Speaker 1: How'd you like to listen to dot NetRocks with no ads? Easy?
Speaker 1: Become a patron for just five dollars a month. You
Speaker 1: get access to a private RSS feed where all the
Speaker 1: shows have no ads. Twenty dollars a month will get
Speaker 1: you that and a special dot NetRocks patron mug. Sign
Speaker 1: up now at Patreon dot dot NetRocks dot com.
Speaker 2: Hey Philly, it's dot net rock.
Speaker 3: Wow.
Speaker 1: Yeah, so long since we've been in front of a
Speaker 1: real crowd.
Speaker 3: That a live show man.
Speaker 1: While I drove down here from New London, it's about
Speaker 1: four and a half hours and I had road trip flashbacks.
Speaker 3: Yeah, yeah, no kidding. It was one of the I
Speaker 3: think we stopped here on every tour, Yes, every single one,
Speaker 3: from the very first one two thousand and five, which
Speaker 3: is entirely your idea because I was brand new, twenty eight,
Speaker 3: twenty ten, twenty twelve, twenty thirteen. Yeah, yeah, everyone. We
Speaker 3: always came to Philly. Sometimes we got to do a
Speaker 3: whole day, like we do a day of dot net thing.
Speaker 3: Sometimes it would just be the evening show. But you know,
Speaker 3: you could always count on Philly folks to come out
Speaker 3: and have a great time. So this is a special one.
Speaker 3: It's a one off, it's one of a kind, and
Speaker 3: we're gonna have some fun.
Speaker 1: We are going to have some fun. Before I introduce
Speaker 1: the other two people who are here with us, let's
Speaker 1: talk about.
Speaker 3: Nineteen eighty one.
Speaker 1: Nineteen eighty one. All right, so do you know what
Speaker 1: happened in nineteen eighty one?
Speaker 3: A few things, tell us, but I focused purely on
Speaker 3: space and technology topics. Well, go ahead, Well it's nineteen
Speaker 3: eighty one, so it is the beginning of the Space
Speaker 3: Shuttle program. Well, the successful launch after years of delay. Remember,
Speaker 3: Shuttle was supposed to rescue sky Lab and they lost
Speaker 3: that in seventy nine. This is nineteen eighty one. They
Speaker 3: missed it by a few years. So STS one flies
Speaker 3: in April with Young Crippon on board. They for fifty
Speaker 3: four hours, thirty seven orbits and a totally successful flight, really,
Speaker 3: no no drama, great test flight. Famously, it was Crippen.
Speaker 3: Young was the guy who flew everything. He was in Gemini,
Speaker 3: he was in Apollo, but Crippen was the new guy.
Speaker 3: And as they were bringing it in for a landing,
Speaker 3: says how it FLI And he said flies like a
Speaker 3: brick shit house because it did right, This a one
Speaker 3: hundred ton vehicle with stubby little wingstune two hundred miles
Speaker 3: nuts at hour. You know, it's stuffed a landa thing. Anyway,
Speaker 3: they had to edit that so if you ever see
Speaker 3: the quote from Robert Crippen it's flies like a brick,
Speaker 3: that is not not what he said Later that year
Speaker 3: sts two. So they turned Columbia around in about six
Speaker 3: months and flew it again in November. This time it
Speaker 3: was angling truly. Engel was supposed to be on Apollo seventeen.
Speaker 3: He got bumped by Harrish and Schmidt, the only scientists
Speaker 3: to ever walk on the Moon. And so they flew
Speaker 3: Columbia again, and the main job of the second flight
Speaker 3: was to test out the Canada Arm because their first
Speaker 3: this was back when they thought they were going to
Speaker 3: fly these things every two weeks and they were going
Speaker 3: to deploy satellites from them, repair satellites from do all
Speaker 3: this cool stuff right. It was a space transport system
Speaker 3: except for all that part where did that didn't work?
Speaker 3: And so they this was the trials for the It
Speaker 3: was going to be five days up there working on
Speaker 3: the Canada ARM And on the first day they had
Speaker 3: a field cell failure and so they were low on power.
Speaker 3: They couldn't operate the arm, so they cut the mission
Speaker 3: down to two days and turn around came back.
Speaker 4: Was that the flights with the white painted external tank.
Speaker 1: This is when the external light was still Another voice,
Speaker 1: what's going on here? Oh, that's Jeff Fritz.
Speaker 3: That's Jeff Frid's okay, Yeah, So that that's back when
Speaker 3: the tanks were painted white, and that paint weighed something
Speaker 3: like fifteen hundred pounds. So that's why they lost it eventually.
Speaker 4: It was just weighs that much.
Speaker 3: When you get that much of it on there.
Speaker 1: Well, you can dump the cans overboard once you get space.
Speaker 1: So and Columbia was always overweight, right, she was. She
Speaker 1: was just the first flying vehicle. And that's why she
Speaker 1: never went to the space station. She couldn't get to it.
Speaker 1: That didn't have any that mass, didn't have enough energy
Speaker 1: get there.
Speaker 5: That was the space shuttles that had.
Speaker 3: Space shttle Columbia.
Speaker 5: Yeah, and that's the one with the bricks underneath it.
Speaker 3: With the well, and that would crumble in your hands
Speaker 3: like they would crush. Right, they're very delicate. It was
Speaker 3: ridiculous to delicate.
Speaker 1: And another voice you heard was Bill Wolf.
Speaker 3: And uh, the geek out start because of me ranting
Speaker 3: at Carl in twenty eleven when Atlantis landed for the
Speaker 3: last time. Well he was in a bar with some
Speaker 3: of this, yes, and uh and just how screwed up?
Speaker 3: You know, space Shuttle never really did what was supposed
Speaker 3: to do, all the missions that it could never actually do.
Speaker 1: And and Richard will never tell you this, but he
Speaker 1: had the Space Shuttle manual and read it cover to
Speaker 1: cover so he could tell you everything about the Space Shuttle.
Speaker 3: Uh, not entirely true, but okay.
Speaker 1: Uh.
Speaker 3: Anyway, he talked me into let's do that as a show.
Speaker 3: And I thought you guys would crucify us for that
Speaker 3: because it was not exactly software. And I was wrong.
Speaker 3: The geekout and I kept doing them. And this is
Speaker 3: eighty this is eighty one. This is the end of November,
Speaker 3: end of December show. The next two shows will be
Speaker 3: the annual Geekouts. So I've been working all my scripts
Speaker 3: for an update on space and an update on energy generation.
Speaker 3: So that's coming last space topic and then we'll talk computers.
Speaker 3: Voyage two does its flyby of Saturn in August, so
Speaker 3: one of the pioneers already coming gone. But this is
Speaker 3: the second time we got to look at Saturn Voyager
Speaker 3: when I gotten there the couple years before, and we
Speaker 3: got some better images, and that sets us up for
Speaker 3: eventually Cassini. And this is when they figure out that
Speaker 3: that titan very likely has a prototypical atmosphere and increases
Speaker 3: the excitement for building bigger missions around all right. On
Speaker 3: the computer side, nineteen eighty one is the year that
Speaker 3: Sony introduces the three and a half inch floppy disco.
Speaker 3: Beginning of all of that, there was competing sizes. There
Speaker 3: were three inch versions and three and a quarters as
Speaker 3: well as the three and a half and a three
Speaker 3: and a half ultimately be the one that was adopted everywhere.
Speaker 3: Not so floppy, though not a flop at all, quite
Speaker 3: rigid actually, which part of the reason it worked so well.
Speaker 3: They used the Bernoulli effect inside that rigid housing so
Speaker 3: they could increase I density. The original discs were only
Speaker 3: three hundred and sixty K, then they went to seven
Speaker 3: twenty and then one point four megabytes, so much storage,
Speaker 3: but by far easily most important story nineteen eighty one
Speaker 3: the release of the IBM fifty one fifty which you
Speaker 3: know is the IBM PC four point seven to seven
Speaker 3: MEGAHURTZ processor. The eighty eighty eight processor by default sixteen
Speaker 3: k ARAM, but you could expand it to two hundred
Speaker 3: fifty six. Later we would come up with an expansion
Speaker 3: burd to take to six.
Speaker 4: Forty, but need more than two hundred and fifty six.
Speaker 3: Well really sixteen k right, basic configuration, but you could
Speaker 3: run twos that's all there was, of course, shipped with
Speaker 3: CPM eighty six, or you could run this brand new
Speaker 3: shiny ms DOUS one point zero. Why did they do
Speaker 3: the eighty eighty eight instead of the eighty eighty six
Speaker 3: because it was cheaper and more readily available. The eighty
Speaker 3: six ers were in high demand and they wanted to
Speaker 3: build a lot of these machines. They were trying to
Speaker 3: get faster to the market, and so the eighty eighty
Speaker 3: eight was more readily available. It also made the machine
Speaker 3: cheaper because it only had an eight bit input bus,
Speaker 3: and so all of the bus structures were eight bit
Speaker 3: instead of sixteen bit. That will be changed with the
Speaker 3: later models, with the XT and so on.
Speaker 1: The turbo, remember the turbo, but.
Speaker 3: Turbo button go up to eight megahertz and dokey go like, yes,
Speaker 3: do not be processor cycle synchronized. That's not a right
Speaker 3: idea idea. It was always intended to be licensable. And
Speaker 3: so within a year Columbia Data Products makes the first
Speaker 3: of the PC compatibles and all of our careers. Yeah,
Speaker 3: also in the same year. And I never realized this
Speaker 3: happened in the same year. You always thought this was
Speaker 3: older the Osbourne one. The Osbourne one came out in
Speaker 3: nineteen eighty one. So this was what we call a luggable.
Speaker 3: It was literally like a suitcase keyboard attached onto the bottom,
Speaker 3: had a five inch displaying in the middle, a pair
Speaker 3: of five and a quarter floppy drives on each side,
Speaker 3: weigh about twenty five pounds speakers. Yeah, I remember having it.
Speaker 3: I had a compact, but not in Osborne in Osbourne.
Speaker 3: And there's a good reason for that, because Osborne were
Speaker 3: quickly out of business by Osborne in themselves. Yes, would
Speaker 3: just say as they started selling their first machine, they
Speaker 3: started talking about what was going to be a second machine. No,
Speaker 3: I was born too, and that stopped sales of Osborne
Speaker 3: one because everybody was waiting for that, and that bankrupted
Speaker 3: the company. So they never built the Osbourne two, and
Speaker 3: they became a verb. Do not Osborne yourself. Don't talk
Speaker 3: about your new product till you're ready to sell it.
Speaker 1: It's okay that we talk about the geek outs because
Speaker 1: our show is free.
Speaker 3: Yeah, there you go. One last thing on computing. We'll
Speaker 3: move on the IETF. That's the Internet Task Force publishes
Speaker 3: AREC Specification seven nine to one, which is a definition
Speaker 3: of IPv four.
Speaker 1: Oh there you go.
Speaker 3: Oh yeah.
Speaker 1: A lot of things happened in nineteen eighty one.
Speaker 3: That's the beginning.
Speaker 1: And in the cultural side, I have three things. Ronald
Speaker 1: Reagan inaugurated in December, shot in March. Nice yeah, sorry sorry, no, no, no,
Speaker 1: it wasn't nice. I mean, yeah, it wasn't nice. MTV debuts.
Speaker 1: I want my MTV Gary Newman here in my cars? Right,
Speaker 1: was the first video Killed the Radio Star? And also
Speaker 1: speaking of Donkey Kong that came out in nineteen eighty one. Awesome,
Speaker 1: But if you played it in turbomodate one and that's it,
Speaker 1: So I guess we should go for better no framework, right,
Speaker 1: all right? Play the crazy music? All right, buddy, what
Speaker 1: do you got? So I've got an interesting GitHub repo
Speaker 1: that's trending, and I thought it was topical for what
Speaker 1: we're going to talk about today. This is from the
Speaker 1: dot Mac and it's called claud dash mem. So this
Speaker 1: is persistent memory compression system built for Claud code. So
Speaker 1: it's a plugin. It's a plugin for Claud code that
Speaker 1: preserves context across sessions by automatically capturing tool usage observations,
Speaker 1: generating semantic summaries, and making them available to future sessions.
Speaker 1: So it's like client side context, right. It squishes it
Speaker 1: all down and then on every other prompt it sends
Speaker 1: it back. So it allows claud to maintain continuity of
Speaker 1: knowledge about projects even after sessions end or reconnect. So
Speaker 1: I kind of like that because you know, keeping all
Speaker 1: that context is kind of expensive these days. But we're
Speaker 1: going to talk about all that stuff coming up here.
Speaker 1: But first, I guess the next thing we do is
Speaker 1: talk about comments.
Speaker 3: Who's talking to us, Richard Hi grab a comment off
Speaker 3: show set nineteen seventy nine, So that's brand new, and
Speaker 3: that's the show we did with Callum Simpson talking a
Speaker 3: little bit about yak Shaver and our ongoing explorer defined
Speaker 3: apps that are actually out in the market using AI technologies.
Speaker 3: This comment comes from Blackweb, and I'm going to summarize
Speaker 3: them just a bit, but he says, on a recent project,
Speaker 3: I gave some AI tools that you discussed or try.
Speaker 3: We're trying to build at ten app using cloud and
Speaker 3: Aspire Native application in VS twenty twenty six, brand new
Speaker 3: VS code. The I tools included Copilot for Windows eleven,
Speaker 3: get hub Copilot with GPT five and Claude four point five.
Speaker 3: Copilot was helpful in all aspects of the project for
Speaker 3: coding and research, but it had trouble fixing errors and bugs.
Speaker 3: Often gets stuck in a loop and can't get out
Speaker 3: of it without some human help. When the Claude four
Speaker 3: point five plugin is at it, it gets even faster and
Speaker 3: better at all aspects of coding, including script generation, which
Speaker 3: it excels at. And now for the bad stuff. Get
Speaker 3: Hub's pricing model and billing is aggressive and predatory. It
Speaker 3: counts premium requests, which is basically anything you send in
Speaker 3: in VS and VS code. You starting free trial budget
Speaker 3: is twenty five requests, which most developers will use up
Speaker 3: in about an hour. You can set the dollar limit
Speaker 3: t additional requests for various purposes, including gethub actions and
Speaker 3: so on. For Copilot pro, you get three hundred quests
Speaker 3: for ten bucks, then of course they're all premium requests
Speaker 3: and you're showing a progress bar. I used up three
Speaker 3: point seven of my requests, so that would be like
Speaker 3: ten in a few hours. I quickly became paranoid about
Speaker 3: exceeding my request budget and running up a big bill.
Speaker 3: Claud's pricing model is even more aggressive than Githubs, starting
Speaker 3: it at seventeen dollars a month, it must be paid
Speaker 3: in advance for about two hundred bucks a year. I
Speaker 3: was impressed by Githubs, Copilot, and Claudes coding ability, but
Speaker 3: I immediately felt like it was aggressively trying to run
Speaker 3: up the request score as fast as possible. It is
Speaker 3: able to edit my code window, generate new files, enter
Speaker 3: in excute CLI commands, and rapidly generate tests, all of
Speaker 3: which eats into that budget. Get help Copilot did generate
Speaker 3: new code that worked, but it did it so fast
Speaker 3: that I had no idea what it had done, and
Speaker 3: had essentially no knowledge or understanding of the code degenerated,
Speaker 3: making me dependent on the AI for fixing any bugs
Speaker 3: it created, which of course consumes more credits. After a
Speaker 3: few hours with giphub, Copilot and Cloud, I felt like
Speaker 3: I'd been replaced by it and had become a little
Speaker 3: more than a human moderator or a monitor. Watched it
Speaker 3: work and gave minimal input in suggestions while running up
Speaker 3: AI tool generated bills of hundreds or maybe thousands of
Speaker 3: dollars per week. I also wondered if it was being
Speaker 3: used to train AI tools to replace me and other developers.
Speaker 3: AI coding tools are not creating bells developers. They're creating
Speaker 3: unemployed or underemployed AI coding tool addicts who are stuck
Speaker 3: in their exponentially increasing bill for those tools if they
Speaker 3: are independent coders. And I've builted the many podcasts on
Speaker 3: AI coding on dot in Rocks, I'm increasing concerned and
Speaker 3: baffled by the endless fanboyism for a by the two
Speaker 3: people I like and respect very much, Carl and Richard.
Speaker 3: You guys need to stop drinking AI kool aid and
Speaker 3: start talking to real developers who workflows and coding skills
Speaker 3: are being adversely affected by AA coding tools that you're
Speaker 3: providing with free blowing and biased reviews.
Speaker 1: Yeah, you know, I've been waiting for a comment like
Speaker 1: this because there is that sentiment out there and it's
Speaker 1: probably underrepresented by our audience. So, first of all, a
Speaker 1: black Web, thank you very much for being brave enough
Speaker 1: to send that to us. And yeah, I mean, I
Speaker 1: feel your pain. However, I don't share your experience, and
Speaker 1: I shared my experience, which is, you know, mostly positive.
Speaker 1: There are times when I don't understand the code that
Speaker 1: the AIS written, but I usually ask it to comment
Speaker 1: any code that it writes so that I can understand it.
Speaker 1: I also develop in pieces. If I say need a
Speaker 1: full stack editor for some classes, I'll start with the models,
Speaker 1: than the data layer, than any kind of manager, then
Speaker 1: finally the UI. I might break all these down into
Speaker 1: different prompts if they're too complex. I didn't always think this,
Speaker 1: but now I believe in baby steps and it works
Speaker 1: for me and my agent of choice is the co
Speaker 1: pilot CLI and clauds on at four five. I never
Speaker 1: felt like it was taking over my job. It's only
Speaker 1: made me more productive, and he basically came back by saying,
Speaker 1: can we hear a balanced view of AI coding tools
Speaker 1: from actual developers, not AI apologists who are trying to
Speaker 1: sell a very expensive product. And I said, well, I
Speaker 1: am a real developer with real customers. The money they
Speaker 1: pay me and the productivity games far out weigh the
Speaker 1: costs of Copilot. I'm not an apologists for any technology
Speaker 1: or any company. I'm an independent developer and a podcaster.
Speaker 1: And he basically said, you know, you make a good point.
Speaker 1: Maybe I just need to figure out more efficient ways
Speaker 1: to use this.
Speaker 5: Well, so time is money, yep. And if this stuff
Speaker 5: saves you time, that's worth a lot. And you could
Speaker 5: easily justify paying charges for things like Claude if it's
Speaker 5: saving you hours of time.
Speaker 1: Yeah, and therefore making you money.
Speaker 3: Yes, I'm working with teams that are spending thousand dollars
Speaker 3: a month. Yeah, but they're cranking through six weeks prints
Speaker 3: in days like they're moving code fast. It isn't. And
Speaker 3: then my problem is and there's two things that really
Speaker 3: hit me with black webs comment and all about billing,
Speaker 3: Which is first is this feels like telcos We're only
Speaker 3: going to find out after the fact how much it
Speaker 3: costs you, and the fact that you're learning on the
Speaker 3: back of it and it's fixing itself means it makes
Speaker 3: a mess then cleans it up for you and gets
Speaker 3: paid for it like that is pretty annoying. Yeah, right,
Speaker 3: But the bigger thing to me is we still don't
Speaker 3: have real pricing. Right. This is the blue ocean phase
Speaker 3: of this technology where they're low buying the figures just
Speaker 3: to get us on board. It will be very interesting
Speaker 3: to see what the real economics this looks like when
Speaker 3: the crazy ends and everybody actually has to pay the piper, Like,
Speaker 3: is this actually going to be viable? I don't know
Speaker 3: the answer to that.
Speaker 1: It's so we have before we have a discussion that
Speaker 1: we need to wrap up the comment because that's basically
Speaker 1: what this whole show and.
Speaker 3: Black Web you're timing is impeccable having provide us with
Speaker 3: a comment that you literally says a foundation for the
Speaker 3: show about what is AI going to look do to
Speaker 3: development in the next in these next few years. So
Speaker 3: thank you so much for your comment and a copy
Speaker 3: of music Code Buy. It's on its way to you.
Speaker 3: And if you'd like a copy of music Code Buy,
Speaker 3: write a comment on the website at dot netroocks dot com.
Speaker 3: When the facebooks you publish every show there, and if
Speaker 3: you comment there and ever reading the show, will send
Speaker 3: you a copy of music.
Speaker 1: Go and if you don't want to do that, you
Speaker 1: can always just go buy it and music to code
Speaker 1: by dot net. It's twenty five minute tracks. There's twenty
Speaker 1: two of them. I'm working on twenty three now and
Speaker 1: you can get the collection in MP three, wave or flak.
Speaker 1: All right, now, let me formally introduce the other two
Speaker 1: guys that are here with us tonight, starting with Jeff Fritz.
Speaker 1: Jeff is a principal program manager in Microsoft's Developer division
Speaker 1: on the dot Net Community Team, where he leads development
Speaker 1: of live video and online content. Jeff is the executive
Speaker 1: producer of the dot Net coonf series of online events.
Speaker 1: Heard of that. He is also a Twitch and YouTube
Speaker 1: partner as well as the founder of the Live Coders
Speaker 1: stream team. You can catch Jeff writing dot net code
Speaker 1: with Gethub Visual Studio in Azure on his video stream
Speaker 1: called Fritz and Friends at Twitch, dot tv, slash c
Speaker 1: Sharp Fritz. Bill Wolf is here and this is an
Speaker 1: important date for Bill, isn't it?
Speaker 3: Bill?
Speaker 5: It is? Because so tonight I've been running Philly dot net,
Speaker 5: which is one of the largest and certainly one of
Speaker 5: the oldest dot net communities on the planet, possibly in
Speaker 5: the Galaxy and been running it for twenty four years.
Speaker 5: So I started it in two thousand and one.
Speaker 1: And also you had something to do with this whole
Speaker 1: Inetta thing, do you remember I did.
Speaker 5: I was one of the I was a VP at
Speaker 5: Anetta and I ran the speakers Bureau and my job
Speaker 5: was to send famous people all over the country to
Speaker 5: various user group meetings.
Speaker 3: And that was all.
Speaker 5: Funded through Microsoft at that time, and so that was
Speaker 5: an interest project. But I started doing user groups in
Speaker 5: nineteen seventy eight, so some of you may not have
Speaker 5: been around then, but it's been a long time. And
Speaker 5: Rob Kaiser, who's been by my side much of this time.
Speaker 5: We were very instrumental in something called PAX, which was
Speaker 5: one of the first user groups in the country, Philadelphia
Speaker 5: era Computer Society. And I'll just one quick story. One
Speaker 5: of my favorite meetings I ever ran at PAS. We
Speaker 5: were at LaSalle University. This was in the early eighties
Speaker 5: and I was because I'm very shy, I was on stage.
Speaker 5: I was on stage sort of moderating, and the panel
Speaker 5: there were the team from eniac OH and because some
Speaker 5: of them still lived in the Philadelphia area, we actually
Speaker 5: got like remember that they got like six people in
Speaker 5: and they explained to us how they built eniac and
Speaker 5: how they made it run and how they tested it
Speaker 5: and stuff. It was fascinating.
Speaker 1: Was that related to the Jetson's uniblab because that's the
Speaker 1: one I remember when I was that old. Well, Bill,
Speaker 1: this is your last hurrah, isn't it. Yeah?
Speaker 5: Yeah, I'm gonna step down as the Philly dot net leader.
Speaker 5: I'll still be involved in community. I still do speaking
Speaker 5: here and there, but certainly not what I used to.
Speaker 5: I can't keep up with these guys. They do a
Speaker 5: dozen conferences a year. I used to do that, you know,
Speaker 5: I used to do the v bits and you know,
Speaker 5: connect and all of those things. But I'm, you know,
Speaker 5: on my way out.
Speaker 1: Well, we're going to give you a good send off.
Speaker 1: That's why we're here. So how about.
Speaker 2: Hands straight down?
Speaker 5: Yeah?
Speaker 3: I want to go back to the to the comments
Speaker 3: from the black web because I think it's all this
Speaker 3: fear element that I think is pervasive. Right now, if
Speaker 3: you think about what he just said, and I paraphrase it,
Speaker 3: Middley's original email was longer. It's like the tool got
Speaker 3: away from them. Yeah. Claud's especially kind of notorious for that, right,
Speaker 3: you get this sort of agentic mode where it's starting
Speaker 3: to make changes in all kinds of places. And if
Speaker 3: you start really thinking about about tokens all the time,
Speaker 3: now you're asking you questions about what it's done, which
Speaker 3: is burning more tokens for you. Just get a picture
Speaker 3: what's going on. Like, I think you got to get
Speaker 3: away from the token trap part.
Speaker 1: I would look into a good system prompt that puts,
Speaker 1: you know, cages, a cage around what you can and
Speaker 1: can't do and which you sure and shouldn't do. And
Speaker 1: I've learned from Jeff and other people that they're these
Speaker 1: great cope There's a Cope pilot, awesome repository that has
Speaker 1: all sorts of prompts for different things. There's a we
Speaker 1: just learned about this. There's a c sharp pro developer
Speaker 1: prompt that has all sorts of of you know, guidelines
Speaker 1: about how it should write code and all that stuff.
Speaker 1: If you're not using those things, you're missing out.
Speaker 5: And I'm running up your charges. Yeah.
Speaker 1: Yeah.
Speaker 5: So I'm an enterprise architect. I actually have a real
Speaker 5: job these days. And one of the things fascinates me.
Speaker 5: I'm at a company that has six seven thousand people
Speaker 5: and part of my job is to try and keep
Speaker 5: track of Azure cost usage.
Speaker 3: Oh wow, yeah, of course.
Speaker 5: Very few people know how to do that, and you
Speaker 5: have to sort of have a fintech degree. Where is
Speaker 5: a Kouva over here. You have to have a fintech
Speaker 5: degree to actually figure out how much it costs to
Speaker 5: run a website connected to a database and all the
Speaker 5: security and all the monitoring and all that stuff. But
Speaker 5: I can see the same problems they're going to apply
Speaker 5: in the AI space and until they have some better
Speaker 5: tools that help you understand that.
Speaker 3: Is part of the problem here is all this stuff
Speaker 3: is so masure like we don't have It's like Agile
Speaker 3: at the beginning, right where nobody really new well because
Speaker 3: you kind of got to do what you want because
Speaker 3: there was no plant.
Speaker 4: So there are tools that are built into Visual Studio
Speaker 4: visual Studio code where there are gauges that you'll see
Speaker 4: down in the foot of the tool. And I'm sure
Speaker 4: everybody here is using Visual Studio twenty twenty six. And
Speaker 4: yes I'm even talking to you, dear listeners. You do
Speaker 4: on that you're using the new Visual Studio, But on
Speaker 4: in the footer there's a little getthub copilot icon, and
Speaker 4: when you mouse over that or you click on it,
Speaker 4: it'll bring up a little set of progress bars and
Speaker 4: show you just how much you're using your Copilot resources.
Speaker 4: Now to a little bit of our our commenter to
Speaker 4: their request requirements. There are different models that you can
Speaker 4: use with Copilot that have different we call them multipliers,
Speaker 4: right right, Okay, I see the agile one has has
Speaker 4: jimmed in here, yes, rate, so right, there are some
Speaker 4: more energy hungry models that you'll see a higher multiplier
Speaker 4: run with. And right now here at the time of
Speaker 4: this recording, right Claude Opus is up there as a
Speaker 4: I think it's a three time multiplier right now. But
Speaker 4: there are other ones that you can use that have
Speaker 4: a zero multiplier, like GPT Mini.
Speaker 1: It's kind of like playing slots exactly.
Speaker 4: So what you want to do is you want to
Speaker 4: choose a model that's going to help you appropriately for
Speaker 4: the types of tasks you're going after. If you're doing
Speaker 4: some text summarization, you're doing a little bit of small
Speaker 4: code generation, using that mini model is going to really
Speaker 4: help you and not cost you a thing. But if
Speaker 4: you're trying to one shot an asp net core controller
Speaker 4: with some great views to go along with it that
Speaker 4: use Bootstrap, break out that Claude Sonnet four to five
Speaker 4: and go to town.
Speaker 1: Right.
Speaker 5: So if I ask Copilot in visual Studio, is it
Speaker 5: going to tell me what's a way to save money
Speaker 5: by changing my model?
Speaker 1: I would.
Speaker 4: So the models sometimes they know about each other, sometimes
Speaker 4: they don't, and you can give them. There's a fetch
Speaker 4: command that you can give copilot GitHub copilot right hashtag
Speaker 4: fetch space and then you can give it a URL
Speaker 4: and it will go out and analyze that RL. So
Speaker 4: you can point it to to your point. Bill, you
Speaker 4: can point it to that pricing page on the GitHub
Speaker 4: website and have it report back here's the relative pricing
Speaker 4: of the models, and it might be able to give
Speaker 4: you some advice as to hey, let's use this model
Speaker 4: instead of this for certain.
Speaker 3: Types of tasks.
Speaker 4: So there is also auto mode, which is there's the
Speaker 4: real slot machine.
Speaker 1: Right, Yeah, that's a weird one, that's right.
Speaker 4: Hey, do choose the model appropriate for this task that
Speaker 4: I'm asking you about, and it will go and figure
Speaker 4: it out. And the best parties, it's not going to
Speaker 4: tell you which one that.
Speaker 3: You that you haven't even turned it all the way up.
Speaker 3: Automode is one thing. Automode with unsafe.
Speaker 4: So curl and curl, and I might have some experience.
Speaker 3: Doing a little bit of that.
Speaker 5: Please don't do this at home.
Speaker 3: No, no, no, but again I have seen folks dialed
Speaker 3: in like that. Yes, and the burn rate is spectacular.
Speaker 3: But you've written your prompts well, and these are not
Speaker 3: These are multi page prompts. These are not small things. No. Yeah, yeah,
Speaker 3: the burn significant that the co generation is astonishing, and.
Speaker 5: So having a very explicit instead of instructions, the prompt
Speaker 5: yeah can reduce the token.
Speaker 1: Yeah, especially with Claude because, like Richard said clause Claude
Speaker 1: is kind of like Scooter the intern that's over eager
Speaker 1: to please the boss. Miss Frankly, shopping your Patsy is
Speaker 1: twice I already shopping them again, So it goes out,
Speaker 1: and you know, I thought you might need some more paper,
Speaker 1: So I went to Staples and I bought fifty reams
Speaker 1: of paper that kind of stuff. And like, I didn't
Speaker 1: ask you to do that. So in the sipstem prompt
Speaker 1: you have to say, don't do anything except exactly what
Speaker 1: I tell you to do, right.
Speaker 4: And this is this is one of the reasons why
Speaker 4: when I when I give talks that that get into
Speaker 4: AI topics like this, I like to come back to
Speaker 4: I believe that the best folks at writing those prompts,
Speaker 4: it's going to be the elementary school teachers.
Speaker 1: Right, because are used to talking to kids. You have
Speaker 1: to hear it's splicit.
Speaker 4: They've got a room full of kids that have ADHD,
Speaker 4: they've got opposition defiance disorder, and you don't know what
Speaker 4: they're going to do.
Speaker 1: Get in the tub, the water use.
Speaker 5: We forgot that step.
Speaker 4: Right, So it's it is one thing to be very explicit.
Speaker 4: You mentioned the awesome Copilot repository. Our friend Burke Holland
Speaker 4: did an amazing job putting together what he called beast Mode,
Speaker 4: which was right, a series of system prompts that help
Speaker 4: out the GPT models to really get you towards your solution.
Speaker 4: But there's also agent descriptions out there so that you
Speaker 4: can bring down those markdown files and load in and
Speaker 4: get that persona.
Speaker 3: Right.
Speaker 4: When we talk about talking to an LLM, one of
Speaker 4: the first things we refer to before you even get
Speaker 4: into defining the context of the problem that it needs
Speaker 4: to solve, is defining that persona who are you going
Speaker 4: to behave as what are you going to do? And
Speaker 4: you mentioned the expert c sharp You are an expert
Speaker 4: c sharp developer and you know how to do this,
Speaker 4: that and the other. I saw one that was one
Speaker 4: of the agents that's in that repository was talking about
Speaker 4: you are a fantastic at writing unit tests, and it
Speaker 4: even referenced personalities in the tech space and said, you
Speaker 4: have the ability to write tests like this person, and
Speaker 4: you know object oriented programming like Uncle Bob Martin, and
Speaker 4: you're going to be able to define and refine and
Speaker 4: do these things.
Speaker 1: Well would uncle Bob do?
Speaker 4: I know?
Speaker 1: So we talked about this and Code with AI episode
Speaker 1: ten which came out this week, that writing agents, and
Speaker 1: we just sort of tried to differentiate between an LM
Speaker 1: and an MCP, which is a model control protocol, model
Speaker 1: content context protocol. I always get that messed up and
Speaker 1: agent right, So the agent is at the highest level
Speaker 1: and the LM you would give the system prompt, right,
Speaker 1: but the agent has a prompt. That's just like Jeff
Speaker 1: was saying, what is your persona, what is your area
Speaker 1: of focus that you're going to do? Right? Are you
Speaker 1: only going to work on testing? Are you going to
Speaker 1: work on code? Are you working on VB code? You're
Speaker 1: working on assembler code? Right, those are the kinds of
Speaker 1: things that an agent can help with, but it's at
Speaker 1: a high a much higher level, and it will do
Speaker 1: things on your behalf, not just not just code, right,
Speaker 1: And you just have to be careful that you don't
Speaker 1: give it permission to go, you know, sell your house
Speaker 1: or something.
Speaker 4: Don't don't give it that essay past word into your
Speaker 4: SQL surfer.
Speaker 3: No, not the production one anyway. Oh, they're supposed to
Speaker 3: be different, that's right, in production. I just told you
Speaker 3: guys still know essay? Like who uses essay anymore? Right? Yeah?
Speaker 4: Managed identity?
Speaker 3: Managed identities now, yeah, and we should take a break
Speaker 3: for these very important message.
Speaker 1: Yeah, we should. We'll be right back. Mission.
Speaker 4: What a show, it's intermission.
Speaker 1: What do you know?
Speaker 3: Okay?
Speaker 1: I think that was inquisition?
Speaker 3: Yeah, oh, agreed to get popcorn?
Speaker 1: 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.
Speaker 3: And we're back. It's don I Rocks Emberger Campbell. That's
Speaker 3: called Franklin. Hey, sitting with our friends Jeff Fritz and
Speaker 3: Bill Wolf. We're here at Philly dot net.
Speaker 1: Ten thousand people in the audience on a very first
Speaker 1: live show in quite a while, and really fun to
Speaker 1: be in front of sitting with everybody and having a
Speaker 1: little fun making a show about I think a pretty
Speaker 1: serious topic coming into the end of the year here.
Speaker 4: But in all seriousness, is this the first time you've
Speaker 4: done a show where there's a court downstairs in the building,
Speaker 4: just in case any of these folks get rowdy.
Speaker 1: That's true.
Speaker 3: Co birds.
Speaker 4: Yeah, I'll tell you know where in Philly.
Speaker 3: You know, I've been working with a couple of software
Speaker 3: development consulting companies now that are not only going all in,
Speaker 3: but they're trying to get all of their developers not
Speaker 3: only using the tools, but they're coming up with sets
Speaker 3: of standards for how they want to make software as
Speaker 3: a whole for their customers. And so they're building out
Speaker 3: templates for deployment, they're building templates for infrastructure, they're building
Speaker 3: out templates for UAX, all focused on educating the cogenerators.
Speaker 3: And that's all these things are as cogenerators right to
Speaker 3: stay within the lines, so that each developer doesn't have
Speaker 3: to get those prompts right, they're just pre configured for them.
Speaker 3: Of like, this is the way we do things. There's
Speaker 3: some really great things to be said about those agents
Speaker 3: and being able to supply to them, or being able
Speaker 3: to pass in in a prompt, a prestructured prompt that
Speaker 3: you can have written down and out there on disk.
Speaker 3: Have those templates built out and sitting in markedown format,
Speaker 3: so that even when you browse your repository and you
Speaker 3: look at the markdown, you can look at the template
Speaker 3: and you know.
Speaker 4: That it looks the right way that you expect it to.
Speaker 4: And the ability for the AI the LM to generate
Speaker 4: and stick to that is really really good. But I
Speaker 4: think Carl made a really good point. Don't just stick
Speaker 4: to the template, but you got to tell it what
Speaker 4: not to do about the template as well.
Speaker 3: Right to get outside of the line.
Speaker 4: There's an example I like to show where I gave.
Speaker 4: I gave an LLLM the ability to summarize the weather
Speaker 4: scenario for a weather forecast, and I gave it four
Speaker 4: options sonny, cloudy, rainy, and snowy and I would send
Speaker 4: into the LLLM a bunch of different forecasts, and inevitably
Speaker 4: it would come back and say clear, nice, clear, Clear
Speaker 4: isn't one of them, right, So you have to tell
Speaker 4: it what to do and what not to do.
Speaker 1: This is a topic that we've talked about a lot
Speaker 1: recently on dot net Rocks, probably much to the chagrin
Speaker 1: of many of our listeners. Can you guys, please let's
Speaker 1: talk about something else than AI pat The fact of
Speaker 1: the matter is it's fundamentally changed the way we write code.
Speaker 1: For me, certainly has, I know for a lot of
Speaker 1: our listeners it has. Black Web may be one of
Speaker 1: the exceptions, but it really has changed the way I
Speaker 1: write code. So here's a really good example. And I
Speaker 1: love using my personal little stupid projects. I'm sitting there.
Speaker 1: I have a forty nine inch screen, and my wife
Speaker 1: and I play this game we call Sherlock, and it's
Speaker 1: a logic puzzle game. It's from the nineties, I think,
Speaker 1: but it's been ported to a bunch of things anyway,
Speaker 1: So it has to live on one side of the
Speaker 1: screen or the other because my wife and I sit
Speaker 1: together and usually I'm watching something on a streaming service
Speaker 1: she's playing, but then when she goes out, I want
Speaker 1: to switch them around, right, So I got to switch
Speaker 1: them like this. So I basically had co pilot generated
Speaker 1: little Windows app that says, Hey, I want to put
Speaker 1: this window here, in this window here, and swap them
Speaker 1: just swap them. Yeah, And I literally did it in
Speaker 1: fifteen minutes, and I know the code. It's not to
Speaker 1: do it, yeah, I don't know, God no, but but
Speaker 1: you know I know how to do it. It's not
Speaker 1: a magerate matter of me being lazy. I just it
Speaker 1: was a fifteen minute thing and I was just like, wow,
Speaker 1: that's so cool.
Speaker 4: But vibe coding is a very powerful thing that I
Speaker 4: think is valuable for project managers. Program managers when they
Speaker 4: get the idea for a user interface update, for a patch,
Speaker 4: and they're able to take some code, they're able to
Speaker 4: take some screenshots, some ideas, talk to the AI generate
Speaker 4: those concepts. And for those of us that are in
Speaker 4: the industry that are experts, like you dear listener with
Speaker 4: the headphones on, like, you can do this, and you
Speaker 4: know those edge cases that you want it to do,
Speaker 4: and we also know how to tell the AI, hey
Speaker 4: there's a plan here. You need to build because I
Speaker 4: am a project planner. I am a project manager, and
Speaker 4: I need you to build that project plan, that spec
Speaker 4: so that we can walk through it. And Copilot is
Speaker 4: very good at following a document and execute on those plans.
Speaker 1: Well, have you done any vibe coding with AI?
Speaker 3: Yeah?
Speaker 1: What do you think?
Speaker 3: It helps a lot?
Speaker 1: Yeah?
Speaker 5: I just you know the old days of you weren't
Speaker 5: sure how to do something. You go to the web,
Speaker 5: you do a you know, a stack, and you know
Speaker 5: and copy stuff and a lot of a lot of
Speaker 5: programmers just copy exactly what's in the page. That leads
Speaker 5: to all sorts of pan.
Speaker 3: Take the error message and the copy that but to Google.
Speaker 5: But having AI sort of guide me through things that
Speaker 5: I haven't done in a while, it really really a
Speaker 5: time saver.
Speaker 1: And you know, to the to the point that I
Speaker 1: was making a black web. You can ask it to
Speaker 1: explain itself. I don't understand this, what's it doing? Comment
Speaker 1: it or give me a summary of what you just did,
Speaker 1: you know.
Speaker 5: But we also use it for QA, generating tests, DevOps,
Speaker 5: you know, figuring out you know, sort of scripts and
Speaker 5: recipes for deployments and you know resources. There's a lot
Speaker 5: of places that it applies beyond just the C sharp coder.
Speaker 3: You know, if you're billing by the hour as a developer,
Speaker 3: you really don't want to No, you.
Speaker 1: Don't want to tell anybody that you even have them installed.
Speaker 3: Yeah, I think or if you are users tools, you
Speaker 3: gotta lie about your hours or matter. It's still I
Speaker 3: think you have to restructure HIO, you, Billy.
Speaker 5: The other side of that, Richard, is I think you
Speaker 5: have contractors that work remotely that really understand these tools,
Speaker 5: and they can actually juggle multiple clients and build them concurrently.
Speaker 5: Not that that's a good thing, but I think you're
Speaker 5: seeing stuff like that going on too.
Speaker 3: Yeah.
Speaker 5: And one of my favorite parts, how many of you
Speaker 5: ever have to interview people? Yeah, don't you love when
Speaker 5: you ask them a question and their eyes are going
Speaker 5: back and forth, and you know that they're talking to
Speaker 5: Claude or some other model and saying, give me a
Speaker 5: good come on, give me something.
Speaker 1: Yeah, there's no such thing as a radio quiz anymore.
Speaker 5: No.
Speaker 3: I talked to one interviewer. He said, here's how I
Speaker 3: asked the question. Close your off, now answer this question.
Speaker 3: That's good.
Speaker 4: Yeah, Well, you made a good point earlier. Richard about
Speaker 4: how project teams are able to get through a six
Speaker 4: week sprint in days.
Speaker 3: What was a project team for six weeks was a
Speaker 3: person with six or seven agents, burning credits like crazy. Sure,
Speaker 3: you know, running hard, but the results were again astonishing.
Speaker 4: What we really can can get into now is those
Speaker 4: things that were in the parking lot on the combine board, right,
Speaker 4: they're now in play.
Speaker 3: Well, you're exactly right. I could keep telling you know,
Speaker 3: black web handed at this everybody's talking about it's like
Speaker 3: we're all going to lose their jobs, and I'm like,
Speaker 3: it just doesn't look like it. A. We need the Shepherd,
Speaker 3: but b how much it's not like any of us
Speaker 3: we're getting to the bottom or to do list right. Yeah,
Speaker 3: Ever there's always more And how many other projects don't
Speaker 3: even get on the board exactly because the backlog is
Speaker 3: so far back Exactly. You go back to the Luddites
Speaker 3: and the industrial clothing production. While it was disruptive at
Speaker 3: the time, it also lowered the cost of cloth enough
Speaker 3: that people started owning more than one set of clothes.
Speaker 3: Imagine right, how many software projects have just never been
Speaker 3: written because they couldn't even get to the table.
Speaker 1: It's interesting that the Luddites were the technologists of the day.
Speaker 1: They had the knowledge of what they were doing, clothes
Speaker 1: out of weaves, and it was suddenly threatened by machines.
Speaker 1: But they were you know, if they were us now,
Speaker 1: like us, we have the knowledge of how to build software,
Speaker 1: so we can interact with the AI. They didn't have AI, right,
Speaker 1: but they could probably be more productive with the machines then.
Speaker 3: Well, in many on the street could follow that story.
Speaker 3: Over time, they got retrained on the new machine. Not
Speaker 3: all of them. Lots of people didn't want to play,
Speaker 3: but a lot of them did and it did change
Speaker 3: things around. It's hard to be in the disruption, but
Speaker 3: we're in the disruption.
Speaker 1: So it reminds me of the CAD revolution, you remember that.
Speaker 1: So my mother was actually a draftsman at Electric Boat.
Speaker 1: Electric Boat, Yeah, and she she did everything by hand
Speaker 1: and she was very good at it. And her friend
Speaker 1: got on the CAD system. She's like, I don't want
Speaker 1: to do that, right, But they left her alone, and
Speaker 1: you know, she listened to her music and did her
Speaker 1: drawings and stuff, and when anybody wanted any real detail
Speaker 1: work done by hand, they called her.
Speaker 4: I was one of the last classes at my university
Speaker 4: that that had a draftsman class required in engineering. I
Speaker 4: had to learn how to use.
Speaker 1: All the tools and slide roll.
Speaker 4: Yeah, a little bit of slide rule.
Speaker 5: So that really makes me remember my father was an
Speaker 5: electrical dressman. Yeah, and he did the routing for electrical
Speaker 5: circuits in nuclear power plants. He worked out three mile Island.
Speaker 5: You can imagine, well, you know, but it's it is
Speaker 5: something that happened, yes, and uh, you know, I don't
Speaker 5: I doubt if his circuit caused the problem, but you
Speaker 5: can imagine all the drawings. It takes every little wire
Speaker 5: back then because it wasn't there was no tc P
Speaker 5: I P.
Speaker 1: Well, Wover Dam was built with slide rules. Yep, no calculators,
Speaker 1: no computers still standing.
Speaker 4: So let can we can we pivot the discussion a little.
Speaker 4: We've been talking about copilot, Claude, some of those tools
Speaker 4: that help us as developers. But I like to get
Speaker 4: outside that box a little bit and and something that
Speaker 4: that I want to make sure that that our developers
Speaker 4: here in Philadelphia and the listeners I want to I
Speaker 4: want to make sure that we're thinking about is not
Speaker 4: just how to use the AI to get your job done.
Speaker 3: To build a.
Speaker 4: Website, but how can you help your customers use the
Speaker 4: AI to deliver their requirements, their needs to their customers.
Speaker 4: There's there's more to that than just slapping a textbox
Speaker 4: into the middle of your appplications so they can ask
Speaker 4: questions about their reports. There's a lot more that we
Speaker 4: can do with that that we need to get. That's
Speaker 4: a great feature, Oh, without a doubt, it's a great feature.
Speaker 4: I can recommend a podcast, yes, but there's there's there's
Speaker 4: things that you can do to return value to those folks,
Speaker 4: and I think that's someplace that we need to help
Speaker 4: the enterprises understand and use the AI better so that
Speaker 4: they can turn right. The developers can help their end users,
Speaker 4: whether it's somebody working in a call center, somebody who's
Speaker 4: working in accounting or doing financial analysis, help those folks
Speaker 4: get that same multiplier that we're realizing as developers.
Speaker 5: Some of the work I'm doing is square there and
Speaker 5: it relates to CAD because I work for a company
Speaker 5: that manufactures construction components to build buildings, and part of
Speaker 5: what we're looking at is AI. How do you, as
Speaker 5: an architect, how do you design a building for the
Speaker 5: coast of Florida, so it can handle hurricanes. And if
Speaker 5: you have and if you ever see those pictures after
Speaker 5: this dorm, you know, a whole bunch of buildings are flat.
Speaker 5: There's one or two buildings center block that know that
Speaker 5: are standing straight. And the AI said, but that's probably
Speaker 5: probably the company I'm at. It has to do with
Speaker 5: how you tie all the pieces together with.
Speaker 3: Metal, roof doesn't get pulled out.
Speaker 5: Yes, yes, and and we do all those calculations, but
Speaker 5: we're trying to figure out how to get a I
Speaker 5: to help do that kind of work.
Speaker 3: That's tricky.
Speaker 1: That's that's cool.
Speaker 3: Yeah, we've been followed. One of the show recurring shows
Speaker 3: for us has been Vishwaz's show where he's now left
Speaker 3: gone into a startup to build l M tools for
Speaker 3: generating RFP responses to government contracts. Oh and it's been
Speaker 3: fascinting to listen to him as he's as they've learned
Speaker 3: more and hit the challenges and sort of that progress.
Speaker 3: It's been a couple of years of this now, like
Speaker 3: we're just trying to pull a narrative together, Like he's
Speaker 3: taking on a tough problem and he's learning more about
Speaker 3: it getting on there, like these different cases are part
Speaker 3: of us trying to find stories about what's working what isn't.
Speaker 1: But a lot of these What's.
Speaker 5: The biggest problem I have with AI is dealing with
Speaker 5: friends and family, oh god, yes, and holiday parties and
Speaker 5: they're like.
Speaker 1: HEYI is ruining everything.
Speaker 3: I'm like, oh, please don't.
Speaker 5: And they come up to me at the party and
Speaker 5: specifically like, you are responsible, You're doing this.
Speaker 1: Right, it's your fault.
Speaker 3: Well. AI seems to be the current scapegoat for whatever
Speaker 3: is happening. It is, but listen, a I didn't lay
Speaker 3: anybody off. That was people. People laid up folks off
Speaker 3: and they may have used AI as their current excuse,
Speaker 3: but we're also seeing lots of them walk that back afterwards,
Speaker 3: but it doesn't get the results they wanted, or that
Speaker 3: it actually wasn't what they were to do in the
Speaker 3: first place. Right.
Speaker 1: I know that, you know, I have a young gen
Speaker 1: Z daughter and her generation is feels hopeless, you know
Speaker 1: about the future that you know, AI kind of take
Speaker 1: everybody's jobs and all that stuff that you were talking about, Bill,
Speaker 1: and it's I just have to constantly express to her
Speaker 1: that you know, this is no excuse to not do
Speaker 1: your best at what you want to do in life.
Speaker 1: You know, if you want to learn something for the
Speaker 1: for the sake of learning it, learn it doesn't matter
Speaker 1: if there's an AI that knows it too. You go
Speaker 1: do what you want to do and be the best
Speaker 1: whatever that you can.
Speaker 3: Assibly help you.
Speaker 4: And yeah, that's that's a great point Bill, where I've
Speaker 4: I've talked to some folks, some artists who really don't
Speaker 4: like and I I agree with him, really don't like
Speaker 4: using AI to generate cartoons, images, photos. You're generating those
Speaker 4: images because you can't get a photographer or the personalities
Speaker 4: that you want to appear in those In those pictures,
Speaker 4: you're you're assembling them for some reason because you can't
Speaker 4: get that to happen, or you can't an artist to
Speaker 4: draw a cartoon. But what you can use the AI
Speaker 4: for is to analyze something that you drew or something
Speaker 4: that you made. Give it a picture and say, you
Speaker 4: know what, take a look at this, review it and
Speaker 4: tell me how I can make this better. And then
Speaker 4: to your point, I can learn how to improve my
Speaker 4: skills and do more I do.
Speaker 3: On the consulting side, I'm not talking to folks who
Speaker 3: are making money off of cleaning up by code of
Speaker 3: projects that don't work. Oh yes, well, and I feel
Speaker 3: like like we're in a stupid period, right, this is
Speaker 3: the early days of this tech. We're still in the
Speaker 3: AI bubble, which is a very stupid period, and people
Speaker 3: are not knowing how to use these tools, are getting
Speaker 3: in over their heads. Yeah, like I did this with
Speaker 3: access back in the day. Lots of people spent the
Speaker 3: weekend and got themselves over their heads, Like there's money
Speaker 3: to be made cleaning up messes and building out the
Speaker 3: kit to get good at cleaning up the message this
Speaker 3: will pass only a few years.
Speaker 1: One of the best customer comments they ever got is
Speaker 1: why is it taking so long? My brother could do
Speaker 1: this in access?
Speaker 3: Yeah, like, you know what, you should try have your
Speaker 3: brother do it.
Speaker 1: I'm done, But.
Speaker 4: Richard, you're you're hinting at using AI to fix the
Speaker 4: mistakes that some human implemented with AI. Like the solution
Speaker 4: to AI being more AI feels like like a we're
Speaker 4: kicking the can down the road here a little bit.
Speaker 3: It's also a normal escalation, but they are missed. You know,
Speaker 3: we've all we've often misused tools and inexperienced you misused
Speaker 3: tools and experienced people can get results, and then you
Speaker 3: start getting real costs. You know, it is possible with
Speaker 3: minimal skill to get to a certain point in building
Speaker 3: an application with these tools, but finishing it it's hard.
Speaker 4: How many of us sell a sp net MVC applications
Speaker 4: with SQL statements sitting in a view right to our
Speaker 4: dot Net listeners?
Speaker 3: You put them in the parameter.
Speaker 1: Oh, just to change tax here. Anybody a musician or
Speaker 1: an amateur musician, play an instrument, write songs, jand up
Speaker 1: if you write songs, a couple of you, Okay, I
Speaker 1: want to know what you think of Suno and Suno
Speaker 1: is essentially something that can build a completely professional sounding song,
Speaker 1: complete with vocals and solos, just from a prompt and
Speaker 1: it sounds amazing. Now I'm a musician, it's very hard
Speaker 1: for me not to take offense at this, but I don't.
Speaker 1: I kind of look at this as like the Cassio
Speaker 1: keyboard of twenty twenty five, you know what I mean?
Speaker 1: Who used to have organs in the home where people
Speaker 1: could play take me out to the ballgame and learn
Speaker 1: their things. But that doesn't mean they're going to take
Speaker 1: a musician's job. Right. This thing, however, it's pretty awesome.
Speaker 1: So who raised their hand? What do you think about Suno?
Speaker 1: Andy down? Thumbs down?
Speaker 3: What's that?
Speaker 1: I can hear you, but i'll replay your yeah?
Speaker 3: Right?
Speaker 1: Why I have a famous pain or painting something when
Speaker 1: you can?
Speaker 3: Yeah? Right?
Speaker 1: Have you listened to a Suno generated song? There? Actually?
Speaker 1: Rick Piatto did a video where he made one in
Speaker 1: five minutes and it sounds like it was done in
Speaker 1: a professional studio with an emotion, a voice full of
Speaker 1: emotion and everything. Who else raised their hand? Over here?
Speaker 1: You you play piano? Oh you never heard of Suno?
Speaker 3: All right?
Speaker 1: Somebody else?
Speaker 3: Yeah? Go ahead?
Speaker 1: It feels gross to you? Yeah? Yeah, synthetic feels grotesque.
Speaker 3: Yeah.
Speaker 1: As a tool to create and refine art, it's kind
Speaker 1: of grotesque. Do you do you think you could tell
Speaker 1: the difference between a Suno song and a professionally recorded song,
Speaker 1: Because I'm a musician and a producer and I record
Speaker 1: bands and everything and make albums. I couldn't tell the difference.
Speaker 1: It was really really good a step, Yeah, it's really
Speaker 1: quite a step. But here's my opinion. I think that
Speaker 1: this is only going to make people want to go
Speaker 1: out and see live music that much more and I
Speaker 1: think that this is a wave, you know, and after
Speaker 1: people have been bamboozled or whatever by a what that's
Speaker 1: AI whatever, you know, then they're going to want to
Speaker 1: see real people performing with real talent.
Speaker 5: Are we approaching a point where all elevator music is
Speaker 5: going to be generated by AI?
Speaker 1: Probably is already.
Speaker 3: Yeah. Spotify is basically admitting they're headed down that path.
Speaker 3: You know how you you can turn on a Spotify
Speaker 3: playlist of songs, you know, and then it just starts
Speaker 3: adding stuff to it. Well, they don't even want to
Speaker 3: pay those royalties anymore, so they're going to start using
Speaker 3: they're using these tools. If you're not paying attention to playlist,
Speaker 3: it's just going to start then sizing music on you.
Speaker 3: And the question is will you notice, Yeah, okay, don't
Speaker 3: really perform their songs.
Speaker 1: Barry Manilowe, who sang I write the songs, didn't write
Speaker 1: And I don't want to make you just to paying
Speaker 1: someone to write the lyrics for me or paying someone
Speaker 1: to play the guitar in the track, because they're real people.
Speaker 1: So that's the difference. Someone would make that argument.
Speaker 4: I think there's a point when we when we do
Speaker 4: talk about generating art whether it's music, video, even generating stories,
Speaker 4: generating write fiction, drama that isn't just coming from nowhere, right.
Speaker 4: We are feeding it information. We are shaping the direction
Speaker 4: of those prompts and sending them down a path, and.
Speaker 1: For some using pieces from real art.
Speaker 4: It is so right, there's copyright concerns there. But when
Speaker 4: we think about that as as creators, when you're looking
Speaker 4: to get that that ball rolling, and you're able to
Speaker 4: have a conversation with an AI with a with a
Speaker 4: language model and start to tease out these things, then
Speaker 4: if you're if you're talking through I want to write
Speaker 4: a song, right and I'm just writing the lyrics and
Speaker 4: and then I don't know. I want to throw help
Speaker 4: me out understand what the what what the instruments are
Speaker 4: that I might want to put at this. It's still
Speaker 4: me that's saying, hey, you know what, let me let
Speaker 4: me hear what a what a jazz piano sounds like?
Speaker 3: With this?
Speaker 4: Personally, I want to hear a human play that. But
Speaker 4: when I'm writing the song, I don't know how to
Speaker 4: play jazz piano. I don't know how to compose.
Speaker 1: You might want to get some ideas.
Speaker 4: I want to get some ideas and to be able
Speaker 4: to get that first level expert to give me those
Speaker 4: ideas demo, to get a demo, to.
Speaker 1: Hire some real musicians to put their own stamp on it.
Speaker 4: Because to the to the point that I've heard a
Speaker 4: number of folks say, there's a human feel and emotion
Speaker 4: that you get when that you see not just when
Speaker 4: you listen to music, but when you look at an
Speaker 4: art piece and you see the paint strokes in the painting,
Speaker 4: when you when you see how an illustration is put together,
Speaker 4: you can see and feel how that was done that
Speaker 4: by a human, even if it is a human that's
Speaker 4: drawing in Adobe Photoshop.
Speaker 1: And conversely, when you read something that's been generated by
Speaker 1: an AI, it has that certain, I don't know, a
Speaker 1: sycophantic feel to it, doesn't it.
Speaker 4: It's got an extra emoji and mdash everything.
Speaker 1: It's just the way that it sounds like in your
Speaker 1: mind when you read it. I had this experience, But yeah,
Speaker 1: I can't. I don't know if this is true because
Speaker 1: I haven't confronted my friend about it. But I did
Speaker 1: a post on Facebook and it wasn't I don't remember
Speaker 1: what it was about, but one of my friends one
Speaker 1: of my Facebook friends commented, and I swear to god,
Speaker 1: this person just took my post, put it into chat
Speaker 1: GPT and said, give me a positive reply about this,
Speaker 1: and then they pasted it in. That's what it sounded
Speaker 1: like to me, and because it kind of summarized everything,
Speaker 1: you know, and said, oh, it's so good that you
Speaker 1: blah blah blah, you know, as if to prove that
Speaker 1: they understood what I was saying. Your real friends don't
Speaker 1: do that. No, right, No, they don't summarize and bullet
Speaker 1: point everything you said. So yeah, and let's they want
Speaker 1: to make fun of it.
Speaker 5: Right, So to move this in a different direction sort
Speaker 5: of positive to me, I think a good area for
Speaker 5: AI is healthcare. And if I have something going on,
Speaker 5: and yes, I want a doctor to read the X
Speaker 5: ray or the MRI, I sure as hell want Claude
Speaker 5: to also look at it, because if they find something
Speaker 5: that the doctor missed.
Speaker 1: Claude is so positive even if you were going to
Speaker 1: die and say, oh, if I just take a couple
Speaker 1: of shots and.
Speaker 3: That's one of the best lung tumors I've ever seen.
Speaker 4: Right, Claude starts every response with you're absolutely.
Speaker 1: Right, right, say when you tell it it's wrong, but
Speaker 1: you're expected lifespan is lesson sick Bill.
Speaker 4: You actually make a good point there, because I'm one
Speaker 4: of those people who I'll wait to the last minute
Speaker 4: to go to the doctor. Right, I've got don't do
Speaker 4: that symptoms.
Speaker 3: Thank you.
Speaker 4: My wife, missus c Sharp Fritz, has been keeping me
Speaker 4: honest with that. So but but right, I've got symptoms
Speaker 4: x Y and say, you know, I've got this weird
Speaker 4: pain in my leg that happens at some time in
Speaker 4: the afternoon. And she's like, you really should talk to
Speaker 4: the doctor about that. But I was like, I don't know, but.
Speaker 1: Okay, honestly, wouldn't trust an AI to give me any
Speaker 1: kind of so anything.
Speaker 4: I mentioned here's the medications that I'm taking, here's the
Speaker 4: weird symptoms that I'm feeling. And this happened to me
Speaker 4: while I was in Portugal for a conference we were
Speaker 4: speaking at and it came back and said, no, you
Speaker 4: you might want to talk to your doctor about that,
Speaker 4: because you might have a wrong dose on this medication
Speaker 4: you're taking. Sure enough, talk to the doctor and yeah,
Speaker 4: let's dial that back. And I don't have the problem.
Speaker 1: But I wouldn't.
Speaker 4: I would have right, because I'm a middle aged guy
Speaker 4: in America. I would have been like, when you drive
Speaker 4: or anything, it could be supreme tuma, but probably a headache.
Speaker 3: The radiology story is an interesting one because there was
Speaker 3: this whole point made by like Jeff Hinton ten years
Speaker 3: ago saying, you know, generative AI is now figured out radiology.
Speaker 3: It's better at analyzing images than humans are. Radiologists are
Speaker 3: are done, They're totally unnecessary. He was one hundred percent incorrect.
Speaker 3: The demand for radiologists has only gone up, and part
Speaker 3: of that is that there was such a huge unmet
Speaker 3: demand for imaging, and the software accelerated the ability for
Speaker 3: radiologists to do a good job. And so now they're
Speaker 3: doing three times as much imaging and most radiologists now
Speaker 3: use it's like seven hundred models certified by the FDA
Speaker 3: for imaging.
Speaker 1: So this is where I totally agree with Bill that
Speaker 1: you have these specified models that are trained on their
Speaker 1: particular like radiology data, right, that are so narrowly focused
Speaker 1: that they're going to give you a better outcome diagnosis.
Speaker 1: Then if you just you know, type in a chat GPT,
Speaker 1: you know, I got it.
Speaker 3: Yeah, I wonder if we're going to move away. As
Speaker 3: the dumb winds down on this. You know, if you
Speaker 3: think how much better the web got after two what's
Speaker 3: the level you know, after the dot com boom ended,
Speaker 3: and you know, after a year or two, we built
Speaker 3: better websites, like we stopped racing, started to think about
Speaker 3: what actually made sense. Right now, the business is good
Speaker 3: for making them models bigger, right Those companies are all
Speaker 3: incentive to make models bigger because they're going for these
Speaker 3: outrageous evaluations. That means they have to constantly show they
Speaker 3: need to spend more money counter to what they actually needed,
Speaker 3: what's actually benefit show the customer. So as this ends,
Speaker 3: and it must, Yeah, I'm wondering if we're not going
Speaker 3: to go a local model. Right. So software development is
Speaker 3: actually a pretty narrow domain space. It's pretty tight. So
Speaker 3: I wonder when the dumb wind's down and we are
Speaker 3: focused on the most efficient ways to do things, is
Speaker 3: that the tookys are gonna become irrelevant. You're gonna own
Speaker 3: that gear.
Speaker 1: And also it becomes more attractive to your customers because
Speaker 1: you're not sharing screenshots and code of their Yeah, you know.
Speaker 3: There's how you get rid of the whole sovereignty problem.
Speaker 3: Never leaves the building.
Speaker 4: Yeah, foundry local is is absolutely a thing, O Lama.
Speaker 4: Running locally is absolutely a thing that we see folks
Speaker 4: using more and more. Right, we get the we get
Speaker 4: the fee models that are running locally on Windows. You
Speaker 4: can run Quinn on Windows. And if you're running with
Speaker 4: that the surface laptop with the NPU on it, you
Speaker 4: can do some amazing things. If you're playing Fortnite and
Speaker 4: you've got a fantastic GPU on your system at home, well, yeah.
Speaker 3: And the radio implodes. GPUs are only going to get cheaper. Yeah,
Speaker 3: and we're probably going to be you know, racking up
Speaker 3: a few of those things in an office. Somebody's got
Speaker 3: to running them on stop.
Speaker 4: We are still seeing that that growth of data centers, right,
Speaker 4: there's been there's a number of properties here in the
Speaker 4: southeast Pennsylvania region around Philadelphia where we are seeing old
Speaker 4: old warehouses, old factories that are being bought out and.
Speaker 5: Being turned into nuclear power plight.
Speaker 4: Let's come back to that in a second. But we're
Speaker 4: seeing these older, right, industrial facilities that are being bought
Speaker 4: out and turned into data centers. And there's people protesting
Speaker 4: because it's it's higher demand on electricity to bill you
Speaker 4: made an excellent point. We brought up three mile.
Speaker 3: Hour in Philadelphia.
Speaker 1: When you say we Microsoft, yeah, correct, Yeah, his blue
Speaker 1: badge is showing. Yeah.
Speaker 3: So Microsoft has kicked in something two billion dollars to Constellation,
Speaker 3: which is the company that had been operating three Mile Island.
Speaker 3: They turned it off in twenty nineteen because it costs
Speaker 3: more to operate than the natural gas combined cycle plants
Speaker 3: that have been built all over Pennsylvania now. And but
Speaker 3: Microsoft doesn't care. Eight hundred megawatts of electricity is eight
Speaker 3: hundred megawats electricity, and it was a way to try
Speaker 3: and put that more power online. It's probably gonna take
Speaker 3: a few more years for they get it done. Twenty
Speaker 3: seven seven, that's what they're shooting for. They're probably it'll
Speaker 3: probably take longer debt. There's a dozen plants like that
Speaker 3: around the US. So now Google has approached some some
Speaker 3: folks in Cedar Springs and Idaho, same kind of situation.
Speaker 3: Five reactors shut down for five years. So I mean
Speaker 3: it's going to help. We're in any more power than that.
Speaker 3: It's interesting to think in terms rather dystopic terms of
Speaker 3: tech companies now going to advance technologies on power generation
Speaker 3: of course, I'm about to record the energy geek out,
Speaker 3: So here you go, you get a preview. Amazon's now
Speaker 3: committed to a new reactor design. Like all the tech
Speaker 3: giants are looking for other power source and.
Speaker 5: Another place AI is got a factor in as grid management. Yeah,
Speaker 5: without getting the electricity.
Speaker 3: But I also think we're overbuilding, right, We're in that.
Speaker 3: We're again in the bubble situation. Like what was the
Speaker 3: overbuild during the dot com boom? It was fiber optic cable, right,
Speaker 3: there was a ton of fiber optic cable was late
Speaker 3: and many of those companies went broke shortly thereafter, and
Speaker 3: the fire and the and the cable was bought up
Speaker 3: ten cents on the dollar. Yeah. The only thing I
Speaker 3: would say that's safer this time around is the tech
Speaker 3: giants for the most part, have been spending cash money
Speaker 3: they already have that it would have used on stock
Speaker 3: buybacks or something and instead or turning it into land.
Speaker 4: But having that extra electric power on the grid not
Speaker 4: only is going to facilitate AI focused data centers, but
Speaker 4: we've been trying to do electric cars for how long?
Speaker 4: We are doing electric cars right, but I mean at
Speaker 4: scale with significant delivery. Here the amount of draw that
Speaker 4: that puts on the grid. When we start thinking about
Speaker 4: higher percentages of vehicles on the road electric based.
Speaker 3: They're not even close to the same league of what
Speaker 3: pot data centers are trying to consume.
Speaker 4: No, no, not at all. But having that extra power on.
Speaker 3: The it's not have more power on ARID is not
Speaker 3: going to hurt. You could stand to have more electricity.
Speaker 3: I think it's going to be an overbuild. I think
Speaker 3: a lot of them are not going to get finished.
Speaker 3: I think it's interesting to think in terms of Microsoft
Speaker 3: not being a software company anymore. Yeah, they're a utility company.
Speaker 3: Their utility.
Speaker 1: So should we call this the future of not software
Speaker 1: development but the future?
Speaker 3: No, I've renamed it the role of AI and software
Speaker 3: development because that's clearly clearly we ended up for better
Speaker 3: or worse. It's only a future. I've been doing this
Speaker 3: AI hype keynote for a while now, very much focused
Speaker 3: on what the end of this bubble looks like because
Speaker 3: we've been through this before. Lots of people are going
Speaker 3: to be hurt. I only it's going to be us. No,
Speaker 3: We're a scarce resource and our ability to learn these
Speaker 3: new tools isn't going to go away. This tool is
Speaker 3: not going to go away anymore than the Web went
Speaker 3: away after the dot com boom ended. In fact, efficiency
Speaker 3: is going to be the word, and most people in
Speaker 3: software actually like being efficient. They don't like the rampant inefficiency.
Speaker 3: And so some ways the knuckle downtimes of a downturn
Speaker 3: is where we thrive because we do make our companies
Speaker 3: more efficient with what we do, and these tools can
Speaker 3: do that. It's just that right now there's a lot
Speaker 3: of people incentive to not focus on that. That will end.
Speaker 3: The tools won't.
Speaker 4: So if we're on AI one point zero and Web
Speaker 4: one point oh, was everybody build your HTML? Web two
Speaker 4: point zero is social media? Now?
Speaker 3: It was? It was mash up too, right, Like I
Speaker 3: would argue that the post bubble AI will be hybrid,
Speaker 3: a lot more client side and a lot more focused
Speaker 3: model types. Like I wonder if you think about how
Speaker 3: constrained you are in a version three of the piece
Speaker 3: of software, right if adding something new to the stack
Speaker 3: is anathema. Right, this is our data store, this is
Speaker 3: our client libraries, like your kind of in a box.
Speaker 3: Like that's a pretty tight little model. Like here's an
Speaker 3: interesting thought for you. The more mature your software gets,
Speaker 3: the less it's going to cost to maintain because the
Speaker 3: simpler the model is, because the constraints are so clear,
Speaker 3: and your most experienced developers who don't want to maintain
Speaker 3: that software and move on to the new things. The
Speaker 3: juniors come in and these tools would protect them from
Speaker 3: making mistakes. Your code coverage betters, your your rules are
Speaker 3: clearly delineated. Like weirdly, we might actually get more reliable
Speaker 3: software out of this for relatively low costs based on
Speaker 3: the way models behave when they're well constrained.
Speaker 4: The push for better models that are smaller and use
Speaker 4: less power means that we don't need to deploy and
Speaker 4: run as many data centers consume.
Speaker 3: As much energy. That stuff's going to be on my desk.
Speaker 3: Yeah right, I don't want to go out in the
Speaker 3: cloud at all.
Speaker 4: Yeah, I want my Raspberry Pie tower running.
Speaker 1: It as Hey, I think we got to wrap it up,
Speaker 1: but let's give one more round of applause for Jeff Fritz.
Speaker 2: And our friend walking out the door beIN a wolf.
Speaker 1: Congratulations, what do I hear?
Speaker 3: One? Two, three?
Speaker 5: All right, that's our rally cry at Philly.
Speaker 1: Awesome and thank you all for coming to dot net.
Speaker 2: Rocks dot net.
Speaker 1: Rocks is brought to you by Franklin's Net and produced
Speaker 1: by Pop Studios, a full service audio, video and post
Speaker 1: production facilities located physically in New London, Connecticut, and of
Speaker 1: course in the cloud online at pwop dot com.
Speaker 2: Visit our website at d O T N E t
Speaker 2: R O.
Speaker 1: C k S dot com for RSS feeds, downloads, mobile apps, comments,
Speaker 1: and access to the full archives going back to show
Speaker 1: number one, recorded in September two.
Speaker 3: Thousand and two.
Speaker 1: And make sure you check out our sponsors. They keep
Speaker 1: us in business. Now go write some code. See you
Speaker 1: next time. You got jud Middle Vans and