: Rob King has this great comment. He said, once again
Speaker 2: another great episode which reaffirms my position that everything old
Speaker 2: is new again. I feel like there's a whole generation
Speaker 2: of developers coming up with only known JavaScript frontends and
Speaker 2: think the server is only for APIs or serving the JavaScript.
Speaker 2: I've started going back to a server side first approach
Speaker 2: of building apps, keeping get all in the server and
Speaker 2: then build it out towards the client only required. Yes,
Speaker 2: Lazer made this is really simple, and it's approached to
Speaker 2: adding interactivity as required. I've even converted a Blazer WASM
Speaker 2: app to pure service side because it's actually faster than
Speaker 2: the first page and the app doesn't need any interactivity
Speaker 2: beyond the forms. To that end, I've found that you
Speaker 2: can integrate htmx something that Mark talked about right seamless
Speaker 2: see into Blazer server and a built out of a
Speaker 2: little demo repo which I'll include in the show notes
Speaker 2: to Rob's GitHub repository. It's just a sort of hey,
Speaker 2: these two things will play together, Blazer and HGMX.
Speaker 1: Yeah, there you go.
Speaker 2: You know, it's just sort of a sort of recognition
Speaker 2: of yeah, we're kind of calling in a cycle. Like
Speaker 2: there's clearly cases for putting more load on the client,
Speaker 2: but one would argue we swung too far that way. Yes,
Speaker 2: although I know as we get into these conversations about
Speaker 2: large language models and machine learning so forth, there's now
Speaker 2: a big push about how do you get more of
Speaker 2: that workload on the client because folks are concerned now
Speaker 2: that too much of that workload is on the server.
Speaker 2: So I think it'll be some good talking points for
Speaker 2: today's show.
Speaker 1: I'm really happy that a guy like Mark Render, who
Speaker 1: you know, go back and listen to his stuff before
Speaker 1: a Blazer came out. He was not really a service
Speaker 1: side guy at all, like very much in the in
Speaker 1: the composable c I CD pipeline kind of thing and
Speaker 1: and all you know JavaScript, and a guy like Mark
Speaker 1: comes out and says, you know what, I took Blazer
Speaker 1: server for a ride and I really like it.
Speaker 2: Yeah, it's really great. Well, you know, and again his whole,
Speaker 2: that whole how simple is simple is getting back to
Speaker 2: the intention of what we're trying to do for the
Speaker 2: customer exactly right, Like I do think we get enamored
Speaker 2: of the technology. And again I brought this show and
Speaker 2: these comments out because we're about to go down a
Speaker 2: path about a whole other class of development, and I
Speaker 2: think it's good to push back on those fundamentals and say,
Speaker 2: what are we trying to deliver to customers?
Speaker 1: Yep, no pressure pre shant, Yeah for sure.
Speaker 2: So Rob, thank you so much for your comment and
Speaker 2: a copy of Music co Buy. It's on its way
Speaker 2: to you. And if you'd like a copy of Music
Speaker 2: co By, I write a comment on the website at
Speaker 2: dot at rocks dot com or on the facebooks. We
Speaker 2: publish every show there, and if you comment there and
Speaker 2: never read on the show, we'll send you a copy
Speaker 2: of Music to.
Speaker 1: Go By, Music to Code by, Still Going Strong. I
Speaker 1: just sold another Flak collection, which is good, and I
Speaker 1: got inspired last night to do a new one.
Speaker 2: How many how many have you made?
Speaker 1: Is it like twenty one?
Speaker 2: Twenty one? Okay, so you know you're digging around for
Speaker 2: another track? Huh, that's awesome.
Speaker 1: Well, I was listening to it last night. I just
Speaker 1: decided to.
Speaker 2: I don't know if you've heard it. Stuff.
Speaker 1: It's pretty good. It is pretty good. Yeah, I've been
Speaker 1: listening for a long time. Yeah, I mean I started.
Speaker 1: I was listening to the more recent ones okay last
Speaker 1: night and I was like, yeah, you know, this is
Speaker 1: pretty good stuff. I think I got inspired to do
Speaker 1: another one.
Speaker 2: Hey, you should meet that guy. He's pretty clever.
Speaker 1: Yeah, so we'll see. I'm not announcing it now, I'm
Speaker 1: just announcing my intent.
Speaker 2: You got it?
Speaker 1: Some itch?
Speaker 2: I like when you have an itch? Yeah, stuff happens
Speaker 2: when you have an itch. Yeah yeah.
Speaker 1: Well, anyway, you can also follow us on ex Twitter.
Speaker 1: We've been there for years, but the cool kids are
Speaker 1: hanging out. I'm Astdon, I'm Carl Franklin at tech Hub dot.
Speaker 2: Social, and I'm Rich Campbell at master do Social.
Speaker 1: Send us a two. That's another way you can get
Speaker 1: a copy of music to code buy. Okay, let us
Speaker 1: introduce Prashant Boyar. He is six times AI MVP, three
Speaker 1: times Business Applications MVP, Microsoft Certified Trainer, speaker, author, and
Speaker 1: leading AI architect from the Washington, DC area of the
Speaker 1: United States. Currently, he works as an AI architect in
Speaker 1: the Office of the Chief Technology Officer at Applied Information
Speaker 1: Sciences That's AI at dot com. Braschant is on the
Speaker 1: leadership committee for AI Fest, AI and mL User Group
Speaker 1: and the Northern Virginia Data Platform User Group. As a
Speaker 1: renowned international speaker, he frequently presents a tech conferences. Additionally,
Speaker 1: Prashant was awarded the Antarctic Service Medal of the United
Speaker 1: States of America for his outstanding service in Antarctica. You know,
Speaker 1: I was going to tease you for not having enough
Speaker 1: stuff on your CV, but let's go right to Antarctica.
Speaker 2: What was that?
Speaker 3: So, first of all, thank you very much for having me.
Speaker 3: That was once in a lifetime experience for me. Like
Speaker 3: I grew up in India and two thousand and seven
Speaker 3: is the year I came to the United States for
Speaker 3: my master's degree and I got I was really fortunate
Speaker 3: that I got a scholarship in the university and as
Speaker 3: part of that scholarship, I got a chance to work
Speaker 3: with a lot of brilliant scientists. And those scientists were
Speaker 3: studying the costmes raise and everyone in that room except
Speaker 3: me was like really brilliant. So I had to, you know,
Speaker 3: fit in and I know, and you.
Speaker 1: Might be a little unbiased towards yourself.
Speaker 3: Now, Like if you if you talk to any experimental
Speaker 3: physics physicists or in theoretical physicists, not only they know
Speaker 3: software development really well, but they also know all this
Speaker 3: machine learning match and some advanced mathematics really well. So
Speaker 3: I have like after that, I have really high respect
Speaker 3: for all these physicists because for the work they do.
Speaker 3: And if you compare the salaries against that, you will
Speaker 3: be surprised.
Speaker 2: Yeah, they don't get paid much for yes, as hard
Speaker 2: as they as they work. Antarctica is a great location
Speaker 2: for doing stuff like measuring comic cause of rays. You know,
Speaker 2: the atmosphere is a little thinner, and it's very very dark,
Speaker 2: and there's a minimum amount of noise, but it's and
Speaker 2: it's sad. That's like the best time to do that
Speaker 2: work is midwinter of Antarctica where there's no sunlight for months,
Speaker 2: so for the tough place to be for shann you
Speaker 2: hung out there.
Speaker 3: Yes, So as part of that experiment, like we built,
Speaker 3: it was a joint collaboration WITHWEN multiple universities, and ASA
Speaker 3: was the main sponsor because typically NASA sponsors this, you know,
Speaker 3: give this huge grant to the unisity so that they
Speaker 3: can do research on their behalf. And as part of
Speaker 3: the research, you know, the best results to get is
Speaker 3: via satellite, but you know, sending any payload to satellite
Speaker 3: is very expensive. So they came up with an approach
Speaker 3: about how we send this really large duration balloons in
Speaker 3: Antarctic continent which fly around you know the south you know,
Speaker 3: South Pole for around twenty to eighty days, so not
Speaker 3: really twenty to thirty days, collect as much data as possible,
Speaker 3: and then we take years and years to analyze the data.
Speaker 3: And not many people know, but US government has a
Speaker 3: really big station there, like Unartic continent is huge and
Speaker 3: a lot of countries have stations, but US in particular
Speaker 3: has like huge stations over there. And the station I
Speaker 3: went to, it's called Macmurdo Station. So when I was
Speaker 3: there into like in the end of twenty eighteen, sorry,
Speaker 3: two thousand and eight, I was there for almost a
Speaker 3: couple of months. And that is like a typical summer
Speaker 3: time in Antarctica, and that's the time where they will
Speaker 3: have like around thousand people on the base and everybody
Speaker 3: will be there to conduct some kind of scientific experiment
Speaker 3: and to support those experiment. They also need a lot
Speaker 3: of people, like they need cooks, they need chefs, you know,
Speaker 3: they knew it, people who can like blow the roads
Speaker 3: and all this kind of stuff. And I met a
Speaker 3: lot of interesting people there who like, let's say a
Speaker 3: software engineering by trade. They work here nine months in
Speaker 3: US continent, and for three months they just go to
Speaker 3: Antarctica and work as a chef or as a cook
Speaker 3: because they just like it there and the pay is good.
Speaker 1: And the base has good internet access. I hear yes,
Speaker 1: and I know that because I have a friend I
Speaker 1: went to grammar school with who is messaging me from Antarctica.
Speaker 1: I'm like, what you're aware, but he was at that
Speaker 1: base and yeah, so pretty cool. How in the summertime
Speaker 1: in Antaro would you think of it as bomby? Would
Speaker 1: you go out in shorts? What's the temperature?
Speaker 3: So temperature is not that bad. You can go out
Speaker 3: and go and go out in shots only for ten
Speaker 3: to fifteen minutes, that's it.
Speaker 1: Wow.
Speaker 3: So like the during the summer time, you know there
Speaker 3: the temperature was in the range of you know, like
Speaker 3: minus let's say ten degrees celsius to plus five degrees celsius. Okay,
Speaker 3: but there used to be some drays where you know,
Speaker 3: it was like very very bad, Like we had like
Speaker 3: one day where it was minus twenty degrees celsius and
Speaker 3: things were pretty rough over there. But you have heated
Speaker 3: rooms and you know, like food is awesome, there are
Speaker 3: some recreational activities and you know, and that when you
Speaker 3: go to those kinds of places, you kind of realize, okay,
Speaker 3: how fast paced your life is back in like you know,
Speaker 3: you know, the civilized world, and how much of a
Speaker 3: time you got when you go to these kind of places.
Speaker 1: Did you ever have to slice open a banta with
Speaker 1: your lightsaber to keep warm? No? No, that's the question.
Speaker 2: I was.
Speaker 3: I was fortunate enough. No, I went there on a
Speaker 3: good good time over.
Speaker 2: There, Yeah, very good. You only had to smell them
Speaker 2: on the outside.
Speaker 1: Oh my, sorry for that little diversion, but I just
Speaker 1: had to know.
Speaker 2: It's great.
Speaker 1: So let's talk about Microsoft co Pilot Studio. I know
Speaker 1: because I went to build with Richard, and we know
Speaker 1: that it's for building your own custom agents and you
Speaker 1: can connect them to a bunch of sources of data,
Speaker 1: and we know that you can hook them up into
Speaker 1: M three sixty five and all that. But what don't
Speaker 1: we know?
Speaker 3: Okay, so let me go back to the history and
Speaker 3: start with Microsoft first, foray into the cornosential AI. Like
Speaker 3: if you remember in March twenty sixteen, I think it's
Speaker 3: the first time when Satya Nadela went onto the stage
Speaker 3: and he announced about Microsoft bought framework. Because still that time,
Speaker 3: there was no good story when it comes to building
Speaker 3: let's say, any kind of child bought or conversational experiences.
Speaker 3: And after maybe two or three, after a month after that,
Speaker 3: I know Microsoft is getting a lot of good price
Speaker 3: right now because of their partnership with open AI. But
Speaker 3: they also had their fair share of failures as well.
Speaker 3: I think In March twenty sixteen, they launched a Twitter
Speaker 3: bought called day oh yeah yes, yes yeah, and its
Speaker 3: long form was thinking about you And they launched on
Speaker 3: Twitter and that pot was from Microsoft Research and it
Speaker 3: was their first public foray into the world of AI.
Speaker 3: And people soon figure out on Twitter, you know, to
Speaker 3: teach that bot to say some nasty stuff and Microsoft yes, yes, yeah, Microsoft.
Speaker 3: Microsoft try to like you know again reteaching like good stuff,
Speaker 3: but within sixteen hours they had to take it down
Speaker 3: and they still now a lot of key executives at
Speaker 3: Microsoft talks about that particular failure saying like, hey, we
Speaker 3: learn a lot from that failure, like how what other
Speaker 3: things not to do with AI stuff? And so they
Speaker 3: continue to work on board framework for next three years.
Speaker 3: But board framework, like is very complex.
Speaker 2: Yeah, we did a show, We did shows on bot framework,
Speaker 2: like you said, like twenty seventeen. It's hard to think
Speaker 2: about that now because the opening eye things overwhelmed everything. Yes,
Speaker 2: and they made a lot of sense, like the fact
Speaker 2: bot to me was totally logical that you could point
Speaker 2: a piece of software at a fact and it would
Speaker 2: give you sort of a natural language interface to it.
Speaker 3: But still, like Microsoft struggle to get really good traction
Speaker 3: with a lot of customers and developers because it was
Speaker 3: extremely hard to get you know, your conversational experiences right.
Speaker 3: And then in November twenty nineteen they launch a low
Speaker 3: code version of it called Power Virtual Asian and the
Speaker 3: name Power itself can give you indication which product family
Speaker 3: belongs to. It was from a power platform. So they
Speaker 3: continued on that journey and then basically there was a
Speaker 3: time where the entire investment on board framework stopped and
Speaker 3: all the things like Microsoft was doing on Power virtualation side.
Speaker 3: They had decent success. But then the real success or
Speaker 3: real i'll call life changing moment for that particular product
Speaker 3: or group was Ignite last year. Fen went on stage
Speaker 3: and say, hey, Microsoft now is the co pilot company,
Speaker 3: and if you would like to create a low code,
Speaker 3: no code co pilots, then Copilot Studio is the product.
Speaker 3: And I know Microsoft get a lot of flak, especially
Speaker 3: their marketing department, for naming like weird products, but I
Speaker 3: think this time really nailed it.
Speaker 2: To be clear, GitHub came up with the name, right,
Speaker 2: That's why it's such a good name, because Microsoft didn't
Speaker 2: think of it.
Speaker 1: We have a history of making really long names that
Speaker 1: are very kind of academic sounding.
Speaker 2: Well they're named by Kimmittee.
Speaker 3: Yes, yeah, and someone I heard. I don't know how
Speaker 3: true it is, but this product was supposed to have
Speaker 3: another name. They already have a lot of printed name
Speaker 3: and banners on it, but just three one week or
Speaker 3: two weeks before Ignite, someone came up with this name
Speaker 3: and they have to, you know, do a lot of
Speaker 3: you know, last minute stuff.
Speaker 2: There's been a lot of name twitching, right copilot for
Speaker 2: Microsoft three sixty five, Microsoft three sixty five copilot like it's.
Speaker 1: It's you know, the AI thing has been sort of
Speaker 1: like a gold rush, hasn't it. Yes, like every company
Speaker 1: wants to get their put their stake in the ground
Speaker 1: and say we're doing AI or we're doing you know,
Speaker 1: generative AI. And I mean I don't I don't want
Speaker 1: our listeners who are dot developers to be thinking that, oh,
Speaker 1: development is gone. Microsoft is just focusing completely from now
Speaker 1: on on AI. Because if you went to build, there
Speaker 1: wasn't a whole lot about dot net, you know what
Speaker 1: I mean. There wasn't a whole lot of code, plumbing code.
Speaker 1: It was all focused on AI. But you know, we're,
Speaker 1: as Richard said in the beginning when he read that comment,
Speaker 1: we need to stay focused on delivering value to our customers.
Speaker 1: And the sort of AI thing just smacked this in
Speaker 1: the face and came out of nowhere and says, hey,
Speaker 1: guess what this is going to allow you developers to
Speaker 1: provide better value to your customers. So really interested to
Speaker 1: hear some scenarios about that, and you.
Speaker 3: Write a call like, you know, just don't think that
Speaker 3: copilot Studio is the only product that I will need
Speaker 3: to use, you know, when I had to create custom copilots.
Speaker 3: There is another story in Azure world called AI Studio.
Speaker 3: So if you are a professional developer and you don't
Speaker 3: want to use let these drag and drop kind of
Speaker 3: tools or low code no code tools and you would
Speaker 3: have full control. Studio is your choice where since it's
Speaker 3: part of Azure, you know it has a really good
Speaker 3: pro developer story. You can be a dot net developer,
Speaker 3: you can be a Python developer, and you can use
Speaker 3: all the good services in Azure. And one other thing
Speaker 3: I like about AI service a Azure EI studio is
Speaker 3: the model selection. Like. One thing Microsoft has done which
Speaker 3: not many people know is Microsoft launched something called Model
Speaker 3: as a service where you can bring in some open
Speaker 3: source model as well and host those in Azure and
Speaker 3: use those in your Genia applications, so you don't have
Speaker 3: to go with let's say models from open Ai or
Speaker 3: models from let's say Microsoft for that matter. You can
Speaker 3: use Lama hugging pace. You know, those models are also available.
Speaker 3: So if you want to create a co pilot or
Speaker 3: agent using that, you can do that now in Asure.
Speaker 1: But they still have GPT four GPT four oh dally
Speaker 1: all that stuff from AI Open ai. But all right,
Speaker 1: so wow, So that is really the fundamental difference between
Speaker 1: these two is that co pilot studio is maybe for
Speaker 1: draggy dropping business people and Azure AI studios more for us.
Speaker 3: There is one fundamental difference which not many people tay
Speaker 3: pay attention. So when it comes to building a co
Speaker 3: pilot or any kind of a application, a lot of
Speaker 3: time people don't think about the UX part of it.
Speaker 3: I know. That's where a copilot studio shines really well.
Speaker 3: Like out of the box, it has really good capacity
Speaker 3: of you know, having a really good UX. Let's say
Speaker 3: I launch a co pilot for my existing enterprise app.
Speaker 3: But then that copilot should able to understand or behave
Speaker 3: like actual human right. The reason chad jipt got so
Speaker 3: popular so fast is because we can interact with it
Speaker 3: and you know, it can understand like at least what
Speaker 3: I'm trying to say maybe ninety to ninety percent of time,
Speaker 3: and it can give me good information back. And if
Speaker 3: you compare that with old version of the chatboards that
Speaker 3: more of these public facing website used to have. Those
Speaker 3: used to be like, oh the major retailers or bank
Speaker 3: used to have those, like used to be simple time
Speaker 3: st where the first thing I used to google that
Speaker 3: time is ourbing is what's the best way to get
Speaker 3: to an actual human agent rather than you know, going
Speaker 3: through their automated.
Speaker 2: Response because that's what everybody does.
Speaker 3: Yes, But now with this, after the generative AI and
Speaker 3: some of the advancement, you know, you can at least
Speaker 3: have some decent conversation. And that's where this co Pilot
Speaker 3: Studios shine where it has a lot of widgets, it
Speaker 3: has a lot of you know, building capabilities that if
Speaker 3: I would like to build a good conversational experience, Copilot
Speaker 3: Studio really shines as compared to Azure Eye Studio.
Speaker 1: Can I take the agents that I make in copiled
Speaker 1: studio and move them over into Azure Ai studio and
Speaker 1: enhance them.
Speaker 3: Right now, the create direct conversation story is not there.
Speaker 3: But what you can do is, let's say, if you
Speaker 3: have already an agent in Azure AI side, you can
Speaker 3: directly call that from a Copilot studio, so you can
Speaker 3: do integration. But there is no like Magic vand to
Speaker 3: convert those.
Speaker 1: That's actually better.
Speaker 2: Yes, well, it also reminds you that there's more than
Speaker 2: one team instead of Microsoft working on this stuff, right,
Speaker 2: you know, that's that's sort of the real reality is
Speaker 2: that there's lots of different places moving at once.
Speaker 3: And I'm a big believer of Fusion Team where hey, developers,
Speaker 3: you focus on you know, doing really crazy stuff behind
Speaker 3: the scene, you know, make the magic, but also gave
Speaker 3: a lot of information worker or business users and ability
Speaker 3: to you know, take the advantage of what they know
Speaker 3: from the business and then you know, have an easy
Speaker 3: way to interact with the back end system. So so
Speaker 3: Copilo Studio also have that functionality way. Let's say I
Speaker 3: have my logic app I would like to call or
Speaker 3: I would like to call my EPI. I can do
Speaker 3: that directly. There is an STTP connector available or I
Speaker 3: can also invoke a power Automate cloud flow, and power
Speaker 3: Automate has more than twelve hundred out of the box
Speaker 3: connectors where you know, or I can build my custom
Speaker 3: connector as well, which then can you know, call my
Speaker 3: back end APIs and then can get the job done.
Speaker 1: I have a quick story here. I got an email
Speaker 1: last week from somebody who said, you know, I see
Speaker 1: that you have a YouTube channel and you've got some
Speaker 1: great content there, and uh, you know, for a very
Speaker 1: low fee. Actually it was pretty good. I will, I will,
Speaker 1: I will generate SEO happy titles, descriptions, tags and stuff
Speaker 1: for your YouTube content. So I said, okay, sure, I'll
Speaker 1: try it. And what I got back was a RAR
Speaker 1: file full of text files for and a lot of
Speaker 1: them were for the dot net rocks videos. And they
Speaker 1: kept mentioning the dot net Rocks videos are simply a
Speaker 1: still shot with our podcast that plays on it. And
Speaker 1: this thing went on and on about how you will
Speaker 1: learn from these training videos, hands on experience and stuff.
Speaker 1: They got to the they got to the songs that
Speaker 1: my band has put up there, and they called, you know, Nachelle,
Speaker 1: who's our background singer, but she does sing on some
Speaker 1: of them, but on this one that I was singing
Speaker 1: like all of it lead singer Nachelle Rollins blah blah blah,
Speaker 1: this big, grandiose stuff, and I wrote it back. I'm
Speaker 1: not gonna say it is by her own back and say, dude,
Speaker 1: this is bullsh this is some AI generated crap that
Speaker 1: doesn't know a still video from you know, from a
Speaker 1: training video for crying out loud. So yeah, I won't
Speaker 1: be using their actually incorrect, factually incorrect and being sold
Speaker 1: as you know, the I don't know whatever.
Speaker 2: So I mean, you say, quietly said Fusion development there,
Speaker 2: which is that power platform working with dot net dev
Speaker 2: and so forth. So you see a sort of same
Speaker 2: dynamic where we have domain experts, which I think is
Speaker 2: the big biggest challenge when you're building a lot of
Speaker 2: these language models, is you need a domain expert checking
Speaker 2: for factual accuracy, knows enough to know that is right
Speaker 2: or that is wrong. Yes, who actually sang on this track,
Speaker 2: as well as some coding knowledge to be able to
Speaker 2: pull the pieces together.
Speaker 3: Yes, that is, that is correct. And one of the
Speaker 3: things is, you know a lot of people like really
Speaker 3: got excited after the rise of generative EI, but there
Speaker 3: are like legitimate you know application, A lot of organizations
Speaker 3: built which doesn't use generative AI at all. Like one
Speaker 3: of my customers, they did use Copilot Studio, and they
Speaker 3: did launch a custom Copilot for their public facing website,
Speaker 3: but they didn't use any of the generative AI features
Speaker 3: at all, Like, because there will be some instances where
Speaker 3: you don't want to give a sort of correct or
Speaker 3: incorrect answer, right, You want to get give a factually
Speaker 3: correct answer. And that are some of the things that
Speaker 3: are there in Copilot Studio where for some of the
Speaker 3: information you absolutely want to give the appropriate answers maybe
Speaker 3: from your own knowledge pace or maybe from your own
Speaker 3: database or you and that you can do. And there
Speaker 3: are certain things where you think, okay, I can use
Speaker 3: generative AI for some of the answers. You can do
Speaker 3: that segregation wasy very easily as well.
Speaker 1: I'm just another story. I love telling these stories my
Speaker 1: Facebook feed. I don't know about you guys, if you're
Speaker 1: on Facebook at all, but every once in a while
Speaker 1: I scroll and I'm seeing these accounts that are something
Speaker 1: like tiny houses, right, and then this clearly generated beautiful
Speaker 1: scene of this wonderful house with multiple staircases and flowing
Speaker 1: waterfalls by a brook. And it's just like a place
Speaker 1: that you'd want to be, right And the caption is
Speaker 1: like peaceful place. It's not. Hey, this is where I
Speaker 1: am right now, it's not. And to get millions of
Speaker 1: views and hundreds of thousands of comments, it just makes
Speaker 1: me mad. Welcome to the new world.
Speaker 2: Well, and again it's you used a bought to generate
Speaker 2: the content. Who's to say you didn't use a bought
Speaker 2: to generate the hits? Well, there you go, all right,
Speaker 2: So let's take a break, and when we come back,
Speaker 2: I've got a couple of questions.
Speaker 1: Great, did you know there's a dot net on aws community?
Speaker 1: Follow the social media blogs, YouTube influencers, and open source
Speaker 1: projects and add your own voice. Get plugged into the
Speaker 1: dot net on aws community at aws dot Amazon dot com,
Speaker 1: slash dot net. Hey Carl, here are you maximizing your
Speaker 1: dot net applications potential? Rayguns tools not only catch errors,
Speaker 1: they provide actionable insights to optimize your code experience the
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Speaker 1: tn E t r O c ks for your free
Speaker 1: fourteen day trial.
Speaker 2: We're back. It's dot at rocks. I'mrogard Campbell. Let's call Franklin. Hey, Hey,
Speaker 2: I'm talking to our friend Forshett about the role of
Speaker 2: as your AI studio as well as the co pilot studio,
Speaker 2: because what's better than two?
Speaker 1: And I like how you can use them together.
Speaker 2: What's the call out for Patreon there, Carl.
Speaker 1: Yeah, And if you don't want to hear those ads
Speaker 1: during the show, you can subscribe to our Patreon account
Speaker 1: for five bucks a month. You can get a feed
Speaker 1: that has no ads. So there you go.
Speaker 2: Awesome, Forsham, you mentioned like the ux of a co
Speaker 2: pilot app. Isn't it just a textbox?
Speaker 3: Not necessarily? Sometimes it can be text paused, but some
Speaker 3: times you may want to show a really nice adapt card.
Speaker 3: Sometimes you may have to show an image or maybe
Speaker 3: the user is looking for a video. So those kind
Speaker 3: of things like if I go with traditional board framework way,
Speaker 3: those kind of things are really hard to you know,
Speaker 3: get it right. And another thing way where Copilot Studio
Speaker 3: really shines is you know, like how I can support
Speaker 3: my copilot across multiple devices or multiple experience. That is
Speaker 3: like one of the biggest challenges I have seen with
Speaker 3: multiple conversational experiences. Like, yeah, like I build it, it's
Speaker 3: worse on my machine, or it works in the environment
Speaker 3: where I have deployed, But what about now the customer
Speaker 3: wants to use the same copilot on Microsoft Teams, or
Speaker 3: they want to use it on a public facing website,
Speaker 3: or the customer loves Slack they want to use it there.
Speaker 3: Like how I can you know, now redo everything so
Speaker 3: that you know, my custom copilot will work on that
Speaker 3: experience as well or on that service as well. So
Speaker 3: copilot Studio has a lot of these channels or services
Speaker 3: already prebuilt where I don't have to redesign my experiences.
Speaker 3: All I do is do some back end like changes
Speaker 3: in the studio itself, and the same copilot then can
Speaker 3: be accessible on let's say SLAG or Microsoft Teams.
Speaker 1: In both of these things, I noticed that you know,
Speaker 1: you can connect it to your database. Yes, And apparently
Speaker 1: the coolest thing is you know how many reverse threaded
Speaker 1: left handed hemptas are left in inventory in Atlanta, Georgia warehouse, right,
Speaker 1: and it'll just tell you that kind of stuff. But
Speaker 1: the cooler things about generative AI for me are building
Speaker 1: models from you know, training and unreal data. And is
Speaker 1: that something that is not necessarily as valuable to regular,
Speaker 1: run of the mill businesses as it would be to say,
Speaker 1: you know, a chat gpt, which is the genie in
Speaker 1: the bottle that knows everything.
Speaker 3: So, if I understand your question correctly, you're alluding more
Speaker 3: to you know. How about I have my own data
Speaker 3: and I would like to use that data into generative AI.
Speaker 3: That's one way of doing it. Or maybe I have
Speaker 3: my own data and I would like to generate my
Speaker 3: own model using that data because I want the answers
Speaker 3: in a specific format or in a specific way rather
Speaker 3: than what you know, Chat Jupiter gives me. So there
Speaker 3: are a couple of things you can do. And you
Speaker 3: may have heard this a lot. Very few companies these
Speaker 3: days are investing building their own model because it's very
Speaker 3: hard to compete with companies like Amazon, Google, or open
Speaker 3: AI or even Microsoft. So most common techniques which most
Speaker 3: of the enterprises are using these days is either you
Speaker 3: go with a rack pattern like retrieval augumented generation, where
Speaker 3: you bring your own data and then feed that context
Speaker 3: or data to your large language model and then get
Speaker 3: the inside. Second very common is not very common, Less
Speaker 3: common and more expensive out is fine tuning this models
Speaker 3: like where as part of fine tuning, then I provide
Speaker 3: series of prompts some training data. So then after a
Speaker 3: fine tuned model is there, then it will be responding
Speaker 3: in a specific way, and it will be also having
Speaker 3: a specific information that let's say the generic large language
Speaker 3: model or foundation on a large language model will not have.
Speaker 1: Now, when you say that, do you mean cleaning the data,
Speaker 1: like taking out the things that aren't relevant, or do
Speaker 1: you mean when you say fine tuning, do you mean
Speaker 1: telling it what to ignore in the data.
Speaker 3: Yes, so you have to basically provide custom instructions. You
Speaker 3: have to provide custom prompts, and you can also provide
Speaker 3: your own data as well. Where okay, these are the
Speaker 3: sete of things that you should be doing in this
Speaker 3: particular way. Don't include that or don't include these, so
Speaker 3: those kinds of additional things you can do. One thing
Speaker 3: I do want to highlight is fine tuning is not
Speaker 3: for everyone it's only you know, for companies where you
Speaker 3: know you're going to have large workload or your consumption
Speaker 3: is going to be very high. And let's say with
Speaker 3: this large language model and or this large language model
Speaker 3: will not work in its current capacity because under the hood,
Speaker 3: if you read the licensing documentation from both open ai
Speaker 3: or even from Microsoft on Azure side, not only you
Speaker 3: had to pay money to find tune the model, but
Speaker 3: once the model is fine tuned, you had to also
Speaker 3: pay Microsoft money or open am money to host that
Speaker 3: fine tune model. And then on the top of that
Speaker 3: the charges for your tokens that you will be exchanging.
Speaker 2: It sounds like rags the way to go there.
Speaker 1: Yes, oh you know what you say that, Richard, But
Speaker 1: we have some mutual friends in Europe, and one in
Speaker 1: particular whose company decided to do away with RAG in
Speaker 1: their company because the percentage of errors that came back
Speaker 1: was too high. And I just asked this person literally
Speaker 1: two weeks ago, has your opinion changed? And no, no
Speaker 1: it hasn't. It's still still or prone. So what do
Speaker 1: you think about that?
Speaker 3: So RAG, you know, on theory, it looks simple, but
Speaker 3: one of the hardest challenge I have seen with the
Speaker 3: RAG is let's say I'm looking for a specific information
Speaker 3: from my company data. There is a limit on how
Speaker 3: much of the day, like how much of a token
Speaker 3: size or context size you can send to a large
Speaker 3: language model. So most challenging part with the RAG implementation
Speaker 3: is how I will make sure for that particular specific
Speaker 3: question or conversation I'm having, how I can get the
Speaker 3: least amount of data that is more relevant, like how
Speaker 3: I can get that, And that's where you know, then
Speaker 3: you have to have vector databases. That's where you need
Speaker 3: to have your ash eye search and those are like
Speaker 3: configuring those and getting those right are easier said than done.
Speaker 3: And let's say if you're let's say, if you the
Speaker 3: example you gave Carl is in case they're saying less errorrates, sorry,
Speaker 3: more errorrates on RAG, maybe you know they will go
Speaker 3: with fine tuning because there they have the build capacity
Speaker 3: in terms of you know, fine tune the model. Because
Speaker 3: fine tuning is not everyone's cup of tea, you know,
Speaker 3: to have certain skills. And also they may be have
Speaker 3: a use case where the cost will justify, you know,
Speaker 3: aware or the cost the amount they had to put
Speaker 3: in the fine tuning will justify in terms of the
Speaker 3: errate they're getting on the right side.
Speaker 1: Yeah, how much would it cost for the rag of
Speaker 1: the Franklin Brothers band to get the lead singer? Right?
Speaker 3: I don't know.
Speaker 1: Those are the questions you're gonna be asking, though, you know,
Speaker 1: we want we want accuracy because sometimes you can ask
Speaker 1: a question if you're if you're looking for information, right,
Speaker 1: real information. Typically you're asking a very specific question. Yes,
Speaker 1: the answer could be yes, or it could be no, yes,
Speaker 1: or the answer could be, you know, one of a
Speaker 1: hundred possibilities, and that is usually a fundamental question that
Speaker 1: business decisions will be made on because if it wasn't,
Speaker 1: what's the point of rag.
Speaker 2: Right.
Speaker 1: We need to ask these fundamental questions, and then you
Speaker 1: need to base decisions on See, we have to be really,
Speaker 1: really sure that that answer is correct.
Speaker 2: And one of these challenges I remember from you know,
Speaker 2: decades ago, where you only have to generate a report
Speaker 2: once that has inaccurate numbers, and everybody questions the numbers
Speaker 2: forever after that. Yeah, that's right, So you know, you
Speaker 2: can't afford to lose that confidence.
Speaker 3: And that's where you know, a lot of companies, especially
Speaker 3: after the rise of Jenny I especially the big companies
Speaker 3: where if they get out any wrong information or incorrect information,
Speaker 3: if there is like a significant financial penalty against it,
Speaker 3: the first thing they are doing is cleaning up their house,
Speaker 3: like cleaning the data. Like what if your data you
Speaker 3: have multiple versions of the data. One is saying good thing,
Speaker 3: but another saying is bad thing, and then somehow your
Speaker 3: AI search or whatever other two you're using picked up
Speaker 3: the bad information and then use that to the for
Speaker 3: you rag pattern.
Speaker 1: I remember Gary Short and Seth Warez both saying that
Speaker 1: the majority of their job, and this was maybe ten
Speaker 1: years ago, was cleaning data, you know, just to get
Speaker 1: more accurate. And so the ability the fine tuning, as
Speaker 1: you say, to omit things that are outliers or standard
Speaker 1: deviations that are way high or something.
Speaker 2: And we now up here in Canada we have a
Speaker 2: core precedent around this with Air Canada. So Air Canada
Speaker 2: deployed a chat bot using an LM for providing advice
Speaker 2: for travel, and a fellow used that tool to figure
Speaker 2: out if he could change a flight for it was
Speaker 2: a bereavement. Fares are called sover Yeah, a funeral or
Speaker 2: something like that. This is a great story. Yeah, And
Speaker 2: the software said, yeah, no problem, you can change that.
Speaker 2: And so then he went to actually change it and
Speaker 2: they said, no, you can't change that, said, well, the
Speaker 2: bot told me, and then he was forced to buy
Speaker 2: the ticket the last minute spend even more so forth.
Speaker 2: But he's now gone through the courts and the court ruled, hey,
Speaker 2: that you're using that chatbot as if it was an employee.
Speaker 2: You have the same liabilities to that employee. So if
Speaker 2: your employee gave incorrect information, you'd be held accountable for it.
Speaker 2: Your software, you're also held accountable.
Speaker 1: This is a great time to be alive in the
Speaker 1: you know, seeing the real genesis of AI taking off
Speaker 1: and getting more and more accurate in the in these
Speaker 1: kinds of stories really shape it well.
Speaker 2: And it's I'm happy to have those case laws in
Speaker 2: place because it's for me as the architects, sitting down
Speaker 2: with leadership saying we want to use this technologies, like
Speaker 2: here is the risk we are assuming, right, right, And
Speaker 2: so you know that's just why you spend money on testing.
Speaker 1: And that was a yes or no answer, right, It was.
Speaker 2: A little more complicated than.
Speaker 1: That, Can I get a refund? Yes or something along
Speaker 1: those lines or something along those lines.
Speaker 2: And yeah, you're right. It pretty much breaks down it
Speaker 2: exactly that, Carl.
Speaker 3: Yeah, and that's the reason. Like some of the customers
Speaker 3: I work with, they are pretty large customers, and one
Speaker 3: thing they're doing is hey, EI is great, but nobody
Speaker 3: should be launching anything because it going through some governance
Speaker 3: committee where they will be evaluating what exactly you're doing.
Speaker 3: You know, what technology you're using, whether it's going to
Speaker 3: be for internal users or for your customers, because if
Speaker 3: you do any mistake, and let's say if it's for
Speaker 3: your outside customers, you know, the implication of that can
Speaker 3: be tremendous.
Speaker 2: Yes, now, definitely the motion at this moment here we are,
Speaker 2: you know to a latter half of twenty twenty four
Speaker 2: is all internal apps. It's an hr bot, Yes right.
Speaker 2: It's those kinds of things where you're only upsetting the
Speaker 2: employee when you lie to them about how many vacation
Speaker 2: days they have.
Speaker 3: And again that's a good point. You got what, Richard,
Speaker 3: because vacation days is something that you shouldn't be relying
Speaker 3: on general to AI. You tell you, it should be
Speaker 3: coming straight from your employee handbook, where where you know
Speaker 3: you have full control over what information is getting from
Speaker 3: there and what information is surfaced.
Speaker 2: If this isn't a project that makes sense Pshan, what
Speaker 2: prout checks do like that seems like exactly, I'm not
Speaker 2: going to read the handbook. Yes, I'm going to pester
Speaker 2: HR for an answer, yes, and they're going to yell
Speaker 2: at me. So we make the bot to solve that
Speaker 2: problem and you.
Speaker 3: Can solve it easily. Now with Copilot Studio along with
Speaker 3: Azure Ai Studio as well, but co Pilot Studio, a
Speaker 3: lot of companies are also using it for customer centric
Speaker 3: BOTE outward bots like Business to Customer as well. And
Speaker 3: one of the reasons they are doing it because some
Speaker 3: of the red teaming and other stuff the co Pilot
Speaker 3: Studio team has done because not peny people understand when
Speaker 3: you launch anything external, especially at childbot like Microsoft learn
Speaker 3: It in twenty sixteen, where people will try to get
Speaker 3: some nasty stuff from it. And if you launch a
Speaker 3: public facing website chadbot, you know, anyone can start interacting
Speaker 3: with it and start asking some stuff that they shouldn't
Speaker 3: be asking or your company shouldn't be giving responsor to that.
Speaker 2: So business bring us back to outward facing is not
Speaker 2: there yet. It's too high.
Speaker 3: Risk y's high risk. Yes, but if you know, if
Speaker 3: you use the right tools, or if you know you
Speaker 3: know you have a good team in house, you can
Speaker 3: definitely use some of these tools where you can have
Speaker 3: a more confidence, you know, launching this outward facing application
Speaker 3: as well.
Speaker 2: I just debate that anyone has a good team at
Speaker 2: this point.
Speaker 1: What are the things that I think would help And
Speaker 1: it's definitely almost impossible with large language models that are
Speaker 1: trained on language right and try to predict the next
Speaker 1: word in their answer, but citing references. Right, if that
Speaker 1: Air Canada bot, you know, when it said yes you
Speaker 1: can get a refund and here's a link to the
Speaker 1: policy that you can read and decide for yourself, right,
Speaker 1: then it might have been a bit more accurate, and
Speaker 1: not not necessarily accurate, but it would have allowed the
Speaker 1: user of that bot to make a more informed decision.
Speaker 2: Yep.
Speaker 3: And I think KYL, like Microsoft co Pilot studior team
Speaker 3: should hire KRL because now he's letting all their good
Speaker 3: features indirectly via all these success stories. But yes, that's
Speaker 3: one of the advantage where this co pilor studio giates
Speaker 3: is if you are going with the rat pattern and
Speaker 3: let's say you're using your knowledge based then while it
Speaker 3: generate the information, it does give you information about the citation,
Speaker 3: like what are the source of this information and so
Speaker 3: that you can click on it and then you can
Speaker 3: verify Okay, this information looks great or I can then
Speaker 3: you know, use it with more confidence and then I'll
Speaker 3: go on with the next stuff.
Speaker 1: That's great. It's so good to hear. Yeah, ask chat
Speaker 1: GPT for a reference. Sometimes get that and on lessly.
Speaker 1: It doesn't know the way, it's bunchet. It's a very
Speaker 1: difficult thing to deal with. Yah.
Speaker 2: Yeah, I think this is where they it's just where
Speaker 2: they use the term grounding. I don't know why this
Speaker 2: term was necessary.
Speaker 3: Yeah, they use the grounding. Yes, Like, okay, we use
Speaker 3: this information from grounding. It's not like something the lllum
Speaker 3: made up and this is the source of it or
Speaker 3: proof of it.
Speaker 2: Can we just always be grounded like thing? Right?
Speaker 3: Like really like the people who have kids, you know,
Speaker 3: grounding me is totally different in their context.
Speaker 1: Yeah right, true enough. Or the people who like to
Speaker 1: walk barefoot out in the lawn every day because it
Speaker 1: helps their magnetism or something.
Speaker 2: Walking barefoot and grass is pretty good. Man. You don't
Speaker 2: have nice.
Speaker 1: It's nice. It's a nice feeling, but it's all it
Speaker 1: needs to be. There's some weird science that is associated
Speaker 1: with it would say not soccer, but okay, pseudoscience anyway.
Speaker 2: Now I appreciate that. I don't want software speculating on
Speaker 2: my van. I prefer it to say I don't know.
Speaker 3: Yes, and those kinds of things you can configure in
Speaker 3: co pilot studio, like you can have Okay, I'm launching
Speaker 3: this copilot and it's going to take care of only
Speaker 3: four or five things. Anything extra, people like, anyone ask
Speaker 3: it can just come back with I don't know or
Speaker 3: I'm not allowed to talk about that. Go through this
Speaker 3: link or talk to this person and then and do this.
Speaker 1: In chat GPT's defense, it does that, you know, when
Speaker 1: it doesn't have an answer, it says you should call
Speaker 1: the manufacturer or you know, contact the whatever the authority.
Speaker 3: One thing people don't understand is chigibt is a custom experience,
Speaker 3: Like you're not interacting with an LM directly like as
Speaker 3: an API. It's a customer application. A lot of guard rails,
Speaker 3: like Opena has put into it. Now they're launching a
Speaker 3: memory as well, so a lot of things are going.
Speaker 3: It's a custom app, so yes, if you have a
Speaker 3: team that can build a custom app like that, then
Speaker 3: yes you will get similar answer. But let's say if
Speaker 3: I'm a developer, I'm calling this LLM directly. You know
Speaker 3: most of this LM don't have a memory as well,
Speaker 3: Like the calls are stateless, so those kinds of things
Speaker 3: it may not say, and it may just give you
Speaker 3: completely like completely incorrect answer as well.
Speaker 2: Yeah, and I guess that that brings me back to
Speaker 2: this whole. Like the HR bot makes sense is if
Speaker 2: you can ground it on. I know you're not going
Speaker 2: to read the user they employee hand books, so I'm
Speaker 2: going to turn it into the ability for you to
Speaker 2: ask questions and it's going to reference accurately. And when
Speaker 2: I'm not when my certainty is low in the SAW
Speaker 2: where let software certainty low, it's like you should talk
Speaker 2: to HR about that. Because from an HR perspective, if
Speaker 2: I can take eighty percent of the stupid questions and
Speaker 2: just answer them so I'm only dealing with the twenty
Speaker 2: percent that are more complex, I'm pretty happy. Like that's
Speaker 2: a good outcome.
Speaker 3: You can use for HR, you can use it for
Speaker 3: your IT support as well, because it has connectors where
Speaker 3: it can talk to your own knowledge base and that
Speaker 3: knowledge base can be in service.
Speaker 2: Now yeah, no, I just put I have an automated
Speaker 2: responder just says you try turning it off and turning
Speaker 2: it back on again.
Speaker 3: Like said, yeah, my favorite code is be smart restart.
Speaker 1: You know there you go. Nice? Nice works for phones too. Yep, yes,
Speaker 1: all the time. And Pat and Dwayne Patrick Kines Douain
Speaker 1: Laflat from security this week say you should be restarting
Speaker 1: your phone at least once a week at least if not,
Speaker 1: you know, three or four times a week. Yeah, all right,
Speaker 1: So where do we go from here? Is there more
Speaker 1: that we haven't discussed? Like how is it expensive? How
Speaker 1: do we get started?
Speaker 3: Okay, so cost wise, even though it's a part of
Speaker 3: Power Platform, its cost is a little bit different. It's
Speaker 3: just like ash. Everything is based on the consumption. So
Speaker 3: the minimum pricing point is you had to bot. You
Speaker 3: had to buy a package for twenty five thousand messages,
Speaker 3: which cost two hundred dollars. And again this is a
Speaker 3: retail price, like if you have an enterprise agreement, you
Speaker 3: may be getting some discount on that, but that brings
Speaker 3: up a question is what exactly a message is? Right?
Speaker 3: A message is basically anything like when I ask anything
Speaker 3: to my copilot, if that's going to trigger any kind
Speaker 3: of response or any kind of AI agent a call
Speaker 3: behind the scene, that constitute as a one message. And
Speaker 3: if I'm using generative AI features, then just for the
Speaker 3: sake of simplicity, what Microsoft has done is any answer
Speaker 3: that has generative AI in it, it is just going
Speaker 3: to count as the two messages. So if I ask
Speaker 3: a question you know how many PTOs are there, and
Speaker 3: I'm using some of the generative EI features and then
Speaker 3: that come back, then it just counts as the two messages.
Speaker 1: Now it's two hundred dollars a month for twenty five
Speaker 1: thousand messages a month. Does that mean that if I
Speaker 1: only use one thousand messages or you know, less than that,
Speaker 1: am I only going to get charge point zero zero
Speaker 1: eight cents per message?
Speaker 3: No? No, you have to, Like the minimum is two hundred.
Speaker 1: That's what you have, whether you use it or not.
Speaker 3: Yes, And that's one story which is really not good
Speaker 3: in my opinion right now, because you buy that license
Speaker 3: for per tenant. Okay, but what if I'm working for
Speaker 3: a really big organization where I have thousands and thousands
Speaker 3: of employee and ten departments are there. Every department has
Speaker 3: their own copilot and how exactly we're going to manage
Speaker 3: the pricing because those twenty five thousand message I can
Speaker 3: be exhausting in one hour or two hours.
Speaker 2: Yeah sure, yeah, yeah, it feels like it's beta pricing,
Speaker 2: Like it's just pricing because you're doing a beta test project,
Speaker 2: not actually deployment level yet. And so now I'm starting
Speaker 2: to feel like the relationship between copile studio and as
Speaker 2: your AI studio is the same relationship between power platform
Speaker 2: and visual studio.
Speaker 1: Correct, right, Yeah, that makes sense to me, Richard.
Speaker 2: So if you're the front end stuff, the place to experiment,
Speaker 2: the price to prototype copilot studio when you want to
Speaker 2: get into precision and tuning and more skilled people AI
Speaker 2: studio knowing that anything you build an AI studio you
Speaker 2: can surface in copilot. Yeah, and all right, I dig this.
Speaker 2: This is huge in development. Like you said, we did
Speaker 2: that show with vishuas earlier this year where we talked
Speaker 2: about that same dynamic between power platform and dot net right.
Speaker 3: That the and I know yeah, which Fus is a
Speaker 3: huge advocate of future development. In fact, he's the one
Speaker 3: who taught me all this concept, and I agree with
Speaker 3: it because the more and more I work with enterprise customers,
Speaker 3: like there's always the case where developers, like good developers
Speaker 3: are a rare commodity. Yeah, and you'll agree that, you know,
Speaker 3: it's very hard to find good developers, Like how about
Speaker 3: then we create some tools where you need developers only
Speaker 3: when that tool cannot do certain things for you, right.
Speaker 2: Right, But the other part is like I never look
Speaker 2: at the power platform folks, and I guess it's the
Speaker 2: copilot studio folks. As these are the lessers, these are
Speaker 2: the domain experts. They're actually going to save you time
Speaker 2: because they know more about the problem space than you do. Right, Like,
Speaker 2: my struggle with really great developers is how much time
Speaker 2: they spend having to learn the domain. Where if you
Speaker 2: have a good domain expert relationship going there, you can
Speaker 2: focus more on breaking those bits down and go, okay,
Speaker 2: well that's where some custom code needs to live. That's
Speaker 2: there is no easy solution there. We need to build something,
Speaker 2: take a spike, you know, those kinds of things. Like
Speaker 2: that's a really constructive way to work quickly.
Speaker 3: And I'm seeing like so I'm talking about this conversation
Speaker 3: AI or Microsoft Board framework since twenty seventeen and since
Speaker 3: last Ignite. I'm saying like just a huge interest from
Speaker 3: developers as well, like pro developers thinking about or are
Speaker 3: eager to learn about co pilot studio. Like a couple
Speaker 3: of weeks before, I was at dev Intersection or next
Speaker 3: Gen EI conference in Vegas and I did session and
Speaker 3: a workshop on copilot Studio and its attendance was more
Speaker 3: than some of the popular you know, pro developer sessions
Speaker 3: or confront or workshops were there.
Speaker 2: Yeah, that's great, but any Yeah, I appreciate this because
Speaker 2: you start now, you start thinking about rethinking the UX
Speaker 2: of an application, a lot less buttons and sliders and
Speaker 2: a lot more texts and most of the screen taken
Speaker 2: up with it presenting information to you based on your requests.
Speaker 3: Right, And it doesn't have to be only this. It
Speaker 3: can be. Yes, for some things you'll be using this,
Speaker 3: but for some more traditional or more things where you
Speaker 3: definite need to have you like proper UI with multiple
Speaker 3: buttons and multiple sliders, yes you'll have that, but this
Speaker 3: can be your beginning point where I'm every day I'm
Speaker 3: starting my work. I'm just coming here, you know, doing
Speaker 3: my stuff. I asking copilot to do a bunch of
Speaker 3: stuff for me. And I always give the example, you know,
Speaker 3: Javis in Iron Man. That's a good example. Hey, there
Speaker 3: are certain things if Javis can do. Let Javis handle that.
Speaker 3: And I'm going to focus more on the complicated stuff
Speaker 3: behind the scene.
Speaker 2: Yeah, I'm waiting for office to bet on this tap. Right,
Speaker 2: Like you think about immediately think in terms of the
Speaker 2: old Alan Cooper of the Sovereign app. Right, what's the
Speaker 2: first app you go to? Now? For me? It's still Outlook,
Speaker 2: you know. That's why I'm sad.
Speaker 1: Six five threads one for me.
Speaker 2: There's also teams, like a lot of folks, that's their
Speaker 2: starting point. And you start thinking about the LLM interface
Speaker 2: to this, how you would start your day? I mean
Speaker 2: right away, your first step in either app is triage. Yes, right,
Speaker 2: what's the most important thing? Right? What do I need
Speaker 2: to deal with?
Speaker 1: Now?
Speaker 2: You know, where are the crises and so forth? Like
Speaker 2: you could see a good agentic type tooling in this,
Speaker 2: helping this or through those priorities. I mean, I would
Speaker 2: argue the first triage is how many of these has
Speaker 2: the bot already responded to?
Speaker 3: MM?
Speaker 2: Hmm right, I've not only sorted these out. Here's the
Speaker 2: ones I think. Here the answers are, are you okay
Speaker 2: with this? It's like yeah, yes, and that that that
Speaker 2: that that that okay? What are the ones I need
Speaker 2: a trioge like my wife could my life could be
Speaker 2: way better?
Speaker 3: Yeah. I think that story is still evolving with Copilot
Speaker 3: for Microsoft sixty five. Uh, and they do have one
Speaker 3: capability there where in case you would like to extend
Speaker 3: the functionality of out of the box co pilot there
Speaker 3: you can use Copilot Studio and its license is already
Speaker 3: covered as part of your Microsoft Pay sixty five Copilot license,
Speaker 3: so I can listen.
Speaker 2: Prashan, I'm an old grizzled Microsoft observer. At this point,
Speaker 2: you're not grizzled. I've come to appreciate disper he said
Speaker 2: on a podcast. You will know a technology is being
Speaker 2: taken serious by Microsoft when they bet their mark key
Speaker 2: products on it. Yes, right, when that shows up in Outlook. Okay,
Speaker 2: they're committed, like, no two ways about it. That's why
Speaker 2: we bought into Active Acts or you know that whole
Speaker 2: control surface. That's why we bought into Ola, and it's
Speaker 2: also why we were suspect of WPF when Office never
Speaker 2: took on WPF. It wasn't until Visual Studio took out WPF.
Speaker 2: They're like, okay, right, Like that's there's a reason to
Speaker 2: take the cues this way as to how far down
Speaker 2: the path these products really are. Like I feel like
Speaker 2: these are still experimental products because the Microsoft seems to
Speaker 2: feel these things are still expound products.
Speaker 1: Well, they may be working on them, but they haven't
Speaker 1: surfaced yet. Yeah, I have a question for you, totally
Speaker 1: off topic, well not really. Apple's coming out very soon
Speaker 1: with probably out by now, the new iPhone that has
Speaker 1: AI Apple Intelligence not artificial at all, and I kind
Speaker 1: of I feel like I get it. They're basically taking
Speaker 1: the SII experience and exposing you know, GPT four oh
Speaker 1: on the back end so that Siri will become more intelligent.
Speaker 1: And I just wonder if you know anything about that,
Speaker 1: because I, you know, if that is as easy as
Speaker 1: it sounds. Man, I'm going to get in my car,
Speaker 1: turn on car Play, and just start talking to my
Speaker 1: car and it's going to be night rider.
Speaker 2: You know, what do you think for Shadow? I have thoughts.
Speaker 3: So I'm not an Apple person. I use most of
Speaker 3: the Android stuff. But after saying, like some of the
Speaker 3: demos from open Ei recently, the demo ware, the latest
Speaker 3: version of GPT four oh is multimodel, and then we
Speaker 3: can have multiple conversation and we can also change the
Speaker 3: context in between. I think we are getting there as
Speaker 3: long as they figure out how to run this LLLMS
Speaker 3: or all these models cheaply on the devices. Yeah, I
Speaker 3: think we are there, like maybe one or two years.
Speaker 3: Once they put some SA safeguards in it. And let's
Speaker 3: say I'm driving a car and if that LM can
Speaker 3: execute on my car's hardware, we should be there.
Speaker 1: Are you saying that the LLLM stuff is on the phone,
Speaker 1: they're not just calling an API out to GPT to
Speaker 1: do the work.
Speaker 3: Yeah, Like once that gets mainstream, like where we will
Speaker 3: have more and more this small language model is doing
Speaker 3: all this stuff, but they're actually executing on the local stuff,
Speaker 3: we'll have like much better experience, and we'll also see
Speaker 3: more and more push from these companies as well, because.
Speaker 1: I don't know if that's the case now. It seems
Speaker 1: to me that they're just calling out too with an API,
Speaker 1: but that.
Speaker 3: Is a very expensive right now, Like if you look
Speaker 3: at yes, they can do it for a few months
Speaker 3: or a few years, but that's not really long sustainable
Speaker 3: business model because all these lllms require really bf infrastructure.
Speaker 3: That's the reason why I think. I don't please don't
Speaker 3: quote me on this, but I heard even though openan
Speaker 3: AA is making so much money, like billions of dollars,
Speaker 3: they're still not profitable because they had to spend a
Speaker 3: lot of money behind the scene on the infrastructure to support.
Speaker 2: That far and far more.
Speaker 1: Yes, what was your thoughts, Richard?
Speaker 2: I think Apple is late to the AI game and
Speaker 2: needed to announce something, and the fact that they keep
Speaker 2: pushing back the release of it is proof they don't
Speaker 2: know what they just committed to. Ooh, and they're panicking.
Speaker 2: You know, we expect Apple to come late with a
Speaker 2: refined product that has clearly not happened. The fact that
Speaker 2: they've already pushed the dates back shows they they said
Speaker 2: they were going to jump before they jumped, and now
Speaker 2: they're struggling because it is a very unrefined product. It
Speaker 2: is not asked Apple asque at all, but they could
Speaker 2: not afford to not say something good way to hear,
Speaker 2: so they had You know, they only do one show
Speaker 2: a year. They do that show in the WWDCS in June.
Speaker 2: Were you really going to wait till twenty twenty five?
Speaker 2: You couldn't, so you said it, and now you're trying
Speaker 2: to make it come true. That's not very apple asque,
Speaker 2: but all the hallmarks are there. The only reason we
Speaker 2: might give a pass is because Zappli you don't do that.
Speaker 2: It looks like they did this time.
Speaker 1: Well that feels like the end of our show, Prashant.
Speaker 1: This has been very enlightening and I appreciate you putting
Speaker 1: up with my digressions and you know, little voyages into
Speaker 1: stories and things. But man, this is just chocolate block
Speaker 1: full of great meaty goodness.
Speaker 3: So thank you, thank you, thanks for having me. And
Speaker 3: it was really it was my pleasure, you know, pleasure
Speaker 3: on all of my side for.
Speaker 1: This fantastic all right, and we'll talk to you next
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