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Speaker 1: Hey guess what, it's dot net rocks from Bill. This
Speaker 1: is the last show we were recording. I'm Carl Franklin.
Speaker 1: We're here with Sef Juarez and we'll be talking to.
Speaker 2: Get that out of there. Get a little fam Sorry,
Speaker 2: you gotta get it out. You know, we're a little punchy.
Speaker 2: This is not only the last show of the day,
Speaker 2: but it's the last show up. Oh, if you got
Speaker 2: a punchy a show with a punchy dude, it's gonna
Speaker 2: be great.
Speaker 1: It's gonna be You've been working hard, that's right. I
Speaker 1: just want to start talking to Seth. Do we really
Speaker 1: have to do the intro stuff? You should do the interesting?
Speaker 2: We gotta do it. You got to pay so you
Speaker 2: know it's episode nineteen fifty seven. Wow, that's the year
Speaker 2: I was born. It's not true. It could be in
Speaker 2: a different universe very close. It could be in a
Speaker 2: parallel universe. If you know quantum theory, the many worlds
Speaker 2: as you know, Richard In posits that perhaps there's another
Speaker 2: universe where I was born in nineteenfty seven. There's a
Speaker 2: million universes. That happened, sixty seven year old set. I
Speaker 2: just wanted to use the word posits in the thing.
Speaker 2: And so now my job is I'm done. You must
Speaker 2: have a master's degree.
Speaker 1: I have something. So do you want to do the
Speaker 1: history first? You want to do I didn't do the history.
Speaker 1: So what happened in nineteen fifty spot Nick one?
Speaker 2: Okay, the first the first artificial salarate over the world,
Speaker 2: and then sput Nick two. They put a dog in
Speaker 2: it and she died. No, actually no, she was in
Speaker 2: the Guardians of the Galaxies. Right, wait, that's not real.
Speaker 2: We want the historical documents and it's worse than any think.
Speaker 2: She didn't she didn't die and re entry, she didn't suffocate.
Speaker 2: She overheated. There wasn't temperature controls oh thanks.
Speaker 1: Sometime around here in the nineteen late nineteen fifties, electric
Speaker 1: boat from General Dynamics was established in the Nautilus.
Speaker 2: That was no electric boat? Is that? Is that a
Speaker 2: like a line dance? What is fifty five? Isn't it?
Speaker 2: The electric boat? Is that like the electric slide? But no, no,
Speaker 2: no waste? The nuclear submarine? Oh yeah, the first nuclear summer, theatlis.
Speaker 1: That's right, Okay, I'm sorry. I had a forward memory of.
Speaker 2: I also usually referenced a computer, and in nineteen fifty seven,
Speaker 2: IBM released a compact computer, the six', ten only eight hundred,
Speaker 2: pounds much smaller than the previous. Ones it really was
Speaker 2: an improvement there you. GO i, mean if you want
Speaker 2: to get, fit you get one of those commonract eight
Speaker 2: hundred pounds. Computers also released the first compiler for. Fortrend hey, yo,
Speaker 2: wow still running some code out. There there's as for
Speaker 2: trend dot hold. On So i'm not. Kidding there are
Speaker 2: some linear algebra operations that were written In fortrand that
Speaker 2: people were, like let's just not do that. AGAIN i
Speaker 2: think it's like some WEIRD langsos method for, computing like
Speaker 2: singular value, decomposition that they're, like let's just not do that.
Speaker 2: Again somebody did, that and we're, gonna we're, gonna we're
Speaker 2: gonna honor them and we're just gonna leave that. Alone. Wow.
Speaker 2: WOW i actually contributed this.
Speaker 1: SOMEHOW i actually remember taking A fortran. Class but it
Speaker 1: was like in the. Eighties, yeah a little further, on yeah,
Speaker 1: too it was like in THE i did fourtran.
Speaker 2: Three oh is there a sound? Effect weing? At BUT
Speaker 2: i love? It all? Right, well, anyway anything else in
Speaker 2: nineteen fifty there was a lot Of there was a
Speaker 2: lot of. Things there's new clear. Test it's all kinds
Speaker 2: of horrible stuff listed with, computers all, right and speaking
Speaker 2: of atomic, fires what are we talking about? Today, well
Speaker 2: we gotta finished the fish such a. Hurry i'm not
Speaker 2: in a.
Speaker 1: Hurry, okay all, right let's do the better note of
Speaker 1: framework roll the, music all. Right, so as you, Know
Speaker 1: Jeff fritz AND I Jeff frits For microsoft And big
Speaker 1: INTO ai And. Blazer we do This blazer puzzle.
Speaker 2: Show AND i was just.
Speaker 1: Talking to him about all of the cool THINGS i
Speaker 1: saw at the, keynote including, you my friend oh hey
Speaker 1: uh at the keynote it build and of course he's
Speaker 1: hipped to all of this, stuff and we were kind
Speaker 1: of talking about how and we talked about this with
Speaker 1: some of our guests this. Week about how the prompts
Speaker 1: are becoming code and instead of having code, libraries people
Speaker 1: are going to start sharing prompt. Libraries Prompt so what
Speaker 1: Did fritz point me? To he pointed me to sharp,
Speaker 1: site which is all these prompts for Doing blazer applications
Speaker 1: with the.
Speaker 2: Agent oh, interesting do they? Work?
Speaker 1: Yeah in good hub co pilot so code style and,
Speaker 1: structure naming, conventions dot net specific, guidelines sho air handling and,
Speaker 1: validation like all of these great prompts to help you
Speaker 1: create Great blazer.
Speaker 2: Applications that's all the. Agent how about? THAT i think that's.
Speaker 2: Great anything that we can do to further the work OF,
Speaker 2: ai because you, Know i've BEEN i was like on
Speaker 2: show like, SEVENTEEN i don't, know something like that eighteen
Speaker 2: AND i was, like machine learning is the future AND
Speaker 2: i was like What and we didn't understand. It then
Speaker 2: we still, don't but at least it's. Working but it's,
Speaker 2: true and you just still understand. It we just, believe you,
Speaker 2: KNOW i really excited that people are, like let's use
Speaker 2: this stuff right five seventy Six july of twenty. Ten
Speaker 2: oh my, gosh aires on machines in eighteen years AND
Speaker 2: i just get more. Beautiful i'm glad this is. True
Speaker 2: in this, medium it's really hard to, know but it's for.
Speaker 2: SURE i want you to imagine, it all.
Speaker 1: Right richard Save, us who's talking to us, today.
Speaker 2: Grabbed to comment off a show nineteen, nineteen which you
Speaker 2: did in twenty twenty, four a little more recent In
Speaker 2: october With Prascent boy here he's AN mvp DOING ai
Speaker 2: related stuff that he wanted to talk, about and we
Speaker 2: did talk About Copilot, studio WHICH i believe has now
Speaker 2: been renamed because. Reasons, yeah And John tallon had this
Speaker 2: great comments from a few months. Ago he, SAID i
Speaker 2: enjoyed the. Show chat BASED ai apps are quick to stand,
Speaker 2: up but tough to get right every time a man
Speaker 2: rag dazzles that. First but yet it's limited without deep. Engineering.
Speaker 2: Ooh in real, conversations not everything is a, question and
Speaker 2: bought led chats are no. Different we must parse the user's,
Speaker 2: intent evaluate the quality of their, input then apply a
Speaker 2: chain of erication to fact checkbox. Responses i'm a practice
Speaker 2: director in data AND II, ai And i've worked in Gen,
Speaker 2: Ai AZURE Ai studio and Co Pilot, studio and despite
Speaker 2: the Marketing Copilot studio isn't for your Quote jim From.
Speaker 2: Accounts it's for seasoned consultants Like brashant who know how
Speaker 2: to use the. Platform using it for complex, interactions like
Speaker 2: a rocket launcher on a. Fly, yeah it, works but
Speaker 2: it's a little over. Complicated rocket launcher on a. Fly it's,
Speaker 2: like use. It, yeah let me say that. Again using
Speaker 2: it for complex interactions is like using a rocket launcher
Speaker 2: on a.
Speaker 1: Fly it's kind of taken a nod from my term
Speaker 1: swatting a fly with a.
Speaker 2: Buick we had a term swatting a fly with a,
Speaker 2: shotgun and my algorithms professor was, like we have an
Speaker 2: algorithm for sorting that's faster than en log, in but
Speaker 2: only with giant data. Sets it's like swatting a fly
Speaker 2: with a. Shotgun, yes, yeah. Nice we should only explose
Speaker 2: enough complexity to get the job. Done letting use His
Speaker 2: harner's AI's potential without feeling. Overwhelmed after, all the goal
Speaker 2: is to DEMOCRATIZE, ai not. Intimidate what year was? That
Speaker 2: that was last? Week, yeah this is like a few months.
Speaker 2: Ago the answer is you just, wait wait till next.
Speaker 2: Years you. Know one of the things that CAME i
Speaker 2: felt came out of build was this sense that nobody's
Speaker 2: dominating in the space at. All, yeah there's lots of different,
Speaker 2: models there's lots of different. Tools the fact that everybody
Speaker 2: jumped ON mcp is. Clear nobody has the confidence to, go,
Speaker 2: oh we'll go it. Alone we're all trying to find
Speaker 2: standards in inter interapt with each. Other. Yeah BUT i
Speaker 2: mean even EVEN mcp has. Limitations AND i have thoughts on,
Speaker 2: that And i'm tired And i'm just gonna say WHAT i.
Speaker 2: Think Oh, NO mtcp definitely has some. Issues there's no
Speaker 2: two ways about. That but let me wrap up With john. Here,
Speaker 2: john thanks you so much for your, comment and the
Speaker 2: copy of Music coba is on its way to. You
Speaker 2: and if you'd like a copy Of Music, COBE i
Speaker 2: write a comment on the website at dot NetRocks dot
Speaker 2: com or on the. Facebooks we publish every show, there
Speaker 2: and if you comment there and every read it on the,
Speaker 2: show we'll send you a copy Of Music. Koba And music.
Speaker 1: To Code by is still going. Strong twenty two tracks
Speaker 1: to help you get in the zone and focus twenty
Speaker 1: five minutes each to coincide with The pomodor, method and
Speaker 1: they will help.
Speaker 2: YOU i actually bought the first. Ones yeah a long
Speaker 2: times years.
Speaker 1: Ago, yeah it was only three AND i did A,
Speaker 1: KICKSTARTER i did.
Speaker 2: THREE i got the. THREE i still have THE mp
Speaker 2: three is somewhere in my junk drawer of one.
Speaker 1: Drive, well now you can get the whole collection IN
Speaker 1: mp three flak en. Wave so you can go to
Speaker 1: music To code by dot net for that or you,
Speaker 1: know you could just send us a comment or leave a.
Speaker 2: Comment, yeah, absolutely and reading on the show you'll get
Speaker 2: a copy of MUSIC o.
Speaker 1: Bye, yeah all, Right So i'm going to introduce.
Speaker 2: You for real areas here we go That i'm introducing.
Speaker 1: It, yeah that voice that you've been listening to the
Speaker 1: can who it is Is Seph wares and he works
Speaker 1: in THE ai platform group At. Microsoft he received his bachelor's.
Speaker 1: Degree that's, it that's your. Title Now i'm going to
Speaker 1: talk about your education because that's the part of your.
Speaker 1: Bio we see his bachelors and computer science at you
Speaker 1: n L v saying place that Guy fieri got his.
Speaker 2: Degree we were. Partners he's a he's a machine learning master.
Speaker 1: Absolutely with a minor in mathematics and complete his master's
Speaker 1: great at The university Of utah and the field of
Speaker 1: computer science And. SETH i have a picture that BECAUSE
Speaker 1: i was in the front row at the build AND
Speaker 1: i took a picture of.
Speaker 2: You oh what WAS i? Doing? Well you you opened the.
Speaker 1: Show you came out and, SAID i Am seth and you,
Speaker 1: know with with somebody, else AND i DON'T i didn't recognize.
Speaker 1: Her BUT i took a picture of you two and
Speaker 1: here it. Is oh my, gosh That's. Kadasha, yeah she
Speaker 1: is delightful human. Beings SO i sent this To, kelly my,
Speaker 1: wife you, know, yes of course she actually met you
Speaker 1: BEFORE i met.
Speaker 2: You, yes she did, so and, she.
Speaker 1: You, know she says back to me and she, says
Speaker 1: that's a great. Picture but who is that black lady
Speaker 1: With Ricky?
Speaker 2: Martin but, WOW i do get more. Beautiful AND i, said,
Speaker 2: again this is the medium for me to say these
Speaker 2: kinds of. THINGS i am getting more. Beautiful look at the.
Speaker 1: Picture your name is not on the, screen it's it's her. Last,
Speaker 1: yeah so she wouldn't know and she didn't recognize.
Speaker 2: You that is so. Funny then WHEN.
Speaker 1: I told, her she, says, oh, yes that is.
Speaker 2: YEAH i actually owe her a. Call she called me
Speaker 2: the other day AND i haven't called. Back, oh, okay she's,
Speaker 2: delightful she. Called she checks up on. Me she does. That, yeah,
Speaker 2: yeah so. Nice well you do need. Supervision it is.
Speaker 2: True my wife has had me now for twenty two ish,
Speaker 2: years and so during THE covid she may be. Tired
Speaker 2: during THE.
Speaker 1: Covid, times she reached out to friends of mine and
Speaker 1: hers that she hadn't talked to in years and, Said,
Speaker 1: hey you know, What i'm just calling BECAUSE i want
Speaker 1: to check.
Speaker 2: In it was.
Speaker 1: GREAT i don't want, anything you, KNOW i just wanted to, Say, Hi.
Speaker 2: GOSH i wish the niceness would rub off on other. People,
Speaker 2: yeah it's not working. Right all.
Speaker 1: Right so we're talking about agentic, hey AND i got
Speaker 1: to tell, You i'm blown away by WHAT i saw.
Speaker 1: HERE i, MEAN i know that there have been other
Speaker 1: agents and products that work with agency and all that,
Speaker 1: stuff and the guys From manet have been into, it,
Speaker 1: right but they all said the same. Thing it's, Like,
Speaker 1: Wow microsoft is kind of catching up to what these
Speaker 1: guys have been. Doing AND i hope they dominate because
Speaker 1: they have the, power, Right microsoft has the power Of
Speaker 1: azure and all of these. Things and WHEN i learned ABOUT,
Speaker 1: mcp we've been talking about anybody who's listened to the
Speaker 1: last six, shows oh, yeah knows about this about the,
Speaker 1: okay is it.
Speaker 2: Model model context context.
Speaker 1: Protocol so this is the protocol by which agents can
Speaker 1: talk to your resources to do things like talk to
Speaker 1: YOUR sql, server talk To azure resources and all that.
Speaker 1: Stuff and you can just tell it to go do, things,
Speaker 1: yes and it will figure them.
Speaker 2: Out.
Speaker 1: Yes the combination of AN llm like get Hub copilot
Speaker 1: AND mcp connections to other resources just blows my. Mind
Speaker 1: it changes the.
Speaker 2: Game, Yeah i'd love to get more low level, though
Speaker 2: so folks have an understanding of what an agent actually
Speaker 2: is because there is This what happened was and you
Speaker 2: were there with me for fifteen. Years i've been in
Speaker 2: this space for fifteen. Years nobody, cared and then we
Speaker 2: flip from nobody cared to this stuff is like jungle,
Speaker 2: magic or this stuff is like going to throw you
Speaker 2: into outer space or.
Speaker 1: So we didn't have to create the. Models, yeah that
Speaker 1: was the hard.
Speaker 2: Thing it's. True but but what happened is we kind
Speaker 2: of missed the in between part Of, hey let's explain
Speaker 2: what these things are doing and why it's actually. Useful
Speaker 2: because there's a THERE'S i don't want. To as you,
Speaker 2: know we had SEVERAL ai winters because of the over,
Speaker 2: hype AND i AM i am concerned that if we
Speaker 2: overhype too much that there's going to be a correction
Speaker 2: when there really doesn't need to be because it actually
Speaker 2: this time does do qualitatively cool. Stuff, yes and that's
Speaker 2: What i'm worried. ABOUT i, mean the winters were mostly
Speaker 2: induced by the fact that there was a limited utility
Speaker 2: what was being made, exactly and there was a limited
Speaker 2: of computational power to actually make the model the thing
Speaker 2: we wanted them. To hinton's famous papers actually the paper
Speaker 2: talking about backpropagation SAYING i can't do with this equipment
Speaker 2: in like nineteen eighty four mm. Hmmm and then In
Speaker 2: suskovar does off that paper in two thousand and nine
Speaker 2: and they start working on the image net. Problem, yes
Speaker 2: and those models are quite simple as compared to the
Speaker 2: ones that are now FOR. LMS i, MEAN i can
Speaker 2: get into the architecture and stuff of. It they're just much.
Speaker 2: Bigger and the thing about the thing about neural networks
Speaker 2: of that size Is i've spoken To Turing award winners about,
Speaker 2: like how do you shape these actual? Models to do
Speaker 2: the things you, Want and the reality is we don't
Speaker 2: really have a good understanding or. Explanation but what happens
Speaker 2: is they're innovations that come out like a transformer model
Speaker 2: with attention that was really designed to do translation that,
Speaker 2: somehow when you made it, bigger learned language, well presented
Speaker 2: language in a form that you could anthropomorphize. Correct and
Speaker 2: that's that's the Part richard does not like at. All
Speaker 2: the thing about it is is WHEN i picture these
Speaker 2: things in my, head it's like the matrix for, me
Speaker 2: Where i'm picturing like there's, weights multiplying vectors and then
Speaker 2: going over some kind of smoothing function to, remove you,
Speaker 2: know the linearities so that these scenes can predict complex.
Speaker 2: Things but in the, end all these things are doing
Speaker 2: is you're getting a string of words that are broken
Speaker 2: up into vectors that get passed in and multiplied by
Speaker 2: the transformer model to figure out how these things are,
Speaker 2: related pushed through a giant neural network multiple, times and
Speaker 2: the output is a giant vector of the entire space
Speaker 2: with a probably distribution that says which token should you
Speaker 2: put out? Next so when it looks like it's, typing
Speaker 2: it isn't it's just going back through the, model and
Speaker 2: that's what these things are. Doing and as, programmers the
Speaker 2: only thing we can control is what goes in the
Speaker 2: front the. Prompt and that's the beefy, bit right that
Speaker 2: people don't. Understand at that, point when we first started
Speaker 2: doing this, stuff it was magical for. Chat, Yeah but
Speaker 2: then early, on about maybe like six months into, it
Speaker 2: folks were starting to use these things for something that
Speaker 2: we liked to THAT i like to call intent. Mapping
Speaker 2: Cassie brevy AND i worked together for a couple of
Speaker 2: years and that's what we called, it where we would
Speaker 2: make THE lm like look at a block of text and,
Speaker 2: say is this question a support or a sales? Question
Speaker 2: and it was at that point that these, things because
Speaker 2: we allowed them to decide is it a support or
Speaker 2: a sales? Question when the code, branched those things all
Speaker 2: of a sudden were imbued with this notion OF i
Speaker 2: can make a CHOICE lm on what this thing. Is
Speaker 2: and at that point those things became, agents, yes and
Speaker 2: and and because we trusted them to answer our. Questions. Yeah.
Speaker 2: Yeah but but but nottice that WHEN i was doing this,
Speaker 2: ENGINEERING i narrowed the task to very specific you will
Speaker 2: say either support or, sales and that's. It and what
Speaker 2: that did is it allowed me to create a prompt
Speaker 2: that made it so that any user input that we
Speaker 2: put in, it it would tell me support or sales every.
Speaker 2: Time and my code had an if statement if support do,
Speaker 2: this if sales do, this, right and that at that,
Speaker 2: point that is the most simplistic way of thinking about an.
Speaker 2: Agent when AN lm has the has the ability to
Speaker 2: choose reason overset of data and then act on the
Speaker 2: reasoning you've. Generated and effectively that became a squishy programming
Speaker 2: control structure and that's what an agent is. Fundamentally but you're,
Speaker 2: like but it's got to be more than. That seth
Speaker 2: what about the tools? Stuff at that people are, like,
Speaker 2: well let's do structured output so that it could output adjacent.
Speaker 2: Thing and if we can output adjacent, thing why can't
Speaker 2: we output adjacent Thing that says this function with these.
Speaker 2: Parameters and at that point those things became tools and
Speaker 2: now instead of supporter, sales it, said run this function
Speaker 2: with these. Parameters and that is the extent of all
Speaker 2: of the, magic sauce of all of anything.
Speaker 1: Agentic NOW i want to say something Here, seth you
Speaker 1: are among some of the most brilliant people That i've ever.
Speaker 2: Met, oh thank.
Speaker 1: You richard is one of. Them you are one of.
Speaker 1: Them but the other people are like dev express. People,
Speaker 1: Yeah Mark miller And Richard morris like these guys and. You,
Speaker 1: NOW i want to tell the listeners if you don't
Speaker 1: know Who seth. Is, seth you had an open SOURCE
Speaker 1: AI mL library Before microsoft hired. You, yeah before they
Speaker 1: Had AZURE. Ai you had this open source. Library we
Speaker 1: did a thing about it on D NR tv that
Speaker 1: showed it to.
Speaker 2: ME i didn't understand. It, yeah of COURSE i kind of.
Speaker 2: Did but but you were doing.
Speaker 1: That you've been doing this longer Than microsoft has been doing.
Speaker 2: It way before it was. COOL i was doing before
Speaker 2: it was. Cool the secret, sauce, though is machine learning
Speaker 2: has been happening since nineteen fifty. Six Frank rosenblatt The,
Speaker 2: perceptron which is the basic foundation OF.
Speaker 1: Ski so that was the last, show righteen Fifty.
Speaker 2: Oh, hey, yeah let's call back To Yesterye, frank we've missed.
Speaker 2: Now but the reality is That microsoft's been DOING ai
Speaker 2: in the form of machine learning for a very long.
Speaker 2: Time WHAT i was doing IS i was trying to
Speaker 2: bring it to the dot net community BECAUSE i recognize
Speaker 2: that there is a space for algorithms which are learned
Speaker 2: instead of, programmed AND i thought that IF i could
Speaker 2: do a. Thing OBVIOUSLY i haven't kept up on that
Speaker 2: for a while because it was just. Me BUT i
Speaker 2: implement a whole set of stuff to make that. Easier
Speaker 2: but my goal has been for the last you, know
Speaker 2: fifteen twenty, YEARS i want people to know exactly what
Speaker 2: these things are, doing so that with, confidence you can
Speaker 2: make them do even cooler.
Speaker 1: Things, right, yeah, Yeah and so now you're At, microsoft
Speaker 1: you're working on THE ai.
Speaker 2: Stuff that's The azure. Stuff that's. Right The Italians channel,
Speaker 2: Nine oh, yeah, No channel nine was. Great you had
Speaker 2: such a good. TIME i really. GOOD i love. That
Speaker 2: and then somebody figured out actually knew them all, stuff
Speaker 2: and it was. Important it's, like you come with, me you,
Speaker 2: know CAN i be? HONEST i ACTUALLY I actually it
Speaker 2: was different than that because AS i started doing The
Speaker 2: channel nine, stuff the reason WHY i knew what questions
Speaker 2: asked is BECAUSE i already knew their content AND i
Speaker 2: knew how to make them talk about and make things.
Speaker 2: Sing but people just started saying stuff to me, Like,
Speaker 2: seth don't you ever get tired of all this technical?
Speaker 2: Stuff And i'm, like oh, man MAYBE i should move
Speaker 2: into something more. Technical maybe. Maybe so THEN i moved
Speaker 2: into DevRel FOR aiml and Then John, montgomery who was
Speaker 2: A cvp Of young he was, Like, seth come work
Speaker 2: for me in THE ai core.
Speaker 1: Group so that's what you obviously knew your, pedigree, right
Speaker 1: they knew they.
Speaker 2: Did nobody, knew LIKE i THINK i think people record
Speaker 2: like see me and like Because i'm pretty jovial and
Speaker 2: and BUT I i.
Speaker 1: Think you don't come across as AN ai.
Speaker 2: Scientist, NO i KNOW i do. Not and but if
Speaker 2: you looked at, me obviously we've said this many, Times
Speaker 2: i'm very beautiful in, appearance and people would not Like mark.
Speaker 2: Exactly people wouldn't, know BUT I i really have been
Speaker 2: doing machine learning. Forever and, again my primary goal is
Speaker 2: for people to understand at a profound level what these things.
Speaker 2: Are and to, me they are basically a new programming
Speaker 2: control structure which can Take english and point you to
Speaker 2: what to run. Next, yeah fairly still Constrained, english, yes, well,
Speaker 2: no even Regular english with AN, lm because if you
Speaker 2: can you can type into AN ll m like Bad.
Speaker 2: English and for, EXAMPLE i did a demo at my
Speaker 2: talk two days ago WHERE i showed him the supporter
Speaker 2: sales thing AND i, said my tent is, broken, yo
Speaker 2: AND i spelled it wrong and it said support AND
Speaker 2: i was, like what's the borst tent in the, world?
Speaker 2: Yo and it was like burst and not b e
Speaker 2: r S t BECAUSE i fat fingered it. Right and
Speaker 2: then it was, like, uh. Sales notice that because it's
Speaker 2: a large language, model it's able to reason over the
Speaker 2: text and then create the output that we. Want and
Speaker 2: if that output is a, function then it tells you
Speaker 2: what to run. Next so it.
Speaker 1: Didn't understand, bursts so it figured that you really meant worst.
Speaker 2: A best, Best, yeah, okay best b e r S.
Speaker 2: T and it was, like even if it was, worst
Speaker 2: it's still a sales and it still was able to
Speaker 2: figure it. Out and and that was with me, again
Speaker 2: and THEN i. CAN'T i don't know how to emphasize
Speaker 2: this is. Enough, basically you get a control structure that
Speaker 2: gives You english and tells you what things run. Next
Speaker 2: WHAT mcp does and whether it does it well or
Speaker 2: not is neither here nor. THERE i don't think it's
Speaker 2: Necessary what it does is it allows you to query
Speaker 2: what functions can you? Call and what it does is
Speaker 2: it maps those into the tool calls that are available
Speaker 2: to the L, m and then it can call. It
Speaker 2: but you can do that With swagger AND. Rest, sure,
Speaker 2: yeah but it's.
Speaker 1: Not it doesn't have the the almost it does.
Speaker 2: Reasoning it, does but it does because the reasoning happens
Speaker 2: in THE. Lm and what it outputs is it outputs
Speaker 2: what functions Are swagger AND rest don't have that they,
Speaker 2: don't but you don't need it BECAUSE, mcp if you
Speaker 2: look at, it is basically Like. Swagger it tells you
Speaker 2: what tools are, available and it's LIKE, rest and it
Speaker 2: tells you how to call. Them, Right and the thing
Speaker 2: THAT i like about like those kind of and OPEN,
Speaker 2: api which has been NOW i think the Next swagger is, that,
Speaker 2: RIGHT i don't. Know but with OPEN api and WITH
Speaker 2: rest you're able to do the same things but with
Speaker 2: the benefit of like twenty years of security, handling with,
Speaker 2: headers WITH, ooth with token, negotiation et. Cetera and so that's,
Speaker 2: why like IF mcp gets you, going like WHEN i
Speaker 2: USE vb, six, right it got me. GOING i loved
Speaker 2: it because it was able to. Get but then when
Speaker 2: you were, LIKE i need to be more memory, CONSCIOUS
Speaker 2: i needed to be more. Careful then you can skip
Speaker 2: down a layer if you if you want. To but,
Speaker 2: AGAIN i love THAT mcp has gotten people thinking about.
Speaker 2: It but the reasoning doesn't happen WITH. Mcp, no it's,
Speaker 2: yeah it just tells you what function are. Next but
Speaker 2: the magic that makes it look good is once it
Speaker 2: runs the, function there's a thread that has like what
Speaker 2: the QUESTION i? Asked it asks for a Function i'm
Speaker 2: like pointing like people can. See and then when it executes,
Speaker 2: out it puts that function return call back on the
Speaker 2: thread and THE lm is called again with that on its,
Speaker 2: thread and then it comes with the. Answer and so
Speaker 2: that actually going out from THE ai space is effectively
Speaker 2: bridging the gap FROM ai reasoning to perative or functional. Code.
Speaker 2: Right how we MAKE sky? IT i get? It, NO
Speaker 2: i mean it's NOT i don't even think IT'S.
Speaker 1: Sky and it may not be the best security wise right,
Speaker 1: now but it's clearly where everybody wants to.
Speaker 2: Go, yeah it's because it simplifies. It but my sense
Speaker 2: is that as you see, This i'm the prognostica, here,
Speaker 2: right BECAUSE i like doing. That as you see this thing,
Speaker 2: maturing it's going to be indistinguishable from rest right and discovery.
Speaker 2: Protocols that it's the way the embracement OF mcp says to,
Speaker 2: me everybody wants to interrupt with everybody. ELSE i fact
Speaker 2: that we could come up with a better way to do,
Speaker 2: it and in fact already. Have we could just be
Speaker 2: publishing APIs for crying. Out, Yeah And i'm telling, you
Speaker 2: like we we had a phase At microsoft and bless
Speaker 2: Our hearts where we went full. Wisdole, yes and.
Speaker 1: That's what The southern people say about the town drunk
Speaker 1: bless his.
Speaker 2: Heart, yeah bless our. Heart But wisdole was the same,
Speaker 2: thing the same kind of desire to to abstract over.
Speaker 2: Primitives and then we went back to primitive still being
Speaker 2: WSD l Or Web Service Discovery language. Wisdom does that sound? Familiar,
Speaker 2: friends done done? Dumb and so What i'm telling you
Speaker 2: is that and some of the things you saw with
Speaker 2: N a Web the reason why this is such a cool.
Speaker 1: Space if we haven't talked about AN a web on the,
Speaker 1: show oh, really yeah this WEEK i haven't.
Speaker 2: EITHER i started looking at. It but the intuition here
Speaker 2: is if you can language do stuff and then call
Speaker 2: stuff and then language do stuff on the, Out like
Speaker 2: what you if you hook this up to the in
Speaker 2: language tool, stuff, Right you're? Fired, yeah you're my problem
Speaker 2: WITH nl. WEBS i keep, thinking why are you talking About?
Speaker 2: Dutch people's confused The dutch. Web but hopefully that helps
Speaker 2: BECAUSE i really want to emphasize that once you know
Speaker 2: that that's what it's doing at a low level and
Speaker 2: you play with it that, way it changes the way
Speaker 2: you think about These. SURE i had a journalist talk
Speaker 2: to me and, say, Hey, SETH i get a lot
Speaker 2: of questions about SHOULD i trust these? Things and my
Speaker 2: response surprised. HIM i, SAID i don't trust these. Things,
Speaker 2: no don't be. Silly that's WHY i program them the
Speaker 2: WAY i. DO i restrict what they have to. DO
Speaker 2: i restrict the tool calls that they, have and then
Speaker 2: when the tool calls are, CALLED i write the code
Speaker 2: that the tool that what the tool responds. With because
Speaker 2: all these things are effectively becoming part of the prompt
Speaker 2: for the next tokens to. Generate and that is when
Speaker 2: you get that, Intuition you're, like, oh that MEANS i
Speaker 2: can do this ANYWAY i want to ye.
Speaker 1: And that's that's WHERE i think we've been talking about
Speaker 1: this this week, too that the attributes of the program
Speaker 1: or the future are going to be imaginations over technical
Speaker 1: probsolutely imagination mean and we may be held back a
Speaker 1: little bit by what we think we can do and
Speaker 1: what we can't. Do but somebody who doesn't have that
Speaker 1: kind of you, know, limitation self, limitation will be able
Speaker 1: to do amazing.
Speaker 2: Things. Yeah like if you watch the keynote on day,
Speaker 2: two you Saw Amanda foster And elijah on stage talking
Speaker 2: to this thing that it was executing a bunch of
Speaker 2: stuff on their. BEHALF i think it was. Software it
Speaker 2: was literally software BECAUSE i KNOW i, WROTE i wrote
Speaker 2: THE i wrote the website and the back. End amanda
Speaker 2: worked on the, agent And elijah worked on the on the.
Speaker 2: Evaluations but the thing about it is it was actually running.
Speaker 2: Live that thing was actually happening. Live and one of
Speaker 2: the things that we had is once you get into
Speaker 2: a single, agent it makes, sense but when you get
Speaker 2: into multi, agent you wonder, like, well what is the
Speaker 2: interface between one agent and? Another and the surprising thing
Speaker 2: is that the interface is actually language, weird which is, weird,
Speaker 2: Right so for, Example i'll give you, example the voice
Speaker 2: agent that we, made and if you watch the, keynote
Speaker 2: you'll see her talking to. It the voice agent is
Speaker 2: using THE gpt four oh Real TIME, api which has
Speaker 2: the also has the ability to issue function. Calls, Okay
Speaker 2: and so what we did is we collected the list
Speaker 2: of azure Ai foundry agents that we made Andree foundry
Speaker 2: AND i just loaded them up and then she picked
Speaker 2: the ones she wanted because she was working on. Them
Speaker 2: and what it did is it mapped those other agents
Speaker 2: as tool calls interesting to the voice. Agent but you're, like,
Speaker 2: well what did you pass to the? Agent, well the
Speaker 2: agents themselves when you run, them have this property called additional,
Speaker 2: instructions which means you can augment their system prompt and
Speaker 2: then that's. It that's all you can. Do so WHAT
Speaker 2: i did IS i mapped all of those agents as tool,
Speaker 2: calls gave it a function. Name you're back to sales
Speaker 2: and support, again that's all this, IS i. Know and
Speaker 2: THEN i, said you passed voice agent additional instructions and
Speaker 2: ask it for what you wanted to. Do so the
Speaker 2: voice agent then is taking the initial requests and assessing
Speaker 2: it to go which agent to go, to back to
Speaker 2: your sales and. Support that's. Right then taking the portion
Speaker 2: of the requests that makes sense for that, agent plus
Speaker 2: the additional, instruction let's send it on. Down but it's
Speaker 2: even more than. That it actually asks, it can you
Speaker 2: help me with? This and we were me And amanda
Speaker 2: because it's like in the two in the, morning we're
Speaker 2: looking at this, stuff like because we're on this stuff all.
Speaker 2: Night we looked at the law because we Ad jason
Speaker 2: printed out everything and we could, see, uh image agent system,
Speaker 2: instructions you are to generate a blah blah blah. Question
Speaker 2: can you generate an? Image it was Actual english and
Speaker 2: we were, like holy, cow this is this is amazing
Speaker 2: and the software being polite to. Software, yes it.
Speaker 1: Was really cook but but it's nothing. New if you
Speaker 1: look at the S MT smtp protocol when you connect
Speaker 1: When i'm When gmail connects to an S mvp, server it,
Speaker 1: says you, know, hello And I'm Carl, Franklin carl frank
Speaker 1: and THE smtp server actually writes, back, Hello Carl, franklin
Speaker 1: nice to meet.
Speaker 2: You, yes but we're not talking part of the. Protocol
Speaker 2: it's Not eliza in This, NO i, KNOW.
Speaker 1: I, know but but you know that those kind of
Speaker 1: friendly things have been.
Speaker 2: Protocols for a long. Time but the difference is that
Speaker 2: the only things are being generated by the through the
Speaker 2: agency of AN. LLM i get. It it's, big big.
Speaker 2: CHANGE i do question if if if the politeness affects
Speaker 2: the prompt. Results, yes it absolutely. Does And i'll tell.
Speaker 2: You i'll tell you what happens And i'll tell. You
Speaker 2: so there was a part of the demo Where amanda
Speaker 2: generates an image for a hackathon and they AND i
Speaker 2: don't think they understood because someone backstage is, like, hey
Speaker 2: can you change the image to something more? Happy and we're, like,
Speaker 2: no it generates it every. Time it's it's doing it
Speaker 2: on its. Own and a man is, like but we
Speaker 2: can change it by telling, it please make the image more,
Speaker 2: happy and like we made that change in The english
Speaker 2: and the and not in the voice controller because that's
Speaker 2: the thing we talked, to but in the literal little
Speaker 2: agent the, customer and it called, it called. It and
Speaker 2: then it was, like and now everyone's happy at the.
Speaker 2: Hackathon and and because everyone thought we were using some
Speaker 2: like pre baked image and, no it's not every, time
Speaker 2: and then we did, that and then it was really cool.
Speaker 1: All, RIGHT i think we got to take a, break
Speaker 1: so we will be right back after these very important
Speaker 1: messages don't go. Away did you know there's a dot
Speaker 1: net on aws. Community follow the social media, blogs YouTube
Speaker 1: influencers and open source projects and add your own. Voice
Speaker 1: get plugged into the dot net on aws community at
Speaker 1: aws Dot amazon dot, com slash dot, net.
Speaker 2: And we're.
Speaker 1: Back it's dot net. Rocks I'm Carl, Franklin I'm Richard,
Speaker 1: campbell And Seth warez is. Here we are JUST i
Speaker 1: feel like we could be talking for, hours hours an,
Speaker 1: hour so much to talk, about but we're talking about
Speaker 1: agentic coding and. PROGRAMMING i Took microsoft Agent mode In
Speaker 1: Visual studio for a spin yesterday to go.
Speaker 2: Good, okay it went really.
Speaker 1: WELL i gave it some tasked to create some stuff
Speaker 1: THAT i knew how to do BECAUSE i wanted to.
Speaker 1: See but there was one very tricky thing that it
Speaker 1: was intuitive for it to, do but it didn't. Work
Speaker 1: so it's not like agent mode is going to compile
Speaker 1: your code.
Speaker 2: And see if it.
Speaker 1: Works it's just going to do what it thinks is. Right, yes,
Speaker 1: SORRY i didn't meet us the ord. Thing it's going
Speaker 1: to use what it assesses to be the correct. Answer
Speaker 1: so but WHEN i went and told, it, no that's.
Speaker 1: Wrong this doesn't work on this particular. Object you're going
Speaker 1: to have to subclass, this it, said oh. Okay AND
Speaker 1: i didn't tell it what to. Do ALL i had
Speaker 1: to say was you're going to have to subclass this,
Speaker 1: subject and it figured it.
Speaker 2: Out it's pretty cool. Stuff one of the things that
Speaker 2: you're going to find is that if you ever have
Speaker 2: an agentic thing that is not quite doing what you
Speaker 2: want in, general from an engineering, perceptive for, me it
Speaker 2: means that they've overstuffed what they want a single agent to.
Speaker 2: Do so the actual engineering problem becomes agent specificity versus agent.
Speaker 2: Count and that's an engineering, issue, right because if you
Speaker 2: have too many, agents it might cost, more or it
Speaker 2: might it might work. Slower if you have too few,
Speaker 2: agents the recall isn't going to be as, good, Right
Speaker 2: and so the actual engineering problem is that it turns
Speaker 2: out the agent you use was. Able once you specify
Speaker 2: down a little, bit it made things. Better but if
Speaker 2: you think you're going to make a single agent with
Speaker 2: like a two page prompt with fifteen, tools it's not
Speaker 2: going to do the right. Thing and that's the trade
Speaker 2: off from an engineering pler because now that you know
Speaker 2: you have to make it, specific you have to limit
Speaker 2: its tool because it's an interesting switch from what build
Speaker 2: up lms the first, place which is making them bigger and.
Speaker 2: Bigger now we're, going, hey if you wanted me to respond.
Speaker 2: Better but see that's the thing, small that's a. Thing
Speaker 2: it's not THE lms that are getting, right it's the
Speaker 2: tasks that you're assigning THE. Lm so it's THE. Lm
Speaker 2: let's just fix THE lm for. One let's just SAY gbd. Four,
Speaker 2: oh like that models, Huge but like you are gonna
Speaker 2: narrow the agent's task to your job is to send
Speaker 2: an email to the right. Place and then here are
Speaker 2: the tools send email or delete email or. Whatever those
Speaker 2: are the only. Things that thing is gonna be very
Speaker 2: good at sending, emails and it's gonna be very good
Speaker 2: to be, like, HEY i need to send an emails
Speaker 2: to whom please type or tell me the. Email and
Speaker 2: then it's gonna be, like please validate the. Email what
Speaker 2: content do you? Want because it's trying to fill out
Speaker 2: the parametery of the function call because that's what the
Speaker 2: prompt wants it to. Do and that's what's. Happening so
Speaker 2: for your, case LIKE i worry like even in some
Speaker 2: of the tools that we, make maybe At, microsoft maybe
Speaker 2: they're too broad and that's where you find this notion of.
Speaker 2: Hallucination now people aren't gonna like, this but the elem's
Speaker 2: job is to actually make up. Tokens if you give
Speaker 2: it too broad a, thing it's gonna make stuff. Up
Speaker 2: if you get it to if you give it a narrow,
Speaker 2: thing it's gonna make the right stuff. Up BUT i
Speaker 2: thought that's.
Speaker 1: What the point of the agent THAT i access in
Speaker 1: visual studio, is and it's NOT gpd four oh with
Speaker 1: this broad uh set of knowledge or. Whatever i'm, Sorry,
Speaker 1: RICHARD i didn't mean to use the. KNOWLEDGE i have
Speaker 1: to apologize every TIME i answerpomorphize than LL.
Speaker 2: M but he is. Right thank you keep him on. Track, yeah,
Speaker 2: No i'm. SORRY i apologize now ALREADY i. Apologize please
Speaker 2: be with the pigmies In New. Guinea. Yeah.
Speaker 1: Amen but it narrows the focus to truss the things
Speaker 1: that are in. Code so theoretically it should know everything
Speaker 1: about the Language i'm using the, markup you, know If
Speaker 1: i'm Using blazer or. Whatever so it's got to UNDERSTAND
Speaker 1: html and JavaScript and all Those you're.
Speaker 2: Gonna love this. Answer it knows. Nothing it knows only
Speaker 2: what You i'm trained on. It, well, yeah so there
Speaker 2: there is a, yeah so that's WHAT i. Mean, Yeah
Speaker 2: so there's certain models that are actually Like, codex for,
Speaker 2: example is perfectly trained on, code but it doesn't know
Speaker 2: like in my, head it doesn't know the. Languages it
Speaker 2: knows that if you give it this sequence of programming or,
Speaker 2: questions it's going to return this. Sequence let me just
Speaker 2: add this to the.
Speaker 1: EQUATION i did the same problem with chatchipt AND i
Speaker 1: did get the right. Answer, yes, so but the thing
Speaker 1: that was The Microsoft agent mode In Visual studio did
Speaker 1: not give me the right answer.
Speaker 2: Because it's a different.
Speaker 1: Model, Right but wouldn't it be narrowed down to just
Speaker 1: the programming stuff like why did CHAT gpt with vast
Speaker 1: programming and learning that it did on all this data
Speaker 1: provide a better answer.
Speaker 2: Because models are stochastic, processes and the thing about one
Speaker 2: model versus another is one will do better with the
Speaker 2: same prompt or. Not and so that's when he gets
Speaker 2: the college. Words that's five dollars five dollar. Word it's
Speaker 2: a stochastic process because the output vector gives you the
Speaker 2: probability distribution and samples according to that probably. Distribution so some,
Speaker 2: models depending on let's just say that use clawed in that,
Speaker 2: one or they are going to do different things and
Speaker 2: it all so the promptly makes a, difference and the
Speaker 2: tools that it has really makes a. Difference and that's
Speaker 2: where it sounds like an excuse to. Me it does
Speaker 2: and here's what they should have. Done just like we
Speaker 2: have unit tests for, code we should have quote unit
Speaker 2: tests for. Agents and those things are called. Evaluations and
Speaker 2: so An azree foundery we have a bunch of evaluations for,
Speaker 2: example is it follow the right? Task is it? Coherent
Speaker 2: is it? Relevant is it? Grounded we also have these
Speaker 2: other agents whose job is to attack our. Agents they're
Speaker 2: called the red teaming, agent and its job is to
Speaker 2: literally send garbage at it to see how it. Does
Speaker 2: and so that is the unit. Test and the difference
Speaker 2: between a unit tests and an evaluation is that an
Speaker 2: evaluation you need to give it enough examples to be
Speaker 2: satisfied that you're going to be. Okay it needs to
Speaker 2: be probabilistically, relevant, Yes and then once you're okay with,
Speaker 2: it when you put it in, production you monitor it
Speaker 2: with the same evails and send alerts when it goes.
Speaker 2: Off the rails diverts because when you're looking, at for,
Speaker 2: example regulated, industries regular, industries like if you make a,
Speaker 2: mistake you got to report, It but they also want
Speaker 2: to know how do you fix these? Things and that's
Speaker 2: probably more, important and that's the part of regulated. Industries
Speaker 2: when you use this s uff you can see where
Speaker 2: it went wrong and you fix it and then you go.
Speaker 1: Back SO i think the real answer, is, hey we
Speaker 1: just launched this, shit give us some time.
Speaker 2: Time i'm not part of that. Team they mean they
Speaker 2: could have made it, better.
Speaker 1: Maybe BUT i mean it literally just came, out like you,
Speaker 1: know so and.
Speaker 2: They'll learn a lot, because, like for, example the question
Speaker 2: you asked it maybe something they never. Anticipated that's, Right,
Speaker 2: yes so there's Learn so there's some alert that probably
Speaker 2: fired during THE evls that's, saying, Oh carl did not
Speaker 2: get this thing that he, wanted and so they update
Speaker 2: the prompt or a new tools AND i expect.
Speaker 1: That. Yeah, so BUT i love the, idea and you,
Speaker 1: know talking To Scott hunter and some other, people the
Speaker 1: possibilities of things That i'm to do with it in
Speaker 1: the real world from my customers is going to blow their.
Speaker 2: Minds, Yes and in terms of like the stuff you can,
Speaker 2: do it's the ceiling is very high because the biggest
Speaker 2: barrier to programming isn't like the new algorithms or the
Speaker 2: new it's like literally people using this stuff and having
Speaker 2: the like like someone Putting bob in the age. Field you,
Speaker 2: know that's the kind of dumb stuff that we write
Speaker 2: most of our code. For right ll ms together in
Speaker 2: an agentic, mode are going to soften that so that
Speaker 2: we don't have like why do we need a web form?
Speaker 2: ANYMORE i can do like my name Is seth and
Speaker 2: you can use my current location to find the.
Speaker 1: Address, now you're absolutely, right and YOU i is going to,
Speaker 1: change yeah from you. Know but but users are trained on,
Speaker 1: forms aren't. They, yes you're going to have to train
Speaker 1: a user to all. Right so here's here's a. Nightmarebox nightmare.
Speaker 1: Scenario a textbox to tell the application what you, want
Speaker 1: and somebody comes in and, says, hey you want A turkey.
Speaker 1: Club i'm going to The. Delhi, Yeah i'll have A turkey.
Speaker 1: Club and that's Writing turkey, club you know in the
Speaker 1: in the. Thing you know WHAT i mean, like there's there's.
Speaker 1: You now you have to train the users as to
Speaker 1: what to say or what to, type and that's the.
Speaker 2: Beauty. THOUGH i think given a properly built, agent that
Speaker 2: turkey sandwich is just gonna be noise to. It like
Speaker 2: it's gonna look for the things that will force the
Speaker 2: linear algebra and the calculus to produce the right next.
Speaker 1: Time, yeah and then there's gotta be validation and. Verification
Speaker 1: did you really mean to say? This is this what
Speaker 1: you really?
Speaker 2: Want you? Know and ALSO i think the software is
Speaker 2: fairly good at recognizing a contact shift and SAYING i
Speaker 2: don't THINK i was supposed to listen to, that, yeah.
Speaker 1: Right and Also i'm assuming that you're speaking to it
Speaker 1: right when you really need to be, Typing like you
Speaker 1: don't Want bob From engineering to pop his head in and,
Speaker 1: say delete all records from, customers you, know.
Speaker 2: DUDE i saw AN mcp server that was attached to
Speaker 2: a file, system AND i was, like maybe let's not do. That,
Speaker 2: yeah it seems like about on mind's. IDEA i remember
Speaker 2: when the you, know the First amazon devices showed, up
Speaker 2: and right away it was, like send my porn collection
Speaker 2: to my mother.
Speaker 1: Joke oh my, gosh send one hundred pounds of cement
Speaker 1: To Richard.
Speaker 2: Campbell oh my, gosh so. Dumb but, yes BUT i
Speaker 2: think LIKE i, Think LIKE i said. It it was
Speaker 2: surprising to me how the the controller agent in this
Speaker 2: case was communicating with other. Agents, yes that. Was and
Speaker 2: then you, Know i'm not surprised by this stuff BECAUSE
Speaker 2: i know how it. Works, Right but that was the
Speaker 2: part that was, Like, okay maybe there's something. Here, well
Speaker 2: and we get back to this whole like The battle
Speaker 2: Of VISIBLE. Ai, yeah, right and we still haven't really
Speaker 2: fixed in there all that. Problem, no there's no way
Speaker 2: we can fix. That, well and that the Whole i've
Speaker 2: read the anthropic paper on the reasoning models and what's
Speaker 2: actually going on. There it's calling itself right couple. Times,
Speaker 2: well first it gets the, results and then it peaks
Speaker 2: pieces of the results and feeds it back into it
Speaker 2: to say why WOULD i have said? This so it
Speaker 2: literally is in reverse. Reasoning i've already decided what the answer,
Speaker 2: is but let me break it down into pieces so
Speaker 2: it looks Like i'm thinking about. It, yeah that doesn't
Speaker 2: seem to be a good way to do, it but
Speaker 2: it makes people, happy, Right, well it doesn't make me,
Speaker 2: happy well until you know what's going. On and it's
Speaker 2: LIKE i just.
Speaker 1: Read something in the news today that said THE ceo
Speaker 1: Of anthropic admitted that he has no idea how this
Speaker 1: stuff for but.
Speaker 2: You, know maybe like in his, defense like there's certain
Speaker 2: things that CEOs do THAT i just don't want to
Speaker 2: know how to. Do, yeah you know What i'm, Saying
Speaker 2: like there's only there's only so MANY i don't know
Speaker 2: if this is, true but there's only so much space
Speaker 2: in my brain for things, nice, Right and it's LIKE
Speaker 2: i have, seen like BECAUSE i work with a lot
Speaker 2: of our EVPs and CEOs and and all that, stuff
Speaker 2: and like And i've worked And i've talked to CEOs
Speaker 2: and cvps and it's just like MAYBE i don't want
Speaker 2: to do. That, Yeah and and the fact that he
Speaker 2: doesn't know exactly how it works is, okay focus on
Speaker 2: the right. Things. Yeah and the other thing that for,
Speaker 2: me LIKE i mathematically know how these things, work BUT
Speaker 2: i don't expect other people to just to. Me it's
Speaker 2: just LIKE i love looking at like the beauty of
Speaker 2: the mathematical structure of these things and trying to think
Speaker 2: about why does this actually?
Speaker 1: Work but you know, WHAT i feel better knowing that
Speaker 1: as long AS sef.
Speaker 2: Knows BUT i think this idea that that neural net
Speaker 2: is is essentially creating a jpeg of, language lossy compression
Speaker 2: of language one hundred. Percent that is exactly. Right but
Speaker 2: when we, say and the whole thing, is don't put
Speaker 2: any too many pictures in, It, yeah you're just gonna
Speaker 2: get a GREAT FUZD i. Know and then but but,
Speaker 2: then but, then because these things are really good at,
Speaker 2: sequences think of it as any. Sequence so we have
Speaker 2: models And andrea found with that do protein. Sequencing, yeah,
Speaker 2: well deep. Fault and if you think about, it you're, like,
Speaker 2: well how could that? Work, well because again you're giving
Speaker 2: it a sequence of, things and it's telling you the
Speaker 2: next likely. Thing and then it's doing it in a
Speaker 2: loop right while not end of line token or end
Speaker 2: of thing. Token, right that's what it's doing and get
Speaker 2: next probable. Words. Yeah and the thing about it is
Speaker 2: people will talk about the, temperature whether it makes it
Speaker 2: more creative or. Whatever it's not making it more. Creative
Speaker 2: it's basically squashing the output distribution so other things are more.
Speaker 2: Likely and that's all it's. Doing and SO i hear
Speaker 2: it's making it more. Creative, no it's. Not it's changing
Speaker 2: the hand of the output. Distribution it'll make it, faster
Speaker 2: but you'll get. Crap it's not even. Faster it's exactly
Speaker 2: the same. Speed it's just the probability distribution is warped
Speaker 2: so that if, like for, sure it's this, one if
Speaker 2: temper is, one it's like the probably is what it
Speaker 2: it'll pick the, right it'll. Almost but if it's, different
Speaker 2: there's other things that become more equally. Likely and so
Speaker 2: it's chain of like this, token now this token it
Speaker 2: Becomes they say it's more, creative AND i hate. That
Speaker 2: why DO i hate? That? Richard it's more, exactly it's
Speaker 2: literally just changing the output.
Speaker 1: Distigue but, okay So joe, programmer, though do you think
Speaker 1: they need to answer pomorphize this stuff in order to
Speaker 1: talk about it or think about it in a in
Speaker 1: a way that they can use it correctly because everybody
Speaker 1: falls into this. TRAP i mean we're, Humans.
Speaker 2: Yeah like humans are funny. Creatures we will we will
Speaker 2: pick up a rock and draw a smiley face on,
Speaker 2: it and so. Happy it's such a happy. Rock it
Speaker 2: must have had a really good Dayne and now. Imagine
Speaker 2: but isn't THIS i WANT i have to think this
Speaker 2: is what makes humans? Humans first, off paradolia is a survival.
Speaker 2: Strategy the tendency to see, faces whether they're there or,
Speaker 2: not means you were the first one to, run and
Speaker 2: so the tiger probably didn't get. You so that's an evolved.
Speaker 2: Trait absolutely, right it's. Paradolia but what if this whole
Speaker 2: practice event memorphizing is the mechanism that led to us
Speaker 2: being able to, strategize like how you have to think
Speaker 2: like the animal to hunt the animal successfully with your limited.
Speaker 2: RESOURCE i, See and so that tendency again becomes a competitive.
Speaker 2: Strategy you're more likely to survive because your hunting is.
Speaker 1: Morphout not to mention, mythology, right, well in, mythos in
Speaker 1: religion and all of these, things it's all about. Anthropomorphizing,
Speaker 1: sure and not just.
Speaker 2: Why is the sky making loud noises?
Speaker 1: Shaming right As thor is sending lightning, thing that's because
Speaker 1: he's angry with.
Speaker 2: US i feel like we got to go the final
Speaker 2: mile and somehow get into The roman spears because we
Speaker 2: all think About roman, period, RIGHT i don't, Know but,
Speaker 2: well actually that project the name for the project that
Speaker 2: you Saw amanda And elijah Do we called it, sustinnao
Speaker 2: which Is latin for, SUPPORT i support, nice, Okay and
Speaker 2: SO i told him that and let's lie it for.
Speaker 2: Sales way to close the loop that was that was.
Speaker 2: Amazing this is a quality. Show my job as co
Speaker 2: host Of. Professionals the joke that's but, Yeah so again the,
Speaker 2: key the key insight is think of these things as
Speaker 2: things that take. Language and if you have a prompt
Speaker 2: that's narrow enough and a collection of tools that support the,
Speaker 2: task it will tell you what consistent. Results, yeah, yeah
Speaker 2: That's these are the dimensions of a tool set for
Speaker 2: a group of programmers to be successful with correct they
Speaker 2: just had to learn the dimensions of. It and the
Speaker 2: joke THAT i, said is it like a switch statement
Speaker 2: and if statement but. Huge So i'm going to try
Speaker 2: to make it a thing and call it the giant swift.
Speaker 2: Statement it's the swift for those that are. Swifties swift all.
Speaker 1: RIGHT i figured out The latin for sales is.
Speaker 2: Isn't That italian full Of? Chicago oh my, gosh that's.
Speaker 2: Hilarious but, yeah and so that that inside couple with
Speaker 2: the ability to evaluate them as unit with the unit
Speaker 2: with the agentic. Unit tests should bring this all back
Speaker 2: to quality instead of the schlop that people put. Out
Speaker 2: sure they, are and THAT'S i mean the battle here
Speaker 2: is how do you measure? Quality, oh there's a way
Speaker 2: to measure? It, yeah BECAUSE i keep being told this
Speaker 2: THIS lm is better than THAT llm is, like how
Speaker 2: how would you measure? That what makes that? True so
Speaker 2: what they do is they give it a bunch of,
Speaker 2: stuff like, like they feed it stuff and then they.
Speaker 2: Measure i'll give you there's a couple of. Emails so
Speaker 2: there's something Like blue And, rouge which is like how
Speaker 2: close is it to the right? Answer? Right those are
Speaker 2: supervised tests where you're, like you give it a thing
Speaker 2: and you're, like here's the OUTPUT i wanted to. Have
Speaker 2: we've talked about this.
Speaker 1: Before, Yeah blue And rouges is like those like supervised
Speaker 1: and supervised.
Speaker 2: Tests. Right but then there's one supervised tests and you're, like,
Speaker 2: well how do those? Work, Well i'll give you an.
Speaker 2: Example there's a there's a judge BASED evl called, groundedness
Speaker 2: and what groundedness. Measures it measures how rounded a response
Speaker 2: from AN lm is in the facts and context that
Speaker 2: you gave. It, Yeah and so for, example the three
Speaker 2: pairing that you give it for the unit test is sas,
Speaker 2: asked how much does your you, know outdoor tent? Cost
Speaker 2: the context is we give it all of like the tent,
Speaker 2: information and the cost is in, there, right it's just
Speaker 2: like the slop of it and the outputs like the
Speaker 2: tent is ten. Dollars. Right so given given that a judge.
Speaker 1: Base before the, tariffs now there are a lot, more.
Speaker 2: That's, Right and now they're gonna be a lot. More
Speaker 2: but that particular three two, pole it's not, twopole but
Speaker 2: the three pole three. Yeah of the, question the context
Speaker 2: and the. Answer, yeah it's given to a judge to
Speaker 2: judge on a scale of one to. Five how grounded is?
Speaker 2: It and that judge is also AN Lllm, yeah okay
Speaker 2: another one, yeah and it. Works and that's why that's
Speaker 2: the whole idea of, Agency, yeah is that you have
Speaker 2: agents that are specialized in their different, things just like
Speaker 2: you have you on your. Staff there special that's. Right
Speaker 2: the people of my staff Question, mark it's just, me all, Right.
Speaker 1: But Joe developer who's listening is, thinking, oh this is.
Speaker 1: Great you, know after listening to these shows for the,
Speaker 1: last you, know the week that we've been doing, These
Speaker 1: i'm thinking now that you, Know i'm going to get
Speaker 1: rid Of bob and engineering And i'm going to replace no,
Speaker 1: agent And i'm going to set up this little staff
Speaker 1: of programmers under. Me agent programmers are going to help
Speaker 1: me do my.
Speaker 2: JOB i don't think that's ever going to. Happen WHAT
Speaker 2: i think is going to happen AS i already. HAPPENED
Speaker 2: i think we're going to solve. WELL i mean those
Speaker 2: that think are they're going to solve it that way
Speaker 2: are going to come in Like i've seen vibe coders
Speaker 2: like put out stuff and be, like oh why is
Speaker 2: MY aws bill so high.
Speaker 1: About vibe Coders and There's we've been arguing about whether
Speaker 1: that's pejorative or, not because the ryan it's.
Speaker 2: Not, okay is it a pejorative?
Speaker 1: Thing some people see, it some people see it, Goodness,
Speaker 1: Yeah But i'm talking about a professional co that maybe
Speaker 1: works by themselves and hires out other specialists to do
Speaker 1: that kind of. Stuff, interesting and NOW i can maybe
Speaker 1: spin off some agents to help me with that kind
Speaker 1: of stuff AND i not depend on them as.
Speaker 2: MUCH i think that the agents are going to be
Speaker 2: able to do with a low powered brain work that
Speaker 2: we as humans do and and but the actual creative
Speaker 2: move forward and always human right and not and not
Speaker 2: be stuck in the drudgery of. That so you actually
Speaker 2: have more creative energy to do more experimentation. Exactly because
Speaker 2: then that's.
Speaker 1: Why imagination is going to be the number one uh
Speaker 1: feature of the Rock star. Developers, oh there you, go
Speaker 1: there you. Go that's WHY i Think i've never been
Speaker 1: a rock star. Though, dude you are the more like
Speaker 1: a flute, player saying like You're Eddie eddie Van halen
Speaker 1: jazz PLAYER ai At Microsoft Cool.
Speaker 2: Beings well look, again AND i just want to emphasize
Speaker 2: this is all accessible to. Everybody you can start playing
Speaker 2: with it. Today make it do a raw function call
Speaker 2: and see the output so you can see that What
Speaker 2: i'm saying is. True don't have it do the function,
Speaker 2: call just call it and tell it to do. Return
Speaker 2: do a tool call and you'll see it literally Returns.
Speaker 2: Jason that, says run this function with these, parameters.
Speaker 1: And be as careful with your prompts with your agents
Speaker 1: as you are with your ten year old when they
Speaker 1: tell them to go to the store and get used xy.
Speaker 2: Eggs or your programmer.
Speaker 1: Husband, yes that's, right that's you, know the one about the.
Speaker 1: Programmer your husband and the wife, said go to the
Speaker 1: store and get a gallon of. Milk if they have,
Speaker 1: eggs get a. Dozen he came back with a dozen.
Speaker 1: Gas they had.
Speaker 2: Eggs they had. Eggs but that's just how we have
Speaker 2: the whole world turned into paper. Clips but if you
Speaker 2: need to be, precise but if you think about, it
Speaker 2: that's exactly how you should think about these. Things that's.
Speaker 2: Right you have to be very because you want to.
Speaker 2: Listen you want to maximize the probability of it returning
Speaker 2: the right tokens based upon the ones you gave.
Speaker 1: It so we're all in for a new world of
Speaker 1: discovery and refinement of search and refinement of. Prompts, yeah
Speaker 1: and the whole new.
Speaker 2: World and if you go about retrieval augmented, generation it's
Speaker 2: literally the process of you get a, question you programmatically
Speaker 2: search for an, answer you put it into the, prompt
Speaker 2: and then you get the. Answer that's. It, yeah that's
Speaker 2: all it. Is that's How, yeah we just gave it
Speaker 2: a fancy. Name, Yeah WELL i don't like the TERM.
Speaker 2: Rag it sounds it sounds kind Of but, YEAH i
Speaker 2: do a demo WHERE i do augmented, generation WHERE i
Speaker 2: literally go and copy From, wikipedia BUT i think the
Speaker 2: right answer is put it in AND i show it
Speaker 2: AND i was, like how does it know the right? Answer
Speaker 2: and it was, like, well because you put it. In,
Speaker 2: okay now you understand what augmented generation. Is the only
Speaker 2: process of retrieval was artisanal in my case BECAUSE i
Speaker 2: went and did. It it was. Artisanal, yeah it's like
Speaker 2: a fine, cheese, Right but we's That and then if
Speaker 2: you think about we're too. Generation by putting the right
Speaker 2: context in and then coupled with tool, calling now you
Speaker 2: have a powerful. Combo and that's WHERE i, think if
Speaker 2: you think about it like, this you're really going to
Speaker 2: change the way things. Happen sure, WELL i also love
Speaker 2: how much you dilute the pretense of all this. Technology,
Speaker 2: OH i know it's the. PRETENSE i said that in
Speaker 2: a very pretentious. WAY i. Know it's kind of.
Speaker 1: Amazing, actually, yeah.
Speaker 2: It was like a two fur three For, yeah there you.
Speaker 2: Go but like That, latin but it's a plural of. Slezolo.
Speaker 2: Right but if you think about, it this has been
Speaker 2: What i've been doing for the last twenty. Years, Sure,
Speaker 2: yes that's. True i've been trying to make this stuff
Speaker 2: lose all of its pretentiousness and when you, do it
Speaker 2: empowers you to do really. Things absolutely. Sure does the
Speaker 2: tools only get? Better that's, Right Brave New.
Speaker 1: World and that's the last word from. BUILD i, Suppose
Speaker 1: WE'RE i, Love we're. Done it's TIME uh to hit the.
Speaker 1: Road thank you for. Listening thank, You.
Speaker 2: Seth it's always a, pleasure always a.
Speaker 1: Pleasure and we'll talk to you next time on dot net.
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