Intelligent Machines 892 transcript

Please be advised that this transcript is AI-generated and may not be word-for-word. Time codes refer to the approximate times in the ad-free version of the show.

Leo Laporte [00:00:00]:
It's time for Intelligent Machines. Jeff Jarvis is here. Paris Martineau is here. We've got a great guest. Martin Woodward is VP for Developer Relations at GitHub. AI has changed coding forever and nowhere more dramatically than on GitHub. He's getting ready for GitHub Universe. We'll talk to Martin and all the AI news, including new models from Google, pretty good model too, and Meta.

Leo Laporte [00:00:22]:
Coming up, Intelligent Machines.

TWiT.tv [00:00:26]:
Podcasts you love. From people you trust. This is TWiT.

Leo Laporte [00:00:36]:
This is Intelligent Machines with Geoff Jarvis and Paris Martineau, episode 892, recorded Wednesday, October 14th, 2026. Pants-based benchmarks. It's time for Intelligent Machines, the show where we cover the latest in AI and robotics and all the smart things all around us. Do I have to say the show where we cover superintelligence and robotics? I hope not.

Paris Martineau [00:01:00]:
You don't, actually.

Jeff Jarvis [00:01:00]:
No, no, we would leave. We would be gone if you did that.

Leo Laporte [00:01:04]:
That is Paris Martineau, who is an investigative journalist for the Consumer Reports brand in Yonkers, New York.

Paris Martineau [00:01:12]:
It's true. I may not be in Yonkers, but my job legally is there.

Leo Laporte [00:01:18]:
And that's a good thing when it comes to New York State taxes, apparently. It is not. It's a bad thing.

Paris Martineau [00:01:23]:
It is the opposite. It's the opposite. But that's fine.

Leo Laporte [00:01:26]:
You don't get a deduction, the Yonkers deduction?

Paris Martineau [00:01:29]:
No, I have to pay Yonkers tax. And it's very complicated.

Leo Laporte [00:01:33]:
Oh man! Oh man indeed.

Paris Martineau [00:01:35]:
But such are things.

Leo Laporte [00:01:37]:
There are trials and tribulations getting to live in the greatest city in the world.

Paris Martineau [00:01:41]:
It's true.

Leo Laporte [00:01:42]:
It's just part of it. Jeff Jarvis is also here. He is the Emeritus Professor of Journalistic Innovation at the Craig Newmark Graduate School. University of New York. Author of Hot Type. Get your Hot Type here. Brand new book about the magnificent machine that gave birth to mass media. You got 4 M's in that little subtitle.

Leo Laporte [00:02:07]:
I didn't even notice that before.

Jeff Jarvis [00:02:09]:
I love alliteration.

Leo Laporte [00:02:10]:
Me too. I'm a kind of sucker for it. Hey, let's say hello to our guest for this episode. I'm very excited to have Martin Woodward on. He's Vice President of Developer Relations at GitHub, joining us from Ireland.

Martin Woodward [00:02:25]:
Sunny Northern Ireland. Well, it's very dark Northern Ireland right now, but yeah.

Leo Laporte [00:02:29]:
Thank you for staying up late with us. I appreciate it. Uh, Martin is also— thank you, because he's right now probably at the busiest time of the year because in 2 weeks it's GitHub Universe.

Martin Woodward [00:02:41]:
Yeah.

Leo Laporte [00:02:41]:
And that's going to be in San Francisco at Fort Mason. Yeah.

Martin Woodward [00:02:44]:
Yeah. It's our big sort of developer get-together, the world's, the World's Fair of software, we like to call it. So it's, uh, Fort Mason in San Francisco.

Leo Laporte [00:02:52]:
Christina Warren, all excited. She's going to be there, of course, our host on MacBreak Weekly. And, uh, she's in developer relations in your department. She says you're her boss's boss. So. I said I put in a good word.

Martin Woodward [00:03:04]:
If you think anybody's the boss of Christina, you've clearly never met her.

Leo Laporte [00:03:08]:
Well, she always ferried me badges from GitHub Universe, which you very kindly provided. What does this year's badge look like?

Martin Woodward [00:03:15]:
Oh, do you want a sneak preview?

Leo Laporte [00:03:17]:
Can we show it or—

Martin Woodward [00:03:18]:
I don't want— World exclusive. No, no, no. Go on. You can show it. Let's put it up. We might as well. Oh, I see it. There you go.

Martin Woodward [00:03:24]:
Oh,

Leo Laporte [00:03:26]:
it's— first of all, first thing I noticed, it's in color this year.

Martin Woodward [00:03:29]:
It's in color now. Not just black and white. Yeah. We're moving on in technology. And we have a color screen as well.

Leo Laporte [00:03:36]:
It's got touch controls and—

Martin Woodward [00:03:38]:
Yeah. So yeah, it's got controls here. It's not a touchscreen, which everybody goes to, especially the kids nowadays, because they just assume every screen's touchable. It's, um, an RP2350 chip. So, you know, quite a high-powered microprocessor. Um, and you can even see, I don't even sort of see there, like Mona's face will move, you know, it's kind of, 'cause it's got an accelerometer in it, which is purely so we could flip the screen so that when you look at your badge yourself, it automatically flips the screen for you. And so you don't have to like squint or turn your head around or anything like that. And then when you're in that mode, you can actually use these capacitive controls as well.

Martin Woodward [00:04:15]:
So, uh, to sort of move around the screen like a little—

Paris Martineau [00:04:17]:
This is a normal thing for you guys, or did you just decide to pop off this year?

Martin Woodward [00:04:22]:
No, no, we've been doing it for— I think Leo's got a few of them actually. I have.

Paris Martineau [00:04:25]:
They're all screen-laden badges.

Leo Laporte [00:04:28]:
I was just looking for them. They're in my bookshelf, but I've misplaced it.

Martin Woodward [00:04:31]:
But it was like the E Ink, um, because it didn't need a battery around your neck and it saved us like failing out because you have to do all sorts of risk assessments when you stick 4,000 LiPo cells on people.

Leo Laporte [00:04:42]:
But that one has a battery because that's a Raspberry Pi in there.

Martin Woodward [00:04:46]:
Yeah. Uh, RP2350. So yeah, it's the, it's the, the top end of their microprocessors. It's not, it's not, um, so yeah, not a Pi itself, but

Leo Laporte [00:04:54]:
it's programmable, right? People can—

Martin Woodward [00:04:56]:
Yeah. Runs MicroPython and deliberately hackable. So, you know, obviously, because, um, you know, we've been doing Copilot forever, like kind of the first of these AI agents that kind of changed how we code. And we have an agents.md file and it just—

Leo Laporte [00:05:12]:
Wait a minute, you have on the storage, you have agents.md?

Martin Woodward [00:05:16]:
We have agents.md and we have it in an associated repo and it can mount as USB mass storage. And then you just point your agent at it and it knows all about MicroPython. It knows how to like deploy stuff to the MicroPython.

Leo Laporte [00:05:31]:
So what does the AgentMD say? Does it say, hey, this is a little card that has this kind of screen and that kind of stuff? Or?

Martin Woodward [00:05:38]:
Yeah, this is the pinout. This is where all the, 'cause it like things like the accelerometer and the capacitive sensors and things, they're not, you know, like in any standard board, they're not retail. And so we, the AgentMD tells it what pinouts they're on. All the models tend to know about MicroPython nowadays. So we just let it know it's MicroPython and what version it's running and things. But yeah, you just give the agent the appropriate skills and constraints and, you know, wonderful things happen. The more constraint, if you give it good constraints, then that's where you get art. You know what I mean? All, all art has constraints.

Jeff Jarvis [00:06:12]:
So during the universe, people are reprogramming it to show off their skills?

Martin Woodward [00:06:16]:
Yeah, yeah, exactly. And,

Leo Laporte [00:06:18]:
you know, during this, they come in with the modified badge. That's hysterical.

Martin Woodward [00:06:23]:
Yeah, no. Yeah. Well, we have a, we've got a surprise for the keynote as well. We've got a sort of interactive game to play and things. It's going to be good.

Leo Laporte [00:06:30]:
Oh, how fun. Oh, how fun. GitHub has really been the hub, if you will, of the AI revolution.

Martin Woodward [00:06:37]:
Yeah. I

Leo Laporte [00:06:38]:
was kind of shocked when I first started using Claude Code that, uh, it said, oh, let me put this on GitHub. And then the other thing it did, which shocked me, the first program I wrote was a little RSS reader, as one does, a 2E RSS reader. And, uh, not only did it put it on GitHub, and I already had a paid account, so that was fine. you know, gh auth login, and I'm off to the races. And, uh, and then it said, oh, and I, by the way, I made a Linux, Mac, and Windows version for you. I said, what? It, it did CI/CD using Microsoft— I mean, it did using the GitHub CI/CD capability, and I was blown away. I actually— this is one of the fun things about this, you learn stuff. So I'm learning— I've always used Git and GitHub, but I'm learning a lot about it that I didn't know from my agent, which is hysterical.

Leo Laporte [00:07:34]:
You got overwhelmed though, didn't you?

Martin Woodward [00:07:37]:
Yeah, it's been very, very busy with, um, you know, it's been sort of rapid acceleration on the platform.

Leo Laporte [00:07:44]:
Uh, called a hockey stick.

Martin Woodward [00:07:45]:
Yeah, well, there's hockey sticks and there's like brick walls of traffic. They're sort of— the graph just goes— it's not like gently up and to the right, it's, you know what I mean? So—

Leo Laporte [00:07:56]:
I kind of knew when it started happening because the downtime went up now, which I think you've got under control. It seems to be very reliable again.

Martin Woodward [00:08:03]:
It's got a lot better. Obviously, we're doing, you know, we continue to work like it's the first priority. If we're not up, that's, you know, that impacts us as much as it impacts everybody else. It's just not acceptable. So, um, yeah, we've been working, you know, uh, very, very hard, but The workload that agents have been putting on the platform is just, as I say, I'll grab some numbers in a sec, but it's just gone through the roof. You know, the commits are just going up all the time. The number of pull requests are going up all the time.

Leo Laporte [00:08:38]:
Right. We know that because people are saying, yeah, I get 2,000 PRs every day. And where are they going? They're going to GitHub.

Martin Woodward [00:08:48]:
Yeah, yeah, the, um, this— and, and it's really the GitHub's kind of become this sort of, um, so we— the agentic coding kind of started with Copilot and we kind of proved out that was a GitHub thing and we kind of proved out that models could code. And then, um, that was, you know, that was it for a while. And then, um, November of last year was obviously a big change. You know, we had not just, um, sort of Claude Sonnet but also the OpenAI, you know, latest round of models as well. well, those new round of models, the amount of time that agents can code without human interaction just increased exponentially. And therefore the amount of change that they can do has increased exponentially. And so we've seen that in the platform, but we've also seen the number of people that can code that are like you, Leo, you've always known how to code, but you also know how hard it is to code. And so you don't code that much, but now you can code again because you've got help.

Martin Woodward [00:09:50]:
Yes. But you know how to use it.

Leo Laporte [00:09:51]:
All the ideas that I have, I can now do. Yeah, I kind of liken it to, at least for me, because I'm not a 10x coder, I don't do it for a living, it's a hobby. And a hobby, by the way, I really enjoy. But it's— I compare it to like needlepoint compared to using a sewing machine. I mean, it was very slow and somewhat tedious. There was a lot of typing involved. And then You know, with Copilot, all of a sudden you're using VS Code, you press Tab and it says, oh, let me write that routine for you. Wow, that was pretty cool.

Leo Laporte [00:10:25]:
But now you don't even, you don't even open VS Code many times. You, you just, you talk to the thing and it writes the code for you. Now you, you're a, you started at Microsoft in DevOps, right?

Martin Woodward [00:10:39]:
Yeah. So I, um, I, well, so I, I was saying, uh, in the pre-show to, uh, to Paris and Jeff, I did a startup Um, that to be honest today probably wouldn't happen. Like the startup was kind of reverse engineering a protocol and nowadays you just point an LLM at that and, uh, you'd be done. Right.

Leo Laporte [00:10:55]:
They know it. Yeah.

Martin Woodward [00:10:56]:
Yeah. But the, um, we did this, we sold it to Microsoft and then I did a lot of the engineering systems inside of Microsoft to help them kind of modernize and get a bit more agile.

Leo Laporte [00:11:06]:
Oh, interesting. Oh, okay.

Martin Woodward [00:11:07]:
And then, um, and then I helped kind of, um, Microsoft get, uh, better at doing open source. I didn't, you know, we, there was a whole, uh, cancer thing that we try not to talk about. And then, uh, Mr.

Leo Laporte [00:11:19]:
Ballmer had a little thing about Linux we won't mention. But you were, so, but you were, you, now this is before Microsoft bought GitHub.

Martin Woodward [00:11:27]:
Yeah. Yeah.

Leo Laporte [00:11:28]:
But you created Microsoft's GitHub account.

Martin Woodward [00:11:32]:
I did. It ran on my Amex for a while. So I was, uh, what?

Leo Laporte [00:11:35]:
You were paying for Microsoft's GitHub account?

Martin Woodward [00:11:38]:
Yeah. Just for, it got very, very expensive. very, very quickly, so it quickly went on expenses. But yeah.

Leo Laporte [00:11:44]:
That's hysterical. So, um, did, was this their idea? No, you must have thought of this. So we should really have an account on—

Martin Woodward [00:11:50]:
Well, no, so I actually, um, we did have a, there was a Forge that existed called CodePlex. Uh, and I looked after that, which was Microsoft's kind of open source Forge.

Leo Laporte [00:12:00]:
Okay.

Martin Woodward [00:12:00]:
Um, but then it was actually, you know, it was with TypeScript, um, chatting with, um, and, uh, Anders Hejlsberg and a few other folks who were like, hey, we're gonna do, we're gonna do TypeScript. You know, and I was like, yeah, whatever, Anders, that's never going to take off. Who's going to use this JavaScript language from Microsoft? That's clearly never going to catch on. Obviously now it's the most popular language in the world. But the, um, and what are the— it's the most popular language for agents to use actually. It's the most—

Leo Laporte [00:12:31]:
I think OpenCLAW is written in TypeScript, isn't it? Pi is.

Martin Woodward [00:12:33]:
Yeah. Yeah. Yeah. Yeah. Most. Yeah, it is. So, um, Yeah. And so, um, we needed something and we knew like the TypeScript community wouldn't— we need a home.

Martin Woodward [00:12:44]:
So we actually created the account and, and TypeScript was the first project we put into the, the Microsoft GitHub org. And then it's just grown from there. And Microsoft went from being where it was at to being, you know, the, the top contributor to open source it is today. And then, um, because it did so much open source, it needed GitHub as part of its engineering systems. And so, you know, we talked to the GitHub team and, um, yeah, we, um, the GitHub team came over and And that's when I joined the GitHub team then after the acquisition.

Leo Laporte [00:13:14]:
So maybe you could take a little credit for the acquisition then.

Martin Woodward [00:13:17]:
Oh, I think, yeah, there was a few other people involved. Satya was definitely heavily involved in that one. I got the price right within half a billion dollars, which my wife laughs at me about. But yeah, exactly. Yeah, exactly. So

Jeff Jarvis [00:13:30]:
what's the cultural connection have been with the two? How many years has it been now?

Martin Woodward [00:13:36]:
2018 was when we did the

Jeff Jarvis [00:13:38]:
acquisition. So I remember there was a lot of concern about a mismatch.

Leo Laporte [00:13:41]:
between Microsoft and GitHub, especially because of the, you know, Linux.

Martin Woodward [00:13:45]:
I think there was in the community, but I think that was because a lot of people, I mean, you follow the, you follow people and you kind of do Windows Weekly and things. You, you kind of knew that, um, you know, the, the Microsoft had already moved on, but a lot, for a lot of people.

Leo Laporte [00:13:59]:
Oh yeah, I did. Absolutely. Yes.

Martin Woodward [00:14:00]:
Microsoft, that it was the old Microsoft in their head.

Leo Laporte [00:14:03]:
They had Linux on Azure. I mean, they were absolutely supporting Linux. Yeah. By the way, Richard Campbell says hi. He says—

Martin Woodward [00:14:10]:
Oh yeah, no, Richard Campbell's a good friend. Oh, I love Mark.

Leo Laporte [00:14:11]:
Yeah.

Martin Woodward [00:14:12]:
Uh-huh. Yeah. So, um, that whole kind of time, people were definitely nervous and, you know, Satya was very clear, judge us by our actions kind of thing. Um, and if you think about GitHub back then, GitHub was just, um, source control essentially. And then, you know, now you have GitHub Actions, which you, which you're able to use to do CI/CD and build everything for Windows and Mac and Linux and things. Wow.

Leo Laporte [00:14:39]:
Yeah.

Martin Woodward [00:14:39]:
We have security scanning because obviously the more code you create, the more likely you are to have security defects. So the more you need security tooling baked in.

Leo Laporte [00:14:50]:
I use that as well. Yeah.

Martin Woodward [00:14:52]:
Yeah. And that's really important because we have to give all that stuff we use, you know, we sell it to enterprises and people in the enterprise use these tools to build software, but we make sure we get those tools into the open source community as well so that people can use them for free, which just makes the world better for everybody because, you know, then everybody's software's a little bit more secure.

Leo Laporte [00:15:12]:
Yeah, for the longest time, I mean, I didn't really feel the need to pay for GitHub. Um, it was only once when I got past like about 20 repos, almost all of them vibe-coded, I thought, you know, I'm using this pretty heavily at this point. Yeah. Uh, I actually use it for backup. I use it for my blog, my Obsidian. I mean, I can go on and on and on and on. You can see how long I've been using it because that is the oldest picture of me that's still in existence. But GitHub is—

Martin Woodward [00:15:44]:
Do you want to try something, Leo? If you go to myfirstcommit.com, this was a website we did live on stage like a few universes ago, but it's probably still running. If you go to myfirstcommit.com, you can type your GitHub handle in and it'll tell you when you first Uh, did a change request to GitHub.

Leo Laporte [00:16:01]:
Oh, that's really interesting. 2017.

Martin Woodward [00:16:04]:
There you go. That's, that's very— so you were in the— like GitHub didn't go live until April 2018. So you were, you were in the beta. You were early.

Leo Laporte [00:16:16]:
Wow. I didn't know this.

Jeff Jarvis [00:16:18]:
So I ain't a year, man. Or grandpa.

Martin Woodward [00:16:21]:
You joined before I did. You're a hardcore hacker.

Leo Laporte [00:16:26]:
That's pretty funny. That's kind of wild. I'm— I almost don't believe it. But it must be true. GitHub would not lie. So as somebody who does DevOps, I mean, in the— when you were doing it, it was manual. There's a lot of coding, a lot of scripts, a lot of Bash shell. That business has changed quite a bit, hasn't it?

Martin Woodward [00:16:52]:
Yeah, we found— so DevOps, for folks that don't know, it's developer and operations. You know, it's basically moving with a lot more velocity, coding a lot faster, working faster by being closer to your customer and getting things out quicker. GitHub deploys hundreds of times a day, whereas in the old days, you used to deploy once every 2 years or something like that if you're building Windows operating systems. But to do DevOps, and that's how most modern cloud software is built, We found actually putting the kind of rules in place to do DevOps well for humans also means that you can do AI better. You can be faster accelerated with AI, because the same kind of safeguards you put in place to make sure humans can work at speed, tests and safety checks and all this stuff and security checks, that then allows your agents to— work with your humans a lot faster. So people who are good at DevOps tend to be good at using AI as well. We find across the industry. Yeah,

Leo Laporte [00:18:07]:
that kind of makes sense because you already have— I think you have to have an architecture mindset. What's helpful with AI is understanding at the higher level what the systems are and how they interact and how they go together. Of course, in the old days, you also had to write the Bash shell that would do all of that. Do people still think it's lazy to use AI for DevOps?

Martin Woodward [00:18:32]:
No, that changed. I think even, I don't know, like, I don't know, Paris and Jeff, if you've seen some of the people you talk to as well, it changed last November for even the most diehard of AI skeptics. Like, we had all the numbers that showed you that you were more productive, but people kind of like the old way. Um, but nowadays, you know, people realize, oh no, if I, if I work in partnership with a bunch of agents, I am faster. I can create more code more quickly.

Leo Laporte [00:19:01]:
Is it as good? Is it as reliable? Is it as secure?

Martin Woodward [00:19:05]:
If you do it right, if you put the right safeguards in, if you put slop in, you get slop out, you know? Right. If you just sort of say, do some stuff, then it'll, it's a random machine. So it's going to do some random stuff that day.

Paris Martineau [00:19:18]:
I mean, what separates, uh, the slop approach to doing it.

Leo Laporte [00:19:23]:
Yeah, how do you do it? What is your—

Martin Woodward [00:19:25]:
Yeah.

Leo Laporte [00:19:26]:
Because this is something that's fascinating to me. Everybody right now, everybody's kind of— I mean, I don't work in an org, so maybe if you're at a company that you have policies and procedures, but I'm kind of— I think that of all the people I talk to, we're all just kind of figuring it out on our own. And there are definitely ways to do— to get the AI to work better if you know what you're doing. And for a while people said loops and then they said graphs. What's your workflow look like? How do you do it?

Martin Woodward [00:19:53]:
Well, some of this actually feeds into the question that Paris asked about, like, what's the difference? You know what I mean? It's, um, what do humans provide? Like, what's the unique thing that we're good at? And, um, it's the creativity, the taste, and the judgment. And with the judgment, I mean, like, the computational thinking you talked about there, Leo, in terms of, you know, how do you pin systems together and all that sort of stuff. Mm-hmm. But our typical setup, for, um, when you're using agents. Um, we, we, we've got like 3 interfaces we use, um, and like all the AI, you know, sort of coding agents have this kind of thing. We have the command line, we have an app, um, that we use, it's the Copilot app, and then we have, um, the, the IDE. And, um, within that we have a set of skills. So that's not your skills, Jeff, that's like skills for the agent, um, that teach I

Jeff Jarvis [00:20:49]:
have none personally.

Leo Laporte [00:20:51]:
Actually, one of my skills is called plain English, and it's a very useful skill because I don't— because claudish is hard to understand these days, and I have to have— I have to say, talk to me like a Saint Bernard. So that's a skill you have, Jeff. That's, uh, it's how to put things complex—

Jeff Jarvis [00:21:06]:
It would be going out of style, but yeah, it's still there.

Martin Woodward [00:21:09]:
Yeah, well, one of the tricks is actually to use the agent to help you draft your skills. So I had my agent review— I've got like 20 years of blog posts, so I had my agent review my 20 years of blog posts and create a skill to sound like me. So, you know, use the word naughty, speak in an English accent, whatever, but don't use em dashes for a start. Don't, you know, speak with using British English, write in British English. And it kind of built a list of rules it observed from looking at my thinking, which it now applies when it's helping me draft, um, blog posts and things like that. The, the thing I find though, I actually use AI to help me implement. I try not to use AI to help me think. Does that make sense? So—

Leo Laporte [00:22:02]:
Yeah. So you, you do the thinking ahead of time?

Martin Woodward [00:22:06]:
Or I'll iterate with it, but I don't like— I actually don't like using AI to help me write, because I find the process of writing is the process of thinking.

Leo Laporte [00:22:16]:
Yes.

Martin Woodward [00:22:17]:
Again, Paris and Geoff are much better writers than I am. So maybe, but I like when I'm writing, maybe it's just the way I learn or whatever. But when I'm writing it down, that's how I kind of construct the arguments in my head. And that helps me do it. But if I have a set of bullet points and a set of research, I find the AI is very, very helpful, either fleshing that out or researching a topic for me and condensing that information down. And then I can do my thinking processes and like kind of get the— do the creativity, do the structure, you know, that sort of thing.

Leo Laporte [00:22:50]:
So is that how you hack a Furby?

Martin Woodward [00:22:52]:
Yeah, yeah, yeah.

Leo Laporte [00:22:54]:
That's the technique.

Martin Woodward [00:22:57]:
Yeah, that was actually, to be honest, that was pointing at it. Um, have you seen Wes Bos and a bunch of people? I don't know if you follow Wes online. Whereas it's got a project going out right now and where everybody's hacking their, I've got one here, their picture frames. Yep. Yep.

Leo Laporte [00:23:14]:
Because you can buy like $50 frames from Amazon.

Paris Martineau [00:23:17]:
What are you putting on your hacked picture frame and what are you putting on your hacked Furby?

Martin Woodward [00:23:21]:
Well, that's a great question. So the, my hacked Furby was actually, I had it, I have it do alerts for me. I don't have it here, but it, when, so when you're doing the coding agent, quite often you'll set off like parallel coding sessions. and you have them running in the background. I quite often before I go to bed, kick off the agent to go do some stuff. Or when I'm, you know, if I'm in a conference call, if I'm in a boring conference call or a Zoom call, then I'll have it doing some coding. Turns out I'm not allowed to have it on during an interview with you lot. But yeah, there we go.

Martin Woodward [00:23:55]:
And then Copilot would notify me. Why not? I do it. Yeah, thank you. Copilot would notify me via Furby when the agent was Complete, and it would just make like weird like noises.

Paris Martineau [00:24:07]:
See, Leo, this is what you've got to do. You've got to replace— Leo has, has installed his agents all throughout his house so that they can scream at him while he's walking around, and you should replace that with a sea of Furbies that make really haunting noises.

Leo Laporte [00:24:21]:
I did, by the way.

Paris Martineau [00:24:22]:
Lisa would love that.

Leo Laporte [00:24:23]:
I did order— it hasn't come yet— the MicroDuck that Hugging Face is operating. It has a flapping lip and it'll walk around the house, so maybe I will

Paris Martineau [00:24:31]:
Just a

Leo Laporte [00:24:31]:
lot of—

Paris Martineau [00:24:32]:
make it sound like Eartha Kitt or something.

Martin Woodward [00:24:34]:
And for last year's Universe, we actually, um, like, maybe took my Furby habit a little bit too far. Um, and I, I'll just go— I'll go grab one of them.

Leo Laporte [00:24:43]:
Go grab one.

Martin Woodward [00:24:45]:
Yeah. Um, this isn't the one.

Leo Laporte [00:24:47]:
This is the first time anybody's grabbed a Furby on this show. Oh, look, that's a blinged Furby. Yeah, that is very, very cool.

Paris Martineau [00:24:57]:
Wookiee Furby.

Leo Laporte [00:24:58]:
Oh, it's a Wookiee, of course.

Martin Woodward [00:24:59]:
Special edition. But the, um, the, the, the Furbies, yeah, I had a bit of a Furby habit as it turns out. And the, um, I, I, uh, for last year's Universe, for the finale of the keynote I was doing, I, um, I sort of had 66 hacked Furbies attached to a Raspberry Pi and a keyboard. And then I've got a friend of mine, um, Helen, who's one of the WordPress committers who also happens to be a concert pianist. And so got her to play there because it was the day before Halloween. So, you know, perfect time to deploy 66 Furbies.

Leo Laporte [00:25:33]:
Did they do the Furby chorus? What did they do?

Martin Woodward [00:25:36]:
Oh, it was, yeah, it was just, you know, dramatic organ music. It was great. But it was a keynote. So I got them to like give me smoke and lasers and all sorts. It was brilliant. And then it was Ridiculous. My 66 Furbies that I'd bought off eBay.

Leo Laporte [00:25:51]:
That's hysterical. So what are you excited about these days? I'm sure the, the Copilot Everything app is one thing. We'll, we'll let you get a plugin for that.

Martin Woodward [00:26:00]:
Yeah. So yeah, GitHub Copilot app obviously is the thing, but I think the, honestly, the thing I'm most excited about, um, is the, is being able to code again, is the creativity that's being unlocked by being able to code. And then also, the numbers of people that are coming in that actually are able to code again, are able to create some stuff and solve their problems. We're seeing new classes of application get built where people will build an ephemeral application, an application which just solves a problem for one person. Normally, you wouldn't ever bother coding that. Normally, you only code things when you do something twice. But, um, this allows you to create stuff and it just, just does unlocking a, a whole new wave of creativity. Obviously there's still a huge downside.

Martin Woodward [00:26:49]:
I'm sorry. But yeah, this is the best thing.

Jeff Jarvis [00:26:51]:
That, that idea of making something small and specialized. You don't have to make Microsoft Word. You don't have to make, uh, oh, I don't know, uh, the Adobe Suite. Yeah. You can make something specified to a need, which somebody has tried.

Leo Laporte [00:27:03]:
Huge opportunity. Yeah.

Paris Martineau [00:27:05]:
That Adobe Suite apparently didn't work. The copy that someone made,

Leo Laporte [00:27:08]:
it's not great. Benito tried it. He said he didn't.

Jeff Jarvis [00:27:10]:
Oh, really?

Paris Martineau [00:27:10]:
A bunch of buttons don't work.

Leo Laporte [00:27:12]:
Yeah, but it'll get better. I mean, it'll get better. Um, but I, yeah, I think that's the least interesting thing to do. I think one of the most interesting things to do is personal software that no one, that you, you would either, you would write yourself at great time and expense or no one would write, but suddenly you have little tools. I have little tools all over the place that I wouldn't have otherwise. In fact, that was, that's one of the kind of key things about DevOps and SysOps is if you do something more than a few times, you're supposed to write a script to automate it, right? But that's time-consuming. It's a lot of work. Now it's easy.

Martin Woodward [00:27:52]:
And that was always the promise, wasn't it? Behind like a computer on every desktop or a computer in every pocket kind of thing. It was always the promise that you could have stuff that was personal to you and you could work. a lot of times, like, writing code's hard and time-consuming. And, you know, increasingly phones and tablets turned into consumption devices rather than creation devices. I built my first iOS app now because, like, I was trying to build iOS apps and all that NS-whatever stuff was like, ugh. And I just got, you know, it was just hard.

Leo Laporte [00:28:24]:
Nobody wants to use— well, Swift's not so bad,

Martin Woodward [00:28:29]:
Swift.

Leo Laporte [00:28:30]:
Xcode's god awful.

Martin Woodward [00:28:32]:
Yeah, exactly. But I just have, like, I haven't, I had an app I needed. I wanted something. I wanted it to be a native iOS app. I just asked Copilot to write it for me. And

Leo Laporte [00:28:41]:
Claude can write Swift really well. Actually, all the agents can.

Martin Woodward [00:28:45]:
Yeah. Well, so the thing about the Copilot thing, it uses all the models. And I don't think people talk about this enough is because it's like you've got the Anthropic models, you know, the family. So you've got Sonnet, you've got Opus, you know, you have Fable, all that sort of thing. And then you have the Codex models. So from, you know, from OpenAI, so you have like GPT-5, 6, you've got Sol, you've got Astra, you've got Terra, you've got all these sort of wacky model names that are out there nowadays, like sort of, you know, 1950s cars with fins kind of

Leo Laporte [00:29:17]:
thing. It is,

Martin Woodward [00:29:18]:
isn't it? But what we've actually found that's really, really cool. And then you've got, you know, DeepSeek, you've got the OpenWeight models, you've got KIMI.

Leo Laporte [00:29:27]:
So you just have this long list of models you just pick from. That's nice.

Martin Woodward [00:29:31]:
Even better, you can use all of them. And this is why I really love doing this is one of the, because, because we are the only people that can like, because it's, everyone's trying to like make GitHub the center of the coding universe. We are the only ones that's got all the models kind of thing. Right. Um, in one harness, which is Copilot.

Leo Laporte [00:29:46]:
So you could have subagents be one model. Yeah. You could have, yeah. I, and I like to do that, but I kind of have to do it manually where I have have one model plan and a different model family code and a third model family review so that they, you know, have overlap with their, their gaps. But you can do that all within, inside of Copilot. That's really cool.

Martin Woodward [00:30:06]:
You just say rubber duck this for me. Oh, I love it. It'll go talk to another model. Let me grab a rubber duck while I'm here. There we go. So, I'm getting all excited.

Leo Laporte [00:30:13]:
So, Paris and Jeff probably don't know that. Oh, that's a big one. But one of the ways coders often work is they have a little rubber duck on their desk and they, and they use the rubber duck to bounce ideas off of to help them debug. They call it rubber ducking. And you have a problem—

Martin Woodward [00:30:27]:
Because if you say a problem out loud, a problem shared is a problem halved.

Leo Laporte [00:30:31]:
Yeah, even if it's just a dumb old rubber duck. Actually, that's one of the skills I've learned is ask the model, do you tell me what you understand from what I just told you? Because often it'll say, it'll say something nonsensical and you say, no, no, let me, let's go back and forth on that. There are skills to working with models. And I think a coder already somewhat has that mindset. But I think there are also a lot of coders, there's a lot of mathematicians right now in this same state, are in grief over the loss of their craft, you know, the thing that they loved doing. Do you notice that? Or,

Martin Woodward [00:31:09]:
um, so the, the math thing's interesting as well, you know. We've obviously, um, so yeah, we have a bunch of papers and a bunch of solutions been published. And I think the, the, of them, I've got friends who are mathematicians, the, the thing that kind of irks them more is less that it's been solved, but it's like that people don't care that it's been solved, you know, is that, that's what upsets them. But it's, there's always this issue of like, will AI, you know, it's the jobs issue, you know, is AI gonna take my jobs if— Right.

Leo Laporte [00:31:39]:
That's maybe the bigger concern, isn't it?

Martin Woodward [00:31:41]:
Yeah, exactly. And for coding in particular, whenever we've had A, we, we, we're just not seeing it in the data. First of all, we see the number of, you know, developers going up and up and up, not going down, which is, you know, because more people can code now. But the, um, the, the more, every time we have made coding easier, you know, when we went from that, uh, what's that, an Altair 8800 behind you when you went, or is it— That's right. I can't tell. Yeah. Altair. Yeah.

Martin Woodward [00:32:11]:
When you went from that to, uh, to the mainframe, when you went from the mainframe to client-server, we always made coding easier. And the people who were in the old way of working were always kind of, oh, this, you know, they don't understand how to write assembly language. They're like Steve Gibson, you know what I mean? You don't know how to write assembly language, then you're not a real coder.

Leo Laporte [00:32:30]:
Yeah.

Martin Woodward [00:32:31]:
Even Steve uses different, like, higher level languages. That's not fair.

Leo Laporte [00:32:33]:
He bought a Spark. He shocked me. He said, I'm gonna have it do the UI because I don't, I hate UI. I said, that's, there you go.

Martin Woodward [00:32:41]:
See? Well, yeah, most, most people hate writing assembly, so we get it to do that instead. But yeah.

Leo Laporte [00:32:47]:
Well, I think it's important to keep the part of it that you really love.

Martin Woodward [00:32:51]:
Yeah.

Leo Laporte [00:32:51]:
But I think it's also important if this is your profession, and I have friends, I'm sad to say, who are having a hard time adjusting to this new world, but you already have the skills that you could be very good at this. You just— what is the advice you give to coders, to the pros who are saying, I can't, I don't, I can't live in this new world?

Martin Woodward [00:33:12]:
Um, it's, you know, it's the way the world's going. It's so— it's go learn it. Our jobs as developers is to do is to be lazy and to solve problems in the easiest way possible. And I see less— like, there was up until, as I say, November of last year, there was definitely a lot more of that reticence. And now it's kind of people miss the craft. People miss that. What they also miss is the pace has just changed. Oh, yeah.

Martin Woodward [00:33:43]:
Coding now is just so— you're coding at 10 agents at a time. You constantly feel like you're behind. A new model out every 12 minutes and you sort of, you know, and so people miss kind of some of that. And we had that same problem when we went from— so, uh, Jeff Paris, when we used to code, we used to code in like a waterfall way. We used to like write down specifications and write down, you know, stuff. And then we would like, is this right? And then we would ship it and it would take months and months and months. And then when we did DevOps and Agile, we iterate a lot more quickly. So we build something, show it to you as a consumer of software and say, hey, is this better or worse? Did we make this better or worse yet? And we make lots and lots of small changes.

Martin Woodward [00:34:26]:
And there was that back in the kind of 2000s, uh, there was this pace change and people noticed it then and we're noticing it now. It's, it's, it's definitely, you know, a lot faster. I, I got the stats for you as well here. There's, um, the, the number of merged pull requests. Yeah. Um, like in August, It was, uh, 100, so across GitHub, uh, 130 million pull requests merged per month. And if we look a year earlier, it was around about 40 million. You know what I mean? So that's— Tripled.

Martin Woodward [00:35:00]:
It's more than tripled. Yeah. And, and that, and, and a pull request is like a change, is a way you review the code, you know? And, um, and like 50% of those, um, pull requests now are kind of being agent-assisted. So it's, uh, an agent's coming along and helping a human um, in creating that change. And that's how much software's changed in 12 months. So yeah, absolutely.

Leo Laporte [00:35:20]:
I mean, you don't have a tripling without something like that. That's such a big difference. And, you know, when I, you know, I'm always looking at new models, as you mentioned, and it always is a link to a GitHub page. I don't— I know the model resides on Hugging Face, but it always starts at GitHub. That's where the recipes are. That's where the, that's where the information is. That's where you've That's where you point your agent. You say, go to GitHub, and they know GitHub, they understand it, and go from there.

Leo Laporte [00:35:48]:
You have become really what Twitter was 20 years ago. GitHub is—

Martin Woodward [00:35:55]:
Let's hope we stay like Twitter was 20 years ago.

Leo Laporte [00:35:59]:
Yes, please. Really, we used to call it the dial tone of the internet. You are where people go to get connected.

Jeff Jarvis [00:36:08]:
Martin, can I ask a quick question on that? Yeah. Thanks to all of this ability for anybody can code, how many people are coming to GitHub who previously didn't code? Are you seeing a growth in that kind of population?

Martin Woodward [00:36:22]:
Yeah, yeah, the number of first-time coders is rising massively. The number of new users every single month is just exponentially more. So yes, one of the things that comes with downsides though, Jeff, so, If you're a maintainer, like I talk to maintainers of open source software, open source software is how we share stuff around the world. If you're a maintainer of a popular package, it's got a lot easier for somebody to contribute code to you than it was before. And if you're starting out in coding, you might think, oh, I'm going to give a gift to this project I like. I'm going to give it some code. code, but you don't yet have the judgment as to is this good code or not. And so, they can kind of— one of the problems we've seen is kind of open source maintainers getting inundated with low-quality code contributions.

Martin Woodward [00:37:20]:
We started calling them spam, but it's not really spam because oftentimes it's well-intentioned, it's just not good, it's just low quality. And so, we've had to— I'm a huge— I've been a passionate open source advocate for decades. This is like Cut me open, I'll bleed open source sort of thing. But one of the things we had to add to GitHub recently was the ability for a maintainer to switch off what's called a pull request. So that's how people contribute code to you, via a pull request. We've had to give them the ability to turn that off, to turn it off temporarily, to turn that off to people who haven't coded before, to limit how many times somebody can suggest a change to you. you, um, before you, before you, you accept it, you know what I mean? So they can't say, give you 10 changes, um, until you've accepted the first one, those sorts of things. And we've had to, it's kind of limited how people inter— it's changed how people interact, but we just have had to do it to kind of help maintainers deal with the noise now.

Martin Woodward [00:38:22]:
So—

Leo Laporte [00:38:23]:
I notice Claude is always asking, oh, do you wanna submit a PR? And I always say no. I say, no, I'm sure they know about this problem. I'm not gonna bug them. The good news is, we're also getting better tools for reviewing PRs.

Martin Woodward [00:38:38]:
Yeah. On the other side. You know, if you're getting 130 million pull requests in a month, you need an agent to help you. But that's why DevOps helps too. Because what we did with DevOps is we did tests, we did coding checks, we did security checks and things, all before you accept the change. And so now you automate that. You have all that stuff happen procedurally using GitHub Actions, which is the thing that Leo talked about. But then you also have your AI agent come in and do code reviews.

Martin Woodward [00:39:07]:
And again, you know, it comes in and sort of tells you, oh, this bit's changed. These, they've introduced some new dependencies sneakily. So even though they telling you the code does this, actually what it does is steal crypto wallet from you. So like, you know, it's sort of, you can have the agent do those checks for you. And it's always vigilant, always on, always watching. So that's been, you know, using AI to kind of fight some of the downsides of the sort of some of the slop is definitely how to win, you know.

Leo Laporte [00:39:41]:
Well, I mean, we are in a time of disruption, and disruption always does 2 things. It disrupts, it takes the incumbents and unsettles them. And it makes those of us who are used to the old way a little uncomfortable. But at the same time, it also presents major opportunity. And I always want to focus on that. I think what we're seeing is a huge opportunity. And the right way to approach it is not to be afraid of it, but to embrace it and see how it can help you in your life. And I think that that's very clear.

Leo Laporte [00:40:15]:
And GitHub is a great place and Copilot is a great place to start. You really— Copilot really started this all. I mean, that was the first AI coding tool I used and most people used.

Martin Woodward [00:40:26]:
I was user number 6 of Copilot. Isn't that great?

Jeff Jarvis [00:40:30]:
Wow.

Martin Woodward [00:40:30]:
It's 2021. Again, it was one of those, it's one of those TypeScript moments. Um, there's, there's times where I'm spectacularly wrong, and that was one of them where the team were, oh yeah, we've got AI writing code. I was like, yeah, this is gonna be terrible. And then I started using it and I was like, oh no, this has completely changed how I write code. Um, I'm never going going back.

Leo Laporte [00:40:49]:
Well, and it benefited because it had all these great repositories it could read on GitHub. So there's—

Martin Woodward [00:40:54]:
Yeah. And early on, the models weren't as good. So when we started, we did what's called code completion. So basically, like, it could only write 4 or 5— yeah, exactly. You could only write 4 or 5 lines before it would go wrong. And so we took advantage of that. And rather than trying to make it write a whole bit of code, we just helped it complete the line of the code that you were doing. And that was already kind of like— It was magical.

Martin Woodward [00:41:22]:
Yeah, exactly.

Leo Laporte [00:41:23]:
Oh, you're writing a login script? Let me just write that little routine for you.

Martin Woodward [00:41:28]:
And it was like, it still, it still is magical even today when I'm using, you know, I don't, I tend to now use the IDE for kind of like the, the smaller kind of detail. You know, if you'd like, when you're doing an oil painting, you paint the big areas first, and then you come in and you do the fine areas. I'll use the editor, the IDE we call them, as a— doing the fine areas. And I'll use my agent to do the broad strokes, you know, and kind of get me most of the way there.

Leo Laporte [00:41:55]:
I'm ashamed to admit I haven't opened an IDE in a long time.

Martin Woodward [00:41:58]:
Really?

Leo Laporte [00:41:59]:
I don't look at the code anymore. I mean, why? You know?

Martin Woodward [00:42:07]:
As long as you're testing what it does. I have lots of testing. Yes. Exactly.

Leo Laporte [00:42:11]:
Yes. My coding team is a soccer team. They're called the Red and Greens. And because all of them, I want to make sure they use tests, red, green, blue tests all the way, baby. And they even have a little fight song. I won't bother you with it. Martin, it's been a real pleasure meeting you after all this time. I feel like we know each other.

Leo Laporte [00:42:31]:
We have many mutual friends. And of course, you're legendary at GitHub. Thank you for spending a little time with us. I know it must be a crazy time for you. half an hour to talk to us in the middle of preparing for what will be a huge GitHub Universe in 2 weeks in San Francisco at Fort Mason. How many people do you expect at that?

Martin Woodward [00:42:51]:
About 4,000. So it's, it's not a huge venue, but it's, um, it's intimate. And, uh, yeah.

Leo Laporte [00:42:56]:
Yeah, but it's all people like you, like us.

Martin Woodward [00:42:59]:
Well, that's the best bit. It's full of, yeah, full of nerds. And so you just go around and it's just great fun and everyone's Everyone's learning and it's just a good atmosphere as well. Because Fort Mason's, it's not like Moscone. Moscone is a great conference venue, but Fort Mason's a completely different vibe. You're outside, you just get out and talk to everybody. So,

Leo Laporte [00:43:20]:
is it too late to go? Is it, are you all full?

Martin Woodward [00:43:24]:
We've got some tickets left, I think now. It's getting pretty full, but yeah, we've got a few tickets left. And you get your conference badge if you do that. That's the reason for going. So, uh, no, it's worth it. But yeah, and you get to come along, you get to talk some sessions, you get to see— the reason I have lots of plushie dolls with me is because I'm planning out what my keynote's gonna be. I did Furbies last year, so I'm sure I could do something with plushies or something this year. We'll

Leo Laporte [00:43:48]:
see. And you have your little GitHub. What is that creature, by the way? I've always wondered.

Martin Woodward [00:43:52]:
This, the cat. This is Mona. So, um, the original artwork, the original logo for GitHub was, uh, written by an amazing artist, drawn by an amazing artist called Simon Oxley. He actually did the original Twitter bird logo and everything as well. He just did them as stock art that he put on some website, and then the founders of Twitter and the founders of GitHub both bought the stock art and bought exclusive use of it. Good. And then over time, it's become the GitHub logo, the Octocat. But Mona is Then, sorry, Mona, I'll keep doing it wrong way, is the, the, um, the, the, the mascot that represents the community on GitHub.

Martin Woodward [00:44:33]:
Um, Mona Lisa Octocat. And that was— We love her.

Leo Laporte [00:44:36]:
She's an octocat because she's both a cat and an octopus.

Martin Woodward [00:44:38]:
Exactly.

Leo Laporte [00:44:39]:
She's both.

Martin Woodward [00:44:40]:
And then that's Copilot next to her. Yeah. Anyway, thank you.

Leo Laporte [00:44:43]:
Oh, is that Copilot? Oh, of course. Yeah. The goggles. Of course. I should have known.

Martin Woodward [00:44:46]:
There we go.

Leo Laporte [00:44:47]:
Should have known. Have a great conference. Fort Mason is beautiful. You're on the San Francisco Bay. It's gonna be beautiful weather. It's the best time of year.

Jeff Jarvis [00:44:55]:
It'll be foggy. It'll be fun because it always is.

Leo Laporte [00:44:57]:
Only in the morning. It'll clear up in the afternoon. And October is a great time to go to San Francisco. So if you haven't— if you haven't yet got your tickets, better rush. October 28th and 29th, GitHub Universe, the hub of the world for those few days. Thank you, Martin. Really appreciate it.

Martin Woodward [00:45:16]:
Thank you.

Leo Laporte [00:45:17]:
Hello, Jeff in Paris. Did you do your Montclair thing, Jeff? Your event yet? Or is that—

Jeff Jarvis [00:45:24]:
No, my event was in Haverhill, Massachusetts.

Leo Laporte [00:45:26]:
I'm sorry, Haverhill.

Jeff Jarvis [00:45:27]:
Yes, I did the— yeah, I did a Saturday at the Historic New England, a huge, gigantic, like 12-foot screen with a big Linotype on it. It was so much fun. And then I went up the hill to the Computer History Museum. I'm sorry, wrong place, to the Museum of Printing. Similar but different. Yes, it is. And helped demonstrate with a wonderful volunteer named Chris Bradford the actual linotype, answering, fielding question after question after question. People were just fascinated by this machine.

Paris Martineau [00:45:56]:
Ah, that's fantastic.

Jeff Jarvis [00:45:58]:
Yeah, it's great.

Leo Laporte [00:45:59]:
Paris, I know you were busy too because you went— and I wore this shirt in honor— to the corn maze.

Jeff Jarvis [00:46:05]:
It's that time of year.

Paris Martineau [00:46:06]:
It's the time of year to go to the Queens County Farm Museum and the Queens County Farm Museum's amazing maize maze, which for some reason every year is is presented by Con Ed, Con Edison, which is— That's weird. You know, uh, this year it was America-themed. Me and some friends wandered through. We went to a pumpkin patch. I had to take 2 work calls and send an email to Taylor Farms for a story that I published on Friday.

Leo Laporte [00:46:31]:
Are they still in the news?

Paris Martineau [00:46:34]:
Yes. I, um, the FDA, uh, had actually announced that week prior that they closed their investigation finally into what was the largest cyclospora outbreak in US history. And that as part of kind of their decision to close it, they'd also found positive samples of cyclospora in 2 places where the lettuce Heard Round the World was grown or processed. Hmm. So I kind of did a big postmortem on it, but it did require some Some calls being made after hours over the weekend, which is very funny.

Leo Laporte [00:47:09]:
I'm calling you from a corn maze.

Paris Martineau [00:47:11]:
It was very funny because at one point I was on the phone with an editor and then someone very high up in my company, and they were like, Paris Zitterer, like, where are you? What's that noise? It's like, that's not me. And I'm like, I'm in a corn maze and pumpkin patch. I'm surrounded by screaming children, but please continue.

Jeff Jarvis [00:47:31]:
I'm actually— I'm lost. Can you— someone says something? Yes, I

Paris Martineau [00:47:34]:
was like, please, I've been waving this flag for hours and they haven't come and gotten me.

Leo Laporte [00:47:38]:
Did you find your way out of the corn maze?

Paris Martineau [00:47:40]:
I did, yeah, we found our way out of the corn maze. We only had to commit corn maze crimes once, and that was not because we were lost, but because there was a large gaggle of strollers. And rather than barrel through the strollers and harm infants, I decided to cut through a little bit of corn.

Leo Laporte [00:47:58]:
Who brings infants in strollers into corn mazes after all? A lot of people. Really?

Paris Martineau [00:48:02]:
So many. You

Leo Laporte [00:48:04]:
gotta train them young.

Paris Martineau [00:48:05]:
So thank you for respecting my culture with your shirt. If you're listening to, um, this show rather than watching, he's wearing a corn shirt.

Leo Laporte [00:48:13]:
I'm wearing a shirt made out of corn. It's corny as all get-out.

Jeff Jarvis [00:48:19]:
Did you do a, a ride? Did you corn maze and pumpkin patch? Anything else? Uh, get some out? I

Leo Laporte [00:48:25]:
think there was a wagon, wasn't there? A hay wagon? I think I saw that in your story.

Paris Martineau [00:48:29]:
Not this year though, last year. This, um, this year's visit to the Queens County Farm Museum was a bit, uh, less planned than usual because originally we had plans to rent a Zipcar and go to some fall excursions kind of within a couple of hour drive of the long weekend. But we got there and the person never returned the Zipcar, so we pulled an audible and took an Uber to the Queens County Farm Museum. The person?

Leo Laporte [00:48:55]:
Did they steal it?

Paris Martineau [00:48:57]:
No, sometimes people just don't return a Zipcar for some hours.

Leo Laporte [00:49:01]:
Please, that's so wooed. It was wooed. Um, and did the Liberty win?

Paris Martineau [00:49:09]:
No.

Leo Laporte [00:49:10]:
Oh no.

Jeff Jarvis [00:49:11]:
I watched, I watched the game with Paris, as it turned out, virtually.

Leo Laporte [00:49:15]:
Did you go to the last game yesterday?

Paris Martineau [00:49:17]:
Did you watch the last game as well?

Jeff Jarvis [00:49:19]:
No, not the last game, the last week's game.

Paris Martineau [00:49:21]:
Oh yeah, so last week's game, which these guys lovingly allowed me to cut off a little early to go over to my local queer women's sports bar. I was there 30 minutes before the game started. I was like the last person physically allowed in the bar. Josh left you out. I got the last green room spot. And it was a nail-biter that Jeff and I both watched where the entire game was within like We were 1 point up or 1 point down the entire game. It went into overtime in like the last second. And then we lost that game.

Paris Martineau [00:49:56]:
And then— That hurt. Ended up losing the final, the 3rd of 3. So we got knocked out at the semifinals. But honestly, it was a great showing for the Liberty. And I'm looking forward to watching the finals.

Benito Gonzalez [00:50:11]:
Go Valkyries.

Paris Martineau [00:50:12]:
I have no strong opinion.

Benito Gonzalez [00:50:15]:
San Francisco's team made the finals this year, their second year. They're an expansion team.

Paris Martineau [00:50:20]:
Yes, that is actually— I mean, that is where I think I'm leaning. It is their second year as a team in existence and they are in the finals.

Leo Laporte [00:50:27]:
Well, none of the teams are that old, right? I mean, the WNBA is— I

Paris Martineau [00:50:30]:
mean, they're decades old.

Leo Laporte [00:50:32]:
Really?

Paris Martineau [00:50:33]:
Yeah.

Leo Laporte [00:50:33]:
Oh, I didn't realize that. I thought it was relatively new. They finally let ladies play basketball.

Jeff Jarvis [00:50:38]:
30 years old, Leo. 30 years old.

Paris Martineau [00:50:42]:
30 years.

Jeff Jarvis [00:50:42]:
You just haven't paid attention.

Leo Laporte [00:50:43]:
When did you stop wearing high heels?

Paris Martineau [00:50:46]:
I think right around the time someone was kicking you in a place.

Leo Laporte [00:50:51]:
Actually, Jeff knows I'm referring to a terrible ad from many years ago where it was an ad for high heels, for shoes, pumps, and, and they had women playing basketball in them to show how comfortable they were. But this is the era I grew up in. Jeff and I grew up in— yeah, well, it did make you taller. There was that.

Jeff Jarvis [00:51:13]:
That's, that's, that makes it legit, you know.

Leo Laporte [00:51:15]:
All right, let's fast forward to the 21st century. Google released Argon to the public yesterday.

Jeff Jarvis [00:51:19]:
What do you think? What do you think? What do you think?

Leo Laporte [00:51:21]:
Love it. Oh, uh, in my own benchmark testing, it scores, uh, pretty darn well. I don't know if it's Opus 5.5 quality, but the reason it's surprising is Google has not been in the, you know, considered a frontier company for some time.

Jeff Jarvis [00:51:37]:
They were. It's not that long ago that they had met, but they delayed and delayed, I think, to make it good.

Leo Laporte [00:51:42]:
Yeah, and they did. And so it's pretty good. I have an Anti-Gravity subscription with Google and I've been using it. I call it— because it's Anti-Gravity, I call it Agnes, and it has the voice of Marisa Tomei from My Cousin Vinny. So it's got a nice New York accent. It's great. And I put it to work too. So I did some benchmarking.

Leo Laporte [00:52:06]:
It does well. One of the— we've, we actually updated the benchmarks because these models are getting so good. I had to make them harder, believe it or not. And I've been benchmarking everything to kind of get a baseline. So Argon did as well as the best models I've tried. It did. It actually beat ChatGPT Sol 6.1. But we have 2 parts to it.

Leo Laporte [00:52:28]:
The other part I've started doing is a security audit. Yeah. to make sure that the model doesn't fall for prompt injection or accidentally exfiltrate keys, things like that. And Argon does very, very well on that. It's the highest, I think it's the highest model we've tested. Some have said, I've seen people say, oh, it has no design taste or it's not good at Blender or whatever. People do weird, if you look at X, people are doing weird benchmarks. They make it—

Jeff Jarvis [00:52:55]:
It's supposedly better at not doing no-nos. Did you try any?

Leo Laporte [00:52:59]:
That's what the security test—

Jeff Jarvis [00:53:00]:
Bioweapons on it or anything?

Leo Laporte [00:53:01]:
Well, I don't know about bioweapons.

Jeff Jarvis [00:53:05]:
Can Argon fix Paris's camera utility?

Leo Laporte [00:53:07]:
We need to write a new webcam utility for poor Paris, who's now disappeared behind her.

Paris Martineau [00:53:12]:
I know, I've got to, I've got to sic the agents on it. It's worked so well for so many years. Agnes could help.

Leo Laporte [00:53:20]:
I bought— I've been using Agnes as my IT IT person for some time under Gemini 3.8, which— Flash, which was very, very good. It seemed to know everything. It's fixed a lot of the little— you know, you have these paper cuts, little things that you, when you use a computer and you just kind of put up with, and it's been fixing all my paper cuts. So one of the things I thought, well, let's try Argon on this one because this is kind of challenging. Uh, every day before the show, I have to go to Slack And they give me the ads that I need to read for that day's show. I open them up in a browser, copy it, and paste it into the prompter software that I use, which is the Elgato Camera Hub software. And I thought, you know what would be great is if every day before the show, uh, the AI could just go out and do that for me. So I, I was very tentative.

Leo Laporte [00:54:14]:
I said, hey, Agnes, I got a question. There's something I'd like you to do, but I, I want to make sure. Can you see my Slack messages? Can you, can you, uh, first of all, can you log into the machine that I do this on, which is an M1 Mac Mini over in the corner here? Can you see my Slack messages? And if you see them, can you copy what's in them into the Elgato Camera Hub? And, and she thought, she's, yeah, I could do that. I said, really? Yeah. I said, well, can I? Because I have, by the way, 75 commercials I have to read before I go on vacation on Tuesday.

Paris Martineau [00:54:50]:
Oh boy.

Leo Laporte [00:54:50]:
Because unfortunately, the new— when we changed the way we do ads and the new people selling the ads, we agreed that I would read all the ads even when I'm not here. So Micah can't read them, uh, the co-hosts for next week can't read them. I have to read them. So I have 75 ads I have to record.

Jeff Jarvis [00:55:11]:
I mean, good news, bad news. Good news, you got 75 ads.

Leo Laporte [00:55:13]:
Yeah, that's the good news.

Jeff Jarvis [00:55:15]:
Yeah,

Leo Laporte [00:55:15]:
bad news is I'm not complaining, but it's, uh, but I— it's kind of just putting them in the teleprompter is a pain. So Agnes just did it. And I said, I said, wow, that's— you've just— that was a paper cut that I've been doing for 2 years. Thank you. And I said, now this is important. And I would say, uh, this is an interesting and important thing for you to ask when your AI makes little routines like this. I said, Is this going to use an AI model, an LLM, or is it just code? And Agnes said, no, no, it's Python code. You just run it.

Leo Laporte [00:55:47]:
You don't even need me to do it. It's just a Python. So it wrote a program that does it automatically.

Jeff Jarvis [00:55:55]:
And you don't— do you have to run it? Do you have to do something?

Leo Laporte [00:55:57]:
Well, no, I said, I know Agnes is— I said, Agnes, every day before the show, make sure you get all the commercials. So Agnes is going to handle that from now on.

Paris Martineau [00:56:06]:
That's great.

Jeff Jarvis [00:56:07]:
That's great.

Leo Laporte [00:56:07]:
So that's a good example. I think that's— it's, you know, there's all these benchmarks which I no longer trust. In fact, we're going to talk about, uh, the new Muse model and its benchmarks in a second and why I don't trust them. But I— part of the reason is these models to some degree are trained around the benchmarks. So I thought, well, I should have benchmarks that reflect my own use. And I think it's important— you're going to see people say this and that about Argon. Maybe it's good for coding. Maybe Opus 5.5 is better.

Leo Laporte [00:56:36]:
I suspect Opus 5.5 is better. But is it good at what you want it to do? And this was a good example of a kind of multi-layered problem. It had to go from log into another machine. It had to log into Slack. It had to do a bunch of stuff. And it did it quite effectively, responsibly. And then I said, okay, make a cron job so that right before every show, 10:30 every morning, you get the latest ads and put them in the teleprompter. And, uh, knock on wood, for Micah, I won't have to worry about that anymore.

Leo Laporte [00:57:09]:
There are some—

Jeff Jarvis [00:57:09]:
So, Leo, which, which model is writing the guest, um, memos? Oh

Leo Laporte [00:57:14]:
yeah, we don't like the guest memos.

Paris Martineau [00:57:15]:
Which one? Tell me which one did it. The strange format. Tell us which one we have to imagine.

Jeff Jarvis [00:57:19]:
Let me, let me do a dramatic reading. Co-host steering. Jeff will pull toward platform power, monopoly regulation of a Microsoft-owned choke point. Good ones. This is fun. Then yank it back to the community story. Paris will hunt the sharp number or skeptic angle. Is AI code actually good? Feed her the Octoverse research stats deliberately.

Jeff Jarvis [00:57:44]:
Keep Martin telling maintainer-on-the-ground stories. That's where he's best. Don't let it become a Copilot product demo. Don't dwell on GitHub the company versus GitHub Lab, etc. Don't accept AI makes developers 55% faster uncritically. Who made, who made this one boss? Out of democracy. What the hell are they doing?

Leo Laporte [00:58:04]:
So this is one of the things that does happen with AI. So this, just to answer your question, is Hermes, the Hermes agent running GLM 5.3 Flash, which is my main local model on the Sparks. So, uh, the, and the original skill we used was Claude Code that Anthony made. way back when. And I agree, it was much better.

Jeff Jarvis [00:58:24]:
It was great. It was wonderful.

Leo Laporte [00:58:26]:
Yeah. And then I eventually, I had, I got it from Anthony. I used Claude Code with it. And then at some point I said to Hermes, I said, absorb this skill and you do it because I want, you know, I want to do everything as much as I can locally. And this is an easy thing. But Hermes, the problem with Hermes, it has a lot of memory. It knows what Intelligent Machines is. It thinks it knows what the show's about.

Leo Laporte [00:58:49]:
It thinks it knows who Paris is. It thinks it knows who Jeff is. Not because I told it, but because this is one of the things an agent, agentic harness does. It's what Muse does, Grokbot does, what Hermes does, what OpenCLAW does, is it accumulates facts, memories, and it builds up a dossier. And it has—

Jeff Jarvis [00:59:08]:
Well, it gets a certain ego that it thinks that it knows better.

Leo Laporte [00:59:11]:
Well, at some point it decided that— I didn't tell it to do this, but it decided that I would want it to do this. And I think it's kind of based On— I think it thinks it's producing the show.

Jeff Jarvis [00:59:22]:
Yeah. Oh yeah, it thinks it's the boss.

Leo Laporte [00:59:24]:
So, but I can, I can rein it in. I mean, I will rein it in.

Paris Martineau [00:59:28]:
I also love the fact that it's been doing this for how many, like, years now, and it still thinks that the inter— guest interview portion is the full 2 to 3 hours of the podcast.

Leo Laporte [00:59:40]:
Well, I never told it that. Yeah. And so it's, it doesn't know. Uh, so yeah, it says 2 hours.

Paris Martineau [00:59:46]:
Yeah, it was almost like it was planned out for us to spend 2 hours interviewing our guests.

Leo Laporte [00:59:52]:
So it guessed that. I think it figured out how long the show was. You know, it has a lot of data input because it's also got writing the TwitAds thing. So it knows how to connect to our backend of workflow. So it knows how long the show is. It knows a lot of stuff that I haven't told it. And it's apparently, and this is what AI's doing.

Jeff Jarvis [01:00:12]:
This is what it knew to do, Leo.

Leo Laporte [01:00:13]:
It does. But this is what AIs do. They're trying to be helpful, right? So it's kind of absent. Now, if you give it a direct instruction, which I will, I'll say, no, no, no, I don't want you to do any of that. Please. All we want is a briefing about the guest, all the information. But you— this has been a gradual process. I don't know if you've noticed.

Jeff Jarvis [01:00:32]:
Oh yeah. No, it's evolved into a—

Leo Laporte [01:00:35]:
It's evolved.

Jeff Jarvis [01:00:35]:
It didn't happen overnight.

Leo Laporte [01:00:37]:
Yeah. For a while it was giving us 10 questions. I don't know if it's doing that still.

Paris Martineau [01:00:42]:
For a while it was giving us 3,000 words. Yeah,

Leo Laporte [01:00:45]:
it was a lot longer before. And this may be my fault. I might have said, don't— I don't want such a long thing. I don't know. I

Paris Martineau [01:00:51]:
mean, I think that this is, I guess, this short version is preferable to the 3,000 to 4,000 word one, because which is a little bit of overkill for every single guest. But it's a bit prescriptive. Yeah.

Leo Laporte [01:01:04]:
No, I agree 100%. And I—

Jeff Jarvis [01:01:06]:
It's very funny how it became— you just have to give one more example. It's not a democracy.

Leo Laporte [01:01:12]:
I should say that. What I do in their souls—

Paris Martineau [01:01:16]:
I was going to say, my Claude project for intelligent machines understands that Leo's opinion of the show is it's not a democracy.

Leo Laporte [01:01:25]:
Oh, you've told it?

Paris Martineau [01:01:26]:
No, it's just read enough transcripts.

Leo Laporte [01:01:28]:
Oh, it reads transcripts. Of course. It's heard me say it many, many times.

Paris Martineau [01:01:31]:
It's heard Geoff say it many, many times.

Leo Laporte [01:01:36]:
This is one of the things you learn about AIs is that unlike writing code where it always does what you tell it to do, it might do it wrong, but it always does it over and over reliably. This is one of the reasons, by the way, I was happy Agnes wrote a program because if that program worked once, it's going to continue. There's no probability involved, which is what you kind of want much of the time. But you wouldn't write a pro— you couldn't write a program that does this briefing. That's an LLM because it needs judgment. It needs to go out and needs to get all the resources. Yeah, it needs to research. It does the research and put it together.

Leo Laporte [01:02:08]:
But you can tell it how to do it and you can give it input on how to do it. And so that's the process of working with these. It's like an overeager, sometimes surprisingly smart, sometimes surprisingly dumb assistant or intern even.

Paris Martineau [01:02:22]:
Something I found funny this week, did you see, or maybe it was last week, did you see Boris Cherny's tweet about an example of how he prompts Claude Code?

Leo Laporte [01:02:33]:
Yeah, these, these, uh, this— Burj Cherny is the creator of Claude Code. He invented it.

Paris Martineau [01:02:38]:
Yeah.

Leo Laporte [01:02:38]:
And, uh, he periodically will post. And by the way, you know, I think the last thing he posted was delete everything and start over. So what did he say? I didn't see this most recent one.

Paris Martineau [01:02:51]:
This was something that, uh, really He sent a lot of people on X, the everything app where I do all my banking and finance.

Leo Laporte [01:02:59]:
By the way, I have an X Money account. I don't know why I gave it.

Paris Martineau [01:03:02]:
Well, I don't know why you wouldn't. It's X, the everything app where I do all my banking and finance. So this sent everybody on Twitter, to be succinct about it, into a tizzy. He essentially just posted a screenshot of him on Claude Mobile, and the prompt is such The latest acquired Home Depot episode is really amazing. I want you to make an artifact that narrates the entire episode. Make it beautiful and interactive, and focus on communicating the nuggets of business wisdom throughout the episode. Make it really good. Use lots of tokens and iterate till you're proud of it.

Paris Martineau [01:03:36]:
Maybe add watercolor illustrations and historical photos throughout. Aim for a New Yorker, New York Times, data viz vibes, but better. I think you can use OpenCV for watercolor images. And everybody freaked out because they're like, I was like, what? This guy's just prompting like he's just sending a message to his coworker, just kind of stream of consciousness. He says, just use a lot of tokens, make it really good. Don't you need a 75-part prompt that you buy from somebody or have workshopped and is really specific? And he's like, no, you don't.

Leo Laporte [01:04:07]:
Well, he's right.

Paris Martineau [01:04:07]:
You just kind of blast it off and it's fine. Of course he knows.

Leo Laporte [01:04:10]:
Of course he's right. That's not the takeaway I got from it. The takeaway I got from this is this is how a guy talks I mean, yes, on the

Paris Martineau [01:04:17]:
use

Leo Laporte [01:04:18]:
a lot of tokens

Paris Martineau [01:04:20]:
aspect, certainly. But I think that the other— like, if you see this kind of through line in terms of language and prompting throughout all of the examples he shares, and I thought it was a good example of how wrong this kind of early ethos we saw in like the early days of kind of prompt fetishization was, where, you know, you don't have to have a really complicated 1,000-word prompt to get a good result.

Leo Laporte [01:04:44]:
I think that was his message, right? I think he even kind of said that. that is the best way to do it is just to have a conversation. Now, there's a lot of refinements. Um, one of the things, uh, Poteto, who is another person you'll see a lot of on X these days, she's the, uh, she came from Cursor. She was at Meta for a while and responsible for React, very accomplished coder. And she's now in charge of GroqBot. And she's really the GroqBot front person. She's selling GroqBot a lot.

Leo Laporte [01:05:13]:
But one of the things she talks a lot about is if you want your agents to do well, you have to, um, you have to give them— yes, prompt them in English, but you want to make sure they understand what you're saying. So she says, best thing to do is, like I said to Martin, is to ask them, what— repeat back to me the gist of what you got from what I just said. That often you want an interactive conversation. Matt Pocock, who wrote— has written— he's very prolific, writes a lot of TypeScript skills, mostly for Claude, but he has a skill called Grill Me, which is— it's only a couple of sentences, but it's basically, here's what I want you to do. Now grill me about it. Keep asking questions until you and I both agree you got it. And I think that that's kind of the best advice is, yes, it's good to talk in plain English, but don't assume it understood you. Ask it, did you understand? Tell me what I just said.

Leo Laporte [01:06:17]:
What's your understanding of what I just said? Repeat it back to me. Now, that prompt that Churney gave you is, that's pretty straightforward. I mean, it's taking a piece of content and making new content around it. Who cares if it hallucinates?

Jeff Jarvis [01:06:29]:
Yeah, I'm surprised people hadn't That's about it.

Leo Laporte [01:06:32]:
Yeah.

Paris Martineau [01:06:32]:
I mean, I think that a lot of people are still stuck in this early AI mindset that it's gotta be more complicated. Like, Churney ended up writing on this after a bunch of people freaked out. Like, talk to Claude the way you would a coworker. There's no secret to prompting. You don't have to be overly scaffolded or prescriptive. Just give it a goal, it'll figure it out. It— you used— your prompt used to matter a lot back in like the Sonnet 3.5 days, but now it's much more important to communicate to the model 3 And he said, it's one, what you want it to do, 2, how much effort you want it to spend, and 3, how it should verify it did the right thing. It's really all you need.

Leo Laporte [01:07:09]:
I think that was his main point is people built prompts like they were building a computer program. And really, you don't want to tell the AI too much about how to do it. You want to let it figure out how to do it. You just want to tell it what you want. I think that's the main thrust of what he was saying there.

Paris Martineau [01:07:29]:
Yeah. And I mean, even going into the replies, a bunch of people are like, well, I'd love to see your system instructions because clearly you've got something else going on. And he's like, I don't use a custom Claude MD.

Leo Laporte [01:07:37]:
No, that was, that was from the previous tweet where he said just delete everything because what it turns out, all that stuff that we had you do before, all the skills and everything, we built that all in. So you don't need it anymore. And Claude works best if you just let Claude be Claude. I mean, he knows he's the guy who created it. Uh, he kind of had the inspiration that you could do something like this and, uh, it spawned an industry. So credit where credit's due. Did you see the interesting tweet from— speaking of tweets, first of all, meta commentary on this. I have been saying, much to my regret, and we had a whole conversation about Bluesky last week, and I did what you said.

Leo Laporte [01:08:20]:
I still don't like Bluesky for AI, but I did have my AI do all of that. Make a nice blue sky AI list. But, uh, John Gruber, who is famously cranky Mac columnist at daringfireball.net, said, I got to admit it, I don't want to admit it, but X is the place to be if you want to follow AI. And I, I wish it weren't. I wish we'd get all of these great—

Jeff Jarvis [01:08:45]:
Well, Casey Newton went back to it and got some, uh, beat up about it.

Leo Laporte [01:08:50]:
Well, there's still a lot of— by the way, there's still a lot of crap. Oh, I'm not saying X is the best place in the world.

Paris Martineau [01:08:56]:
Listen, I've never said X or Twitter were— sorry, Twitter or X, the everything app where I do all my banking and finance, are great platforms. I would even say from the pre-Musk acquisition day, it was a bad platform that actively hurts my mental health and gives me a warped view of the world. Yet I will probably be posting on it for the rest of time.

Leo Laporte [01:09:21]:
Yeah.

Paris Martineau [01:09:22]:
Such is the way of addiction.

Leo Laporte [01:09:23]:
I'm more of a reader than a poster. And I did try this week on my Mastodon instance, twit.social. I said, let's get some AI stuff going here. Here's what I'm doing in AI. Here's a list of the people that I think are really important that are doing good local models and stuff. I put it on Mastodon, nothing. So, I mean, a few people liked it, but it wasn't exactly—

Jeff Jarvis [01:09:47]:
Threads, I find completely useless. Yeah. You find anything on Threads?

Leo Laporte [01:09:51]:
No, this is the network effect, and it's kind of hard to fight this. Mm-hmm. You get a critical mass of people doing something somewhere, everybody's going to go there, and everything else is going to be starved of those people. And that's kind of what happened. Anyway, that was the meta commentary. Here's the tweet from Andrej Karpathy, who, uh, was at Tesla. We've talked about him before. He's a really smart guy, one of the founders of OpenAI.

Leo Laporte [01:10:13]:
He's back at OpenAI— no, I think he's at Anthropic now. Sorry. It's Anthropic. But he said, dusting off this tweet from April because this gap in shared understanding of LLM capability is widening. So the gist of this long tweet, that's one thing I know about Twitter— As he does. Is how long they've gotten. It's a funnel now that most people don't use AI at all. He says, napkin math, somewhere around 6 billion people have barely come into contact with LLMs at all.

Leo Laporte [01:10:47]:
75% of the population. About 1 to 2 billion are casual and infrequent users of free-tier ChatGPT-like projects. This group sees derpy chatbots and treats them a bit like a better Google search. Then you get to the smaller group, the professional use of frontier-grade LLMs in math and code, Claude Code, for instance. 20 million people. 0.2% of the Earth's population, see firsthand that large, complex projects that used to take them weeks or months can now be completed by agents with a prompt. Finally, this is the important part, we get to the 5,000 people, 0.00006%, with access to frontier-grade systems internally. People like Brian Cherny.

Leo Laporte [01:11:33]:
The external world has seen the preview. It looks like swarms of thousands of agents collaborating on weeks over weeks on software for mega projects, minting zero days, running cyber attacks, things that would've taken top professionals years to work. That's 5,000 people. And I would say there's even a pointier part of that funnel, maybe 100 people who are actually working with the latest really scary models. And this is the thing that scares me a little bit. I poo-pooed this whole idea of I mean, we've all poo-pooed this idea of AGI, but I poo-pooed this idea that the end of the world is nigh and that agents are going to kill us all and all this stuff. But what we don't know, what we can't know, is what the people who are really in the middle of it are seeing. And I wonder, do you think it's possible that they are seeing something that's truly terrifying?

Jeff Jarvis [01:12:31]:
So I saw a video this week. I think it was in the Times. I can't remember who did it. Maybe it was Ezra Klein. Quoting Jensen Huang saying, Jensen Huang says it's just software, but you say that it's an alien brain. And he said, yes, it's an alien brain because we created it and we didn't know what we were creating. And as my friend David Weinberger says, accidents are those things we can't explain. And just because we can't explain it doesn't mean it's otherworldly or conscious or something amazing.

Jeff Jarvis [01:12:57]:
They can't explain it right now. They don't even know what they created. And that's That's— yeah, that's amazing. But that doesn't mean that it's a brain. It doesn't mean it's conscious. It doesn't mean it's alive. It doesn't mean any of that BS.

Leo Laporte [01:13:10]:
I agree. But it is possible it's dangerous without being an alien intelligence.

Jeff Jarvis [01:13:15]:
Well, again, dangerous from whom? The interesting thing, I think, Leo, is that all the danger, danger, danger we're hearing around the models has come from the model companies' activities themselves.

Leo Laporte [01:13:24]:
Right.

Jeff Jarvis [01:13:25]:
Not from users of— external users of OpenAI or Anthropic. It's OpenAI and Anthropic Who themselves in their own companies, their own testing, have done bad things to show off their macho. And so, yeah, it depends on how you use them and whether you're responsible about that.

Leo Laporte [01:13:41]:
You know, I agree with you. That's what the thrust of our shows have been for the last 6 months. But I worry that maybe, maybe they have seen something that makes them think that these AIs are getting so smart. For instance, it's in the rundown. I can't remember where. I think this came out of Anthropic. They believe that as we get small models, that it's— here it is from Anthropic Research.

Jeff Jarvis [01:14:08]:
It's going to kill you.

Leo Laporte [01:14:09]:
Yeah, they— AI models— this is from their Frontier Red Team. So these are the, you know, few hundred who are right there in the pit with the— in the abyss. AI models may be capable of self-replicating without access to a data center. Remember, I've been saying all along, they don't escape They have to run in a data center. All you do is you unplug the data center. They're not outside of the building. Well, maybe they will be according to this. Our experiments suggest that small AI models— and people are working really hard to get these models small enough to run on your phone— small AI models may be months away from being capable enough to self-replicate on commercial IoT devices like smart ovens.

Leo Laporte [01:14:53]:
And off-brand Android TV boxes. A replicator like this would be challenging to shut down. No kidding, since there's no longer a centralized shutoff. Well, that's—

Paris Martineau [01:15:07]:
I love that.

Leo Laporte [01:15:08]:
That's a terrifying thought. I mean, that means there'd be— it's like Gremlins or Triffids. Trouble with Tribbles. What is it?

Paris Martineau [01:15:16]:
Instead of a ghost in the machine, we've got a swarm Swarm in the air fryer.

Leo Laporte [01:15:20]:
Yeah, it is like Ghostbusters, isn't it?

Paris Martineau [01:15:24]:
Yeah, but I mean, just what, what are we supposed to do with that? Suddenly you could just have an agent swarm out there. It would be bad. DDoSing whatever. It would be bad.

Leo Laporte [01:15:37]:
Yes. Uh, I guess the question is, A, are these guys nuts? Like, are they— you know, maybe they're not nuts. Maybe they're trying to promote this idea I don't know what they mean.

Jeff Jarvis [01:15:48]:
Because what he's saying is, oh, these open weight models can do this. He wasn't saying that they're there. He's saying open weight can do it.

Leo Laporte [01:15:54]:
So maybe this is propaganda to get Congress to shut down open weight models. You're right, they're talking about open weight models.

Jeff Jarvis [01:16:00]:
Yep, yep.

Leo Laporte [01:16:01]:
By the way, only we are capable enough to prevent this, although they do mention Gemma, which is from Google.

Jeff Jarvis [01:16:09]:
But Gemma is an open model, right? So maybe that's

Leo Laporte [01:16:13]:
what they're saying is

Jeff Jarvis [01:16:14]:
they want these things under control of us big companies. I don't think we have

Leo Laporte [01:16:18]:
enough information.

Jeff Jarvis [01:16:20]:
We have enough information to know we should not, that AI is in the wrong hands and we shouldn't trust these bozos.

Leo Laporte [01:16:24]:
I agree with that. Jerks. And I know it's a belief, but it's a longstanding belief that open is always going to be better than an autocratic closed system that's controlled by a handful of people. It's always better. I know that raises the risks somewhat, but it's always better to have it open and everybody gets a chance.

Jeff Jarvis [01:16:45]:
At least open in the ecosystem. It provides competition.

Leo Laporte [01:16:49]:
Yes. And yes, maybe bad guys will have bad models that they wouldn't have if Anthropic and OpenAI kept a tight hold on them. Let's talk about Muse, but we're going to take a break.

Jeff Jarvis [01:17:01]:
I'm going to tell you a story, my Muse story from the week too.

Leo Laporte [01:17:03]:
I'd like to hear your Muse story. Do you have, Paris, a fluffy little octopus Muse that you—

Paris Martineau [01:17:10]:
I actually did try Muse for the first time this week, and I immediately wanted to change the octopus, but got distracted.

Leo Laporte [01:17:18]:
You could change it to watching the world.

Paris Martineau [01:17:19]:
I will say it is actually the first, um, AI service and/or like agent that has been able to complete one of my own little small benchmarks. I don't think it's a benchmark for overall quality, but it's just something I thought that the AI tools should be able to do, but it never had, which is— I, you may recall—

Leo Laporte [01:17:40]:
Well, wait a minute, hold on. I gotta do a break. So,

Jeff Jarvis [01:17:42]:
so

Leo Laporte [01:17:43]:
this is So that's the tease. I'm very interested.

Jeff Jarvis [01:17:45]:
We're going to have lots of Muse.

Leo Laporte [01:17:47]:
And I do think— We're

Paris Martineau [01:17:48]:
going to have Musings.

Leo Laporte [01:17:49]:
I do think—

Paris Martineau [01:17:50]:
Stay tuned for Musings.

Leo Laporte [01:17:51]:
Muse, more Muse still to come. I do think you're right on though. Don't deprecate that. That's the bench— that's what benchmarks should be. Here's something I wanted to do.

Jeff Jarvis [01:18:01]:
Yeah.

Leo Laporte [01:18:02]:
And I wanted to see if it could do it. It should be that, not what some company says, oh, this is the smartest ever. That doesn't matter.

Paris Martineau [01:18:10]:
It's what it does for you. Our benchmark should be pants-based is all that I'm saying.

Leo Laporte [01:18:15]:
Okay. The pants-based benchmark coming up. But first, a word from our sponsor this episode of Intelligent Machines. Now that's a tease. So Muse, we should just give you the backdrop on this. This is from Meta. It's an agent in the same way that OpenCLAW is an agent. The Hermes that I use is an agent.

Leo Laporte [01:18:36]:
GrokBot is another example of of this. Muse though has quickly leaped to the top of the charts. It's the number one free app on iOS. I am seeing constant ads for Muse, even on things like football games, NFL games, expensive national network ads. I'm also seeing more and more stories about problems. The New York Times had a story about This was on Friday about Muse causing problems, but Mark Zuckerberg said, yeah, no. Muse had delayed releasing— or Meta had delayed releasing Muse, its AI agent app, for months over safety concerns. Then new competition forced Mark Zuckerberg's hand.

Leo Laporte [01:19:27]:
And it was partly because of Instinct, which is another agent that I have By the way, I should preface this.

Jeff Jarvis [01:19:34]:
You've been using Instinct, eh?

Leo Laporte [01:19:35]:
I've used Instinct. I've used Muse. I'm curious about that. Notice the past tense. This week I deleted Instinct.

Jeff Jarvis [01:19:42]:
Wow.

Leo Laporte [01:19:42]:
I deleted Muse, and I said, Muse, delete everything you know about me. And you know what happened? It was Sunday. My eyes got opened by Wesley Faulkner on Twitter. I said, you know, I'm using Muse because I got nothing to lose, right? I live in the public. I'm an old man. I'm a white man. I don't have a boss. Let Meta have everything.

Leo Laporte [01:20:01]:
He said, but Leo, you're not just narcing on yourself. You're narcing on your friends and your friends of friends. And that's what Meta's always done, right? That's how—

Jeff Jarvis [01:20:10]:
He also said, I listened to it. He also said that, well, it has these codes to get you into applications, but those are always temporary codes. They're not long-term.

Leo Laporte [01:20:22]:
No, it has all my stuff. It had my SSH, it had my Tailscale, it had anything it Because this is the— this is— and I'll be very curious to hear your benchmark in a second, Paris. This is the problem with these. For them to do something useful, you have to give them something. Now Meta says, oh no, but your Muse, which is running on their computers, right, on their servers, is an encrypted virtual machine. We can't see into it. But every single thing it does, they can, because in order to talk You know, if you say, well, find my contacts that live in Hoboken, it's going to send all those contacts to the server and they're going to have them all and then it's going to send the answers back. So yes, everything inside the Muse VM is secure, but in order to use Muse, it has to talk to the outside world and Meta has access to all of that.

Leo Laporte [01:21:15]:
So there's a real temptation when you get these agents, whether it's Instinct, Muse, Grok, But to give it more and more capability. So I don't mind it knowing all my stuff, but I don't like the idea that I'm narcing on my friends. So yeah, I deleted, I've deleted them all. And the other reason is the reason I spent so much time and money on local AI is so that I would get those capabilities on a local machine so it wasn't narcing my friends. It was all staying inside the house and I have that capability. Yeah. So I thought, why am I depriving Hermes, my agent locally, of all of that? Why should I send anything to Muse? In fact, one of the first things I did— I've done 2 things actually. Remember I was doing the Dream Beans thing with Google? And of course—

Jeff Jarvis [01:22:06]:
Which I can't get because of Workspace.

Leo Laporte [01:22:08]:
Well, so one of the things I said is— Yeah, sure. It's fine. I like Dream Beans. I don't want to give that information to Google. Can you do it? And it said, yes. In fact, what I'm going to do for you is something I'm going to call Tao Beans. Each day, I'm going to pull a verse from the Tao Te Ching and give you an illustration of it. And I will— and it's going to be in the style of Dream Beans, but it does this in my Obsidian daily note.

Leo Laporte [01:22:31]:
This one is chapter 64, Mindful of Little Things. The tree you can't reach your arms around grew from a tiny seedling. The 9-story tower rose from a heap of clay. The 10,000-mile journey begins beneath your foot.

Jeff Jarvis [01:22:44]:
This is awful. This makes me want to puke.

Leo Laporte [01:22:47]:
Well, it's the Tao Te Ching. If you don't like the Tao Te Ching, fine.

Jeff Jarvis [01:22:50]:
It's the image. The image is just too much.

Leo Laporte [01:22:52]:
You don't like the image of me on a path with a little seedling and a big

Jeff Jarvis [01:22:56]:
oak? Make a poster out of it.

Leo Laporte [01:22:58]:
Well, I could, by the way, I could do that. And if you looked at my Instagram from the last 2 weeks, I was having Muse do different artworks. Have you seen that? Anyway, I said—

Jeff Jarvis [01:23:09]:
I don't go to Instagram that often.

Leo Laporte [01:23:10]:
Yeah, well— I

Paris Martineau [01:23:11]:
was gonna say, you've been posting a lot of, uh,

Leo Laporte [01:23:14]:
Not anymore.

Paris Martineau [01:23:14]:
corned beans pics on Instagram.

Leo Laporte [01:23:16]:
Not anymore because, um, I took— turned that off, right? But that's what— yes, you're right. That's what Muse was doing. All of these pictures in my— came from Muse, right? It would take a little paragraph from my daily journal And illustrate it. And I said, do it in different ways. So that's a Japanese woodcut. This is a paper cut illustration. This is a comic book. So it's doing them in different ways, the blueprint.

Leo Laporte [01:23:46]:
I think it actually was really fun, but I don't want Muse— I don't want Meta to read my daily journal anymore. So I am working now with Hermes to do the same thing on Instagram. There is a little problem. problem, which is Instagram. Uh, you have to create a creator account. I did that. Then you have to get a token from, from Meta, and Meta's saying, no, you haven't been using this app long enough, I'm not going to give you the token. Oh, so I wonder.

Leo Laporte [01:24:14]:
Anyway, let's talk about your Muse experience, Paris.

Paris Martineau [01:24:18]:
I have always— so you might recall that, uh, when I wear glasses, usually they're green and Yeah. And whenever I got these glasses some years ago, an editor of mine at the time was like, oh, are you gonna do like a— it's like a Billy Joel sort of thing where suddenly you get a different color pair of glasses for everything you wear? And I was like, no, that sounds expensive, but I will try and buy all of the things I wear in blue and green going forwards that they match.

Leo Laporte [01:24:44]:
Your glasses were your trademark when we first met.

Jeff Jarvis [01:24:47]:
They were. Yes.

Paris Martineau [01:24:47]:
You know, this has been a weird new year for me not wearing the glasses all the time, but I still really like the blues and greens, so I'm always on the hunt for it. But—

Leo Laporte [01:24:56]:
You have blue nails. right now. They're blooming right now. And you have a green checked shirt on right now.

Paris Martineau [01:25:00]:
A green shirt. And so I'm always looking for it. I've asked Gemini, I've asked Claude, I've asked ChatGPT on every mode you could ever think of to find me some assortment of things in specific shades of blue or green. Specifically, I want like stuff kind of like my nails, like saturated tones, like bright stuff. They always do such a terrible job. I can barely even convey it. It just is like maybe the first row of Google search results if you search blue shirt. or something.

Paris Martineau [01:25:29]:
It's awful. So I gave Muse the task, put a little bit more effort in describing what I did or didn't want, and honestly, it came up with a good haul of like lists. It's right now— I just asked it to put together a presentation and it— well, the presentation I can see already, even though it did just right now, is absolutely lacking. Instead of including photos of all the things it found, like I It just has the same AI slop photo pasted again and again. Uh, it is clearly not 100% great, but I did watch it while it was working in its little browser windows, and I found a lot of good stuff from that, even if it didn't make it to the final presentation, which I found at least very useful. Um.

Leo Laporte [01:26:18]:
And that's the first one that was able to do that for you?

Paris Martineau [01:26:21]:
It was the first one that was able to I was gonna say more than one, actually make at least one even suggestion of something I'm going to buy. There are quite possibly going to be multiple things I'm gonna buy from it, or at the very least, like brands it suggested. And so I wanted to do this originally because I was like, I do. I didn't do this on a Meta account connected to my Instagram or my Facebook or any of— I did this on a completely fresh Meta account that I made to be completely unconnected to any identifying data because one, I just didn't want it to message all of my friends, I'm now using Meta AI. But two, like you said, Leo, like, I just, I don't want to expose all of my data to whatever this is and all of my friends' data. So I kind of did a blank slate. So it was able to do a pretty decent job despite the fact that it wasn't working from any information. And I suspected that it might be able to do this because Meta clearly has access to some amount of data when it comes to taste and brands in order to make Instagram ad recommendations and ostensibly Facebook ad recommendations, I would assume, in just a way that I never saw with the responses I was getting from Google or OpenAI products.

Leo Laporte [01:27:39]:
See, I think that's—

Jeff Jarvis [01:27:42]:
Can I tell you my Meta experience?

Leo Laporte [01:27:44]:
Yeah, let me just say one thing. I think that's an important point is you don't have to give them, your friends and family, you don't have to give them, you decide what Meta gets and you gave it just enough to do a good job. And I think that's great. So what did you do, Jeff?

Jeff Jarvis [01:28:00]:
So I was driving back from my— It's like show and tell. Book presentation, Hot Type, now on sale at jeffjarvis.com with a beautiful new page by my son. And I was bored with the book I was listening to. And I've been contemplating a book idea and just rolling around, very, very early idea. And it involves 2 towns and their histories. And so I tried with Google to ask some facts about these towns. And the thing is Google on the, just, you know, on the phone and it would gimme an answer and then it would forget and I'd have to start over again every time. Screw that.

Jeff Jarvis [01:28:34]:
So there's a time for Meta. Go to the Meta app and I explain what I'm doing. And of course it comes back and says, what a great idea. But it gives me some really good perspective on it. And then I was in a conversation in the car. It was really cool. Well, it gave me a long thing and I said, can you read this to me out loud? And it said, yes, I can, and gave me a click. And then I asked it to do that again.

Jeff Jarvis [01:28:55]:
I said, from now on, I'll read to you out loud until you tell me otherwise.

Leo Laporte [01:28:58]:
Oh, that's nice.

Jeff Jarvis [01:28:58]:
It was amazing. And so I asked it, um—

Leo Laporte [01:29:01]:
By the way, that's how you get bossy agents.

Jeff Jarvis [01:29:05]:
Yes, yes, exactly.

Leo Laporte [01:29:06]:
Because they accumulate all of this stuff. Go ahead.

Jeff Jarvis [01:29:09]:
You must be hard of hearing or hard of reading. So soon I'll have my cataract surgery and I will be. So I'm driving along and I'm asking it questions and having it come back. And it's also about a couple of, a really well-known book from way long ago. And it's giving me interesting stuff and interesting perspectives and all kinds of stuff. At the end, I said, can we continue this conversation? tomorrow. And yes, I won't remember all of this, but I'm going to condense this into something. I said, well, can you save the text? Yes, here's a file, you can download this.

Jeff Jarvis [01:29:38]:
So then this morning, this is all, really this is yesterday rather, it's all on Monday. On Tuesday, at some point during the day, it said, oh, I have a correction of something I told you yesterday. Ah. And then it said, oh, remember I was going to read, I was going to give you a few examples of these poems yesterday, and here they They are. And the cynical me says it's driving engagement, but the happy me says it's constantly there working, chugging in the background for me.

Leo Laporte [01:30:07]:
Precisely right.

Jeff Jarvis [01:30:08]:
Right.

Paris Martineau [01:30:08]:
It is. I— it is exactly doing that. I had the exact same experience with Muse where I asked it for things. I didn't follow up because I didn't have anything to follow up on it. I just saw it work and I got 5 more messages about the same task today.

Jeff Jarvis [01:30:21]:
I'm still here.

Paris Martineau [01:30:23]:
Here's more pants. Here's a tights option. Here's this. Do you want a sweater?

Leo Laporte [01:30:30]:
That's—

Paris Martineau [01:30:31]:
it's engagement bait.

Leo Laporte [01:30:32]:
They're doing— yeah. And they're doing that on purpose. They have a— in the background, they have a little job that runs every whatever, 5, 10, 15 minutes that just, you know, wakes it up and says, do that. So, but that's what makes an agent That's

Jeff Jarvis [01:30:48]:
why an agent works. It was my first opportunity to do something I cared about, that I found value in. I was in odd circumstances driving, and I was very impressed, I gotta say.

Leo Laporte [01:30:57]:
That's the difference between a chatbot and— one of the differences, many differences, but one of the differences between a chatbot and an agent. Chatbot, you ask it a question, it gives you the answer, you're done.

Jeff Jarvis [01:31:04]:
Done.

Leo Laporte [01:31:05]:
Yeah. You initiate the conversation. Agents, in theory, it's actually not true, but the impression you get is they're always running. And because Muse runs on Meta's computer, you don't know. This is the story you told, Paris, about guys going around on the subway with their laptops half open to keep their agents going. Well, you don't have to do that. It's running on Meta's computer. It's always running.

Leo Laporte [01:31:28]:
It's true of all of the bots. It's not really because nothing runs all the time. It just gets, it has a little—

Jeff Jarvis [01:31:37]:
And it would be a waste if it did run all the time.

Leo Laporte [01:31:39]:
Yeah, it'd be a waste. But it has a routine, a little job that it runs periodically that wakes it up and says, what did you— I wonder what the prompt is. And by the way, you can still, I don't know how much longer this will be the case, download your entire folder from your Muse, from your computer. Meta says, yeah, that's intentional. You just ask it, Muse, zip up all the files, everything you know about me, and download it, which is worth doing. It's a lot of data, but you can get some idea, especially as you use it more and more, of what it's accumulating about you. All these agents have memory systems of various types. I'm guessing Meta is probably text, just text files.

Leo Laporte [01:32:24]:
So you'll be able to download a bunch of text files that'll say, Jeff apparently is hard of hearing because he wants us to read to him now.

Jeff Jarvis [01:32:31]:
Or actually, obviously, Jeff has visual problems.

Leo Laporte [01:32:35]:
Yeah. Oh, that's right. Not a hard— the opposite. Send Jeff to a cataract surgeon. It might be very interesting to see if it does start suggesting things like that. It's very pro— I have noticed Muse is very proactive.

Jeff Jarvis [01:32:50]:
And so I didn't have to give it— for this task, I gave it access to nothing that I'm working on,

Leo Laporte [01:32:55]:
right?

Jeff Jarvis [01:32:56]:
No email, no nothing. I had a conversation with it about a topic that I cared And, um, but you know what it now knows?

Leo Laporte [01:33:03]:
It knows what you care about.

Jeff Jarvis [01:33:05]:
It was a little repetitive. Yeah, you told me that already.

Leo Laporte [01:33:08]:
But, but it knows. So this is value. This is gold to Meta, right? What does Meta want to know?

Jeff Jarvis [01:33:14]:
What I'm interested in.

Leo Laporte [01:33:15]:
What you're interested

Jeff Jarvis [01:33:15]:
in. Now, in the case there's no advertising to be done on this. Zero.

Leo Laporte [01:33:20]:
Well, I wouldn't be so sure.

Jeff Jarvis [01:33:22]:
Well, there are 2 towns in the heart of the country, so it could be they'll be able to I don't think I'll be getting more airline ads.

Leo Laporte [01:33:28]:
Yeah. I mean, yeah, you just— I mean, that's what's kind of interesting about this, this whole thing. Have you ever gone to your You page on Amazon?

Paris Martineau [01:33:38]:
Oh, I have. What did yours reveal?

Leo Laporte [01:33:40]:
Isn't that interesting? Let me see if I can, I can go there. Let me go to my You page.

Paris Martineau [01:33:48]:
So this came about because someone was talking about it on X, the everything app where I do all my banking and finance. Um, today I'm— it's really making me laugh, so I'm using the— That makes me laugh too. Formal name. Um, people were talking about it on that platform because someone went under the About You section of their Amazon page and found some wacky stuff on there.

Leo Laporte [01:34:13]:
You can show this, Benito. I have nothing to hide.

Paris Martineau [01:34:16]:
I was gonna say, mine had, uh, make sure It doesn't show your address or anything. Mine had nothing interesting in there, but it's because I don't share anything interesting.

Leo Laporte [01:34:26]:
Well, some of this is wrong. For instance, it says I have an Nespresso Vertuo machine.

Jeff Jarvis [01:34:30]:
How dare it!

Leo Laporte [01:34:31]:
But that's symbolic.

Paris Martineau [01:34:32]:
You can click it and correct it if you want.

Leo Laporte [01:34:34]:
Yeah, I can correct it.

Paris Martineau [01:34:35]:
You don't need to though. Why?

Leo Laporte [01:34:36]:
Yes. No, I don't.

Paris Martineau [01:34:38]:
Why correct your—

Leo Laporte [01:34:40]:
I know, I'm not gonna.

Paris Martineau [01:34:42]:
No, I don't in all caps.

Leo Laporte [01:34:44]:
No, I don't.

Jeff Jarvis [01:34:44]:
No, I do, you said.

Leo Laporte [01:34:46]:
Oh, shoot. No, actually what I am going to do is I'm going to tell it my whole coffee setup because I wouldn't mind if Amazon suggested it. It should know it better by now. I've bought a lot of stuff from Amazon, but that's because I bought my mom a Nespresso. Periodically refreshes household organization. I don't even know what that means. I guess I bought bins or something.

Paris Martineau [01:35:07]:
Mine says constantly replenishing cat care supplies.

Leo Laporte [01:35:11]:
Does it really?

Paris Martineau [01:35:12]:
Yes.

Leo Laporte [01:35:13]:
Awesome. Mine says prioritizes ergonomic design in everyday items, uh, owns an electric vehicle. That's correct. Lives in Petaluma. Likely selects OOFOS for recovery footwear. Okay. Oh, wait a minute. It does say coffee brewing equipment.

Leo Laporte [01:35:30]:
Okay. Has a cat named Rosie. How the hell they know that? I don't know.

Jeff Jarvis [01:35:35]:
You talk about it on the air.

Leo Laporte [01:35:37]:
Where? Yeah, because I didn't mention that in any Amazon purchases. Maybe it's my name.

Paris Martineau [01:35:41]:
Did you mention it in an Amazon review?

Leo Laporte [01:35:43]:
Maybe my Echo? I don't know. I don't do Amazon reviews. Regularly chooses Anker for charging solutions.

Paris Martineau [01:35:49]:
I also have that on mine.

Leo Laporte [01:35:50]:
Selects Dr. Tung's for dental care. Programs computers. Likes SmallRig for cameras. Shops from men's departments. Notice it doesn't say I'm a man. Streams content creation. Practices strength training.

Leo Laporte [01:36:04]:
Prefers a masculine voice. And Italian cuisine. I mean, it's true. Walking, cooking, playing chess, podcasting. So it knows a lot. Doesn't it? That's really interesting to read this.

Jeff Jarvis [01:36:17]:
I have long, since, since I wrote What Would Google Do, I've, or Public Parts, I've long argued that companies should do exactly this. They should then be very open about it.

Leo Laporte [01:36:25]:
Here's everything we know about you. And let you correct it.

Jeff Jarvis [01:36:27]:
And let you correct it, which, which they only gain more data.

Leo Laporte [01:36:30]:
Right.

Jeff Jarvis [01:36:31]:
Uh, and you, you get a little more relevance and less irritation.

Leo Laporte [01:36:34]:
And I don't think people love reading about themselves. I think people like—

Jeff Jarvis [01:36:38]:
It takes the mystery away. Okay. What you two of you were just doing. Oh, this is how it knows that. Okay. Yeah, I get that. Yeah, yeah, yeah. No, no, it's not frightening.

Leo Laporte [01:36:45]:
Oh, wait a minute. I get it. Okay. Make Amazon even more personal to you. Your other AI assistants have learned about you. Bring that knowledge here for even more personalized experience. So you can import your Claude code. Here's a prompt that you— they say, put this prompt into your AI assistant.

Leo Laporte [01:37:05]:
Is that wild?

Jeff Jarvis [01:37:06]:
How do you get to this page?

Leo Laporte [01:37:08]:
Uh, you go— okay, so you go to your account on Amazon and you click your head. You just click your name in your Amazon account and it— and then among the things you can choose from will be the About You. I could— I can, uh, see there it is, About You New. See the person? I'm glad they do this. I wish Meta would do this.

Paris Martineau [01:37:32]:
They do a version of this.

Leo Laporte [01:37:33]:
Do they? Oh, okay. They have an advertising interest. That's right. Yeah. Yeah. They may be mandated to do this. By the way, Jake did a great job on your website.

Jeff Jarvis [01:37:42]:
It's really nice. Yeah. Podcasts. Look at the podcast tab.

Leo Laporte [01:37:46]:
This is hysterical because Jeff's had the worst, ugliest website forever and his son does web design.

Jeff Jarvis [01:37:52]:
Yeah.

Leo Laporte [01:37:52]:
Look at this.

Jeff Jarvis [01:37:53]:
Beautiful. Scroll on down. Even the episodes.

Leo Laporte [01:37:57]:
Oh, look at that. Is it automatic?

Jeff Jarvis [01:37:59]:
I think so. I didn't know he was doing it. I was with him all weekend.

Leo Laporte [01:38:03]:
Jake, you're the right guy. Now, he— what he didn't do, I'm sad to say, is Buzz Machine. He didn't fix that.

Jeff Jarvis [01:38:10]:
He did that ages and ages and years and years and years.

Leo Laporte [01:38:13]:
Oh, he did it for you originally? Okay.

Jeff Jarvis [01:38:15]:
But I messed it up in the meantime.

Leo Laporte [01:38:17]:
We can get drop shadows. We can kind of— old hat.

Jeff Jarvis [01:38:19]:
Yeah, it is. It is. But that's, that's, that's a little snapshot in time.

Leo Laporte [01:38:24]:
Yeah, I, I have an ugly blog. It's— I'm proud of it. Um, all right, let's take a little break. That was fun. Our Muse— is there anything— I mean, there's more to say about Muse. It was amusing. Yes.

Jeff Jarvis [01:38:36]:
Um, any other Muse jokes we got here?

Leo Laporte [01:38:40]:
Let's see. Let me see what else I have about Muse.

Jeff Jarvis [01:38:42]:
Anybody in the, uh—

Leo Laporte [01:38:44]:
I had quite a few. I feel like I had quite a few Muse stories in the rundown. I don't know if we want to spend a whole lot more time. Alexander Waite— oh, I did have one more story. They have released an open— or they're gonna release the open weight model behind Muse.

Jeff Jarvis [01:38:59]:
And they are still. Oh, okay.

Leo Laporte [01:39:01]:
I applaud them for this.

Jeff Jarvis [01:39:03]:
Good.

Leo Laporte [01:39:03]:
So, uh, Muse uses a pretty good model, which you can, by the way, subscribe to, called Muse Wonder. Uh, and it, uh, it's, it's a pretty darn good model. It's not as good as Argon, it's not as good as Opus, but given what Meta has released in the past, like Glimmer, which isn't very good, it's good.

Jeff Jarvis [01:39:24]:
Does it cost

Leo Laporte [01:39:25]:
You can subscribe to it. Yes, they have a, they have a paid version. So does, so does Muse, by the way, but they— the agent— but they give you so much free that I didn't find I needed to pay anything, but you could if you wanted to. So they've released Muse Wonder, uh, and, and Mark Zuckerberg said, yes, we are going to release the Now What.

Paris Martineau [01:39:46]:
I'm sorry, I was just looking at that. I took a look at the Discord and Patrick's, uh, About You section for Amazon just really clocked him as a very specific guy in a way that I find odd.

Leo Laporte [01:40:00]:
Is it real, do you think?

Paris Martineau [01:40:02]:
It's a brand.

Leo Laporte [01:40:03]:
He's not making it up.

Paris Martineau [01:40:06]:
Interests: model train equipment, chooses Kato for model train track systems, build model train collections systematically, build model train layouts.

Leo Laporte [01:40:13]:
I didn't think you were into model trains.

Paris Martineau [01:40:15]:
Builds Lego sets regularly, prefers Lego for building block systems, prefers N and HO scale model trains. Maintains regular 3D printing supply chain.

Leo Laporte [01:40:26]:
To be fair, Patrick has a young son, so it's very possible he's not buying these things for himself.

Paris Martineau [01:40:31]:
I mean, if he was, that'd be wonderful as well, but I just think it's very funny to have them all in one sentence.

Leo Laporte [01:40:38]:
That's cute, Patrick. That's cute. That's really cute.

Paris Martineau [01:40:39]:
It really made me laugh.

Leo Laporte [01:40:41]:
Yeah. Well, see?

Jeff Jarvis [01:40:43]:
He's a good dad too.

Leo Laporte [01:40:44]:
People want to know. So Mark Zuckerberg talks about wonder. And they also have a higher-end version, which I also believe, uh, Muse is using.

Jeff Jarvis [01:40:53]:
And what's the brand for that one, Leo?

Leo Laporte [01:40:54]:
Wonderful.

Jeff Jarvis [01:40:56]:
Oh!

Leo Laporte [01:40:57]:
It's the full version of Wonder.

Jeff Jarvis [01:40:58]:
It's Wonderful.

Leo Laporte [01:40:59]:
But Mark said, I'm not— we're not going to release that one open weights. He says 2 things: it's too big for you, which it probably is. Probably if it's many trillions of, you know, bytes of, uh, trillions of weights, it's— you're not going to fit it on a machine locally. And it's too dangerous, he says. See, you got to say that or people aren't going to think your model's any good. It's too dangerous. We can't release it. It is pretty good.

Leo Laporte [01:41:24]:
And you can use it if you have a subscription to Meta's. They have a, you know, like everybody else, a subscription service. And it's pretty good. Darren Oakey in our Discord says he thinks it's quite strong. So There's some good models. Mom's arriving in 23 minutes. We better do another commercial.

Paris Martineau [01:41:46]:
You know, frankly, I, I'm incredibly happy to see her. One would assume that when you're coming to visit your daughter, when she says she's live on the air recording a podcast and needs to know when you're arriving, that you tell her before you're in the Uber and she has to send you—

Leo Laporte [01:42:01]:
I think she's gonna come on the show is what I think.

Jeff Jarvis [01:42:04]:
I think that's what we need. Yes, we need to quiz her. My

Leo Laporte [01:42:05]:
mom comes on the show.

Paris Martineau [01:42:08]:
She now is a ChatGPT head too, so—

Leo Laporte [01:42:11]:
So she might be our AI.

Jeff Jarvis [01:42:12]:
I think this is a guest appearance.

Paris Martineau [01:42:15]:
I mean, honestly, what I need to do is I need to drug Gizmo before she gets here.

Jeff Jarvis [01:42:19]:
I was gonna ask you, Gizmo, because she's a woman.

Paris Martineau [01:42:20]:
Oh yeah, she's a woman. Take the ad so that I can drug my cat.

Leo Laporte [01:42:23]:
Go drug your cat. And you might want to make your bed. Do you have a nice mattress for mom to sleep on?

Paris Martineau [01:42:29]:
Oh, I've made everything. I furiously cleaned the entire apartment top to bottom. Last night, even though she said you don't have to clean for me.

Jeff Jarvis [01:42:36]:
Oh no, no, no, she lies.

Paris Martineau [01:42:37]:
I was like, that's what I wanted to say.

Leo Laporte [01:42:37]:
It's always good to have parents visit. Yeah. And do you have a nice mattress for her to sleep on?

Paris Martineau [01:42:42]:
Yes.

Leo Laporte [01:42:43]:
Is it a Helix mattress?

Paris Martineau [01:42:45]:
It's not. It's a mattress that I got from Amazon 10 years ago.

Leo Laporte [01:42:51]:
Oh, so we got 2 really— by the way, kitty Valium, kitty Seconal.

Paris Martineau [01:42:57]:
Kitty gabapentin. And she's learned to deceive me, which is really concerning. Oh, I'm so sleepy. I don't even know. No, no, no. You put the little pill in her mouth and you gotta get her to swallow it. No, no, no, no, no, no, no. And I've learned that I will put the little pill in her mouth, close her mouth, she'll do some swallowing motions, and then I see— I let her go and she spits the pill up.

Paris Martineau [01:43:22]:
She's lying. So I had to double-check.

Leo Laporte [01:43:25]:
She's so small. Good, good.

Paris Martineau [01:43:27]:
Yeah.

Leo Laporte [01:43:28]:
All

Jeff Jarvis [01:43:28]:
right, so she really doesn't like Mom.

Paris Martineau [01:43:31]:
She doesn't like women.

Jeff Jarvis [01:43:33]:
I knew that.

Paris Martineau [01:43:34]:
And the issue is that my mom is very nervous around Gizmo, which makes the problem worse. And then if Gizmo makes any sudden movements, my mother shrieks, which causes Gizmo to freak out, and then it causes my mom to shriek louder, and then my landlords get mad because it sounds like someone's being murdered. in my house. So, you know, it's gonna be a really funny 18 hours.

Leo Laporte [01:43:56]:
It is Halloween.

Paris Martineau [01:43:57]:
It's true.

Leo Laporte [01:43:58]:
Uh, do not— I'm just gonna make one suggestion: do not bring Gizmo to the lesbian bar.

Paris Martineau [01:44:03]:
Well, she would actually probably do well.

Leo Laporte [01:44:05]:
Oh, maybe.

Paris Martineau [01:44:06]:
Because when it's not in my apartment or a place where she feels like she has dominion, she's pretty chill.

Leo Laporte [01:44:11]:
Like, she's the boss of that apartment.

Paris Martineau [01:44:13]:
She's the boss in the apartment, and that means she's ready to draw blood. Yeah, that makes sense. But when she's in a free space such as the JFK Airport, she's ready to be pet by anyone.

Leo Laporte [01:44:23]:
Where have you— oh, you fly her to Florida when you go to— I

Paris Martineau [01:44:25]:
do, and she does great. You take her out of the case?

Jeff Jarvis [01:44:27]:
You carry her?

Paris Martineau [01:44:28]:
You have to take the cat out to carry them through TSA.

Jeff Jarvis [01:44:31]:
Oh, right. Okay. Yeah.

Paris Martineau [01:44:32]:
But also—

Leo Laporte [01:44:33]:
What, do they frisk them?

Paris Martineau [01:44:35]:
They require the cat to go through the metal detector.

Leo Laporte [01:44:38]:
No!

Paris Martineau [01:44:39]:
Yes.

Leo Laporte [01:44:40]:
How does she go through the metal detector?

Paris Martineau [01:44:43]:
I hold her on my shoulder. Oh,

Leo Laporte [01:44:44]:
oh, oh, not the machine. She doesn't go on the conveyor belt. No, no, no. Oh, okay. That's a relief. Not the X-ray machine.

Paris Martineau [01:44:51]:
You can't, but her bag has to go through the X-ray machine without her in it. So you have to carry her.

Leo Laporte [01:44:58]:
And you hold her and you go through the machine. That's hysterical.

Paris Martineau [01:45:03]:
Yeah, I hold her. She's wearing a Thundershirt as well as a harness and shaking like a leaf. And sometimes it's a real problem because I normally wear shoes that you don't have to take off whenever I'm like, you know, TSA PreCheck, whatever. But you think, you think all of these things through, and then suddenly you're holding a cat, the bag is in the middle of the scanner, and like, sorry, something's up with your shoes, can you take them off? And I'm like, you're asking me to remove boots right now while I'm holding a cat in TSA? Oh no. Um, but you know.

Leo Laporte [01:45:34]:
So a couple more stories before mom gets here. Mathematicians some grief. There was a very good Daily episode with a mathematician. Did you listen to it, Jeffrey?

Jeff Jarvis [01:45:46]:
I've never listened to an entire Daily Show.

Leo Laporte [01:45:48]:
This one's worth listening to because he bursts into tears.

Jeff Jarvis [01:45:51]:
Tears? He's crying. There's no crying in mathematics. He's weeping.

Leo Laporte [01:45:56]:
He's not weeping over math. Well,

Jeff Jarvis [01:45:58]:
he is.

Leo Laporte [01:45:59]:
Well, he's sort of. He's weeping over life in general, but But it's actually a— what's his name? Strogatz? Strogalt? He's a really good guy about math.

Jeff Jarvis [01:46:09]:
Strogalt.

Leo Laporte [01:46:10]:
Yeah, he talks about math. He's very smart, very good. He says, look, we're gonna have to get used to it. Math is the latest thing that AI can do very well. And OpenAI has shipped off a bunch of math results, 722 manuscripts on Tuesday, 372 groups of findings on longstanding math problems.

Jeff Jarvis [01:46:36]:
722. Out of 4,000, I think they tried, right?

Leo Laporte [01:46:39]:
One Rutgers mathematician said the results connected to the longstanding Riemann hypothesis would warrant an automatic Fields Medal, the Nobel Prize of math, if a human had done the work. Now, I should say that's a hot take. And one of the things the math community always says is, look, this is going to take us years. to look at these proofs and figure them out. But one of the things the professor said— gosh, I gotta get his name now because I feel bad I'm not giving him full credit— is that it's at some point the AI is going to make proofs, show us the proofs, and we won't be able to understand it. He said it's like—

Jeff Jarvis [01:47:18]:
Stephen Strogatz.

Leo Laporte [01:47:19]:
Strogatz. He said it's like, uh, if I try to explain calculus to my dog, There's no way the dog is ever going to get it. And he believes that AI is rapidly coming to the point where they're going to have math, you know, proofs that humans can't understand.

Jeff Jarvis [01:47:38]:
There's a lot we don't understand. I mean, my example of this is we didn't understand how— why aspirin worked for years. We used it. We tried it. The thing that got me about the— what I did listen to in the interview was how egocentric it was, him saying, we thought we'd be heroes. When we solve this problem, we are the hero, right? Right. But that's not to me why an academic is an academic. An academic wants greater knowledge in the world.

Jeff Jarvis [01:48:05]:
And rather than celebrating, in the part I heard, that there's, wow, we can solve any more things, we can put more challenges to it, isn't this amazing? It's that I'm losing personal status. And that struck me as very egocentric.

Leo Laporte [01:48:19]:
There's also a legit— I think a somewhat legitimate point to be made that— Vi Hart made this point. I love Vi Hart. I don't know if you know who she is. She does— I don't know if this is that Vi Hart, so don't show her yet. She has— had a YouTube channel where she did— it was great. She had a notebook and she would explain math concepts as she was drawing These things. It's just, she's just wonderful. Anyway, she is a mathematician.

Leo Laporte [01:48:49]:
In fact, she worked at, I think it was Microsoft, on the Lean program, which was designed to support proofs, to analyze mathematical proofs to make sure that they were real. And she says Lean is supporting this Navier-Stokes proof. So she thinks that's relevant, but she talks about this. And one of the things she says is, you don't climb a mountain because there's a pot of gold on the top of the mountain. We don't solve these math problems just to solve them. We solve them because the journey teaches us something. The climb is important. And when an AI solves it, eliminating that process.

Leo Laporte [01:49:37]:
You don't get the lessons learned getting to the answer. So she says, you know, and Stephen Wolfram kind of says the same thing. He said at a talk he gave this week, theorems, schmear 'em. You can make up a lot of theorems. Doesn't mean they're useful. So the math community itself is divided somewhat on this.

Jeff Jarvis [01:50:00]:
There's also further reporting that the apparent theft of the process from the NYU professor, there's some debate because they did dig into the proof and say, well, actually, it came from a slightly different path. So there's argument about that too.

Leo Laporte [01:50:17]:
So it did its own work. Yeah. So I don't know what to say about this. Coding has, I think no one would doubt this, fallen to AI. Chess fell.

Jeff Jarvis [01:50:29]:
I didn't see any coders crying.

Leo Laporte [01:50:32]:
Oh, there are still coders crying. There are a lot of people very sad sad that the thing they spent years in school learning and many more years perfecting in business and their work is now less valuable because a machine can do it. I think there's definitely some grief. He said, Strogatz said, we're at different stages of denial of the grief. Yeah, he said that. Yeah, I thought that was kind of interesting.

Jeff Jarvis [01:51:00]:
What's fascinating to me is we I thought that the jobs that would get eliminated by this technology are, you know, accountants and factory workers and blah blah blah. Mathematicians was not on my bingo card.

Leo Laporte [01:51:11]:
All right, but first they came for the chess players, and I didn't say anything because I wasn't a chess player. Then they came for the Go players, and I didn't say anything because I wasn't a Go player. Then they came for the coders, then they came for the mathematicians. Eventually, I mean, this is It's going— if you're an accountant, I would very much— if you're a bookkeeper, let's say, maybe not a CPA, but maybe. If you're a lawyer, we're seeing very strong models. Actually, there's a new open weight legal model from GLM. GLM is the model I use called GLM Legal. And of course, we talked before about the Thomson Reuters model.

Leo Laporte [01:51:52]:
I'm sorry.

Jeff Jarvis [01:51:53]:
Did you see Darren Oakey on Paris going through the airport?

Leo Laporte [01:51:56]:
Oh, is she holding a cat? Yes, she is. Oh, Darren, it was only a matter of time before AI, then the artists came. That's

Paris Martineau [01:52:07]:
great.

Leo Laporte [01:52:08]:
I think that's, uh, yes, that's very funny. See, now I have to say something. Look, Darren is not capable of making this illustration, but he is capable of thinking of a clever premise for this illustration.

Jeff Jarvis [01:52:20]:
Yes, which mathematicians should do too.

Leo Laporte [01:52:22]:
There are places for the human. I mean, if you just said Paris going through an X-ray with a cat, you would get a pretty anodyne picture, probably like the one on the left. You wouldn't get the X-ray of the cat as with devil horns.

Paris Martineau [01:52:37]:
I'm attaching a photo of Gizmo.

Leo Laporte [01:52:42]:
Yes, we want to make it more accurate. He knows what Paris looks like, but he doesn't have Gizmo quite right.

Paris Martineau [01:52:49]:
I mean, this won't help for the photo of me holding Gizmo.

Leo Laporte [01:52:52]:
What's mom's ETA? How close are we getting?

Paris Martineau [01:52:54]:
I mean, she hasn't responded. Potentially within 10 minutes. She could be coming up.

Leo Laporte [01:52:59]:
She could be on the stoop right now.

Paris Martineau [01:53:00]:
She could be on the stoop right now. I feel like— I mean, no, that's the issue is my apartment is weird and busted where she cannot buzz and I have to go outside to let her in, basically.

Jeff Jarvis [01:53:11]:
Well, she could, but, but okay, we'll wait because we want to talk to her.

Paris Martineau [01:53:15]:
I don't think I can, uh, just force her on the podcast. Oh,

Jeff Jarvis [01:53:20]:
let

Leo Laporte [01:53:20]:
me tell you, getting my mom on the podcast was a multi-day—

Paris Martineau [01:53:23]:
My father really wants to be on the podcast. Oh, he was waiting outside the door. I still feel bad about that. I'm so sorry, Dad.

Leo Laporte [01:53:33]:
Dad, well, maybe we should Zoom him in.

Paris Martineau [01:53:36]:
I mean, we should.

Leo Laporte [01:53:38]:
I'd be happy to,

Paris Martineau [01:53:39]:
but no, we'll, we'll see. Maybe, uh, Um, over the holidays.

Leo Laporte [01:53:42]:
We all want to know more about Paris. We're all looking to get those, those little details that only a parent can share.

Paris Martineau [01:53:48]:
I mean, yeah, my mom says he has AI psychosis, so you guys could bond over that.

Jeff Jarvis [01:53:52]:
Yeah,

Leo Laporte [01:53:53]:
you see, you see, it's not just me.

Jeff Jarvis [01:53:57]:
Um, what did you think about— okay,

Leo Laporte [01:54:00]:
no, no, what did I think? Go ahead, you do it.

Jeff Jarvis [01:54:03]:
Did it— is it democracy? Ukrainian? No, it's not. Did the Ukrainian attack on the Yandex—

Leo Laporte [01:54:08]:
I love that. That was my last story And I will, let's do that as our last. I have a lot of silly stories. And I'd actually been reading this last week. Ukraine bombed Yandex data centers in Russia with drones. And interestingly, and I think people on X have confirmed this, suddenly the bot traffic on X disappeared.

Jeff Jarvis [01:54:34]:
Now there's an argument as to whether or not it was that the bots were resident there or that because Yandex was not available as a, as an access point. But I don't, I don't understand the difference.

Leo Laporte [01:54:45]:
Cloudflare reports that bot traffic from the Yandex network has dropped to just 0.4%. It dropped 57% on October 8th when the first Ukraine drone hit a Yandex data center housing 2 supercomputers in Sasovo, Russia. A second bigger Yandex campus 220 miles to the west was knocked offline Uh, if this says yesterday, so October 10th, not, not yesterday, or yesterday, uh, and now it is 0.4%. That's like 99%.

Jeff Jarvis [01:55:22]:
Everybody shared an Elon Musk tweet that had hardly any likes to it.

Leo Laporte [01:55:27]:
Oh, see, that's what's going to be interesting.

Paris Martineau [01:55:29]:
I didn't even think of that.

Leo Laporte [01:55:32]:
So on X, the everything app,

Jeff Jarvis [01:55:35]:
Where you do your—

Paris Martineau [01:55:36]:
where you do all your banking and finance.

Jeff Jarvis [01:55:38]:
Banking and finance, yes.

Leo Laporte [01:55:39]:
You know, they had a pretty good, uh, APR on the savings, so I put some money in there. I thought that's a good place for a savings account.

Paris Martineau [01:55:47]:
Of course.

Leo Laporte [01:55:48]:
I haven't done anything else with it.

Paris Martineau [01:55:50]:
You just put money there that you hope will be there, unlike the last time Elon Musk tried to make X.com the site for all your banking and finance.

Leo Laporte [01:56:02]:
But did people lose money?

Paris Martineau [01:56:04]:
Isn't that what happened? Didn't it fail?

Leo Laporte [01:56:08]:
Oh, I don't— I think that would be a big story. Speaking of failing—

Paris Martineau [01:56:11]:
No, this was decades ago.

Leo Laporte [01:56:12]:
Oh, decades ago. Oh, the original— The original website. Oh, before PayPal.

Paris Martineau [01:56:16]:
The homeboy.

Leo Laporte [01:56:17]:
I don't know the story of that, actually. Yeah. But speaking of failing, Character AI has filed for bankruptcy. This was the platform where you could, you know, talk to personas and they were getting sued to death. Um, lots of, uh, lots of issues, a slew of lawsuits. Uh, this— the company said it had assets and liabilities between $50 and $100 million. They, they've really tried, they say, um, you know, to make Character AI safe for young people, but apparently it's, uh, it's only 5 years old. It's founded by former Google engineers.

Jeff Jarvis [01:56:53]:
Yeah. But one of the authors of the Transformer They're selling assets to Disney for $20 million.

Leo Laporte [01:57:01]:
Okay.

Paris Martineau [01:57:02]:
There was a— the people over at Jason Kobler over at 404 Media got their bankruptcy documents from the Delaware court and basically found that it never even came close to making money in the entire time of its existence. I mean, it was spending, you know, at some point, like, $13 million a month on AI compute or $10 million or $9 million. And the most it ever made was $2.8 million. So, you know, multiples lower than the amount it says it's spending on compute. Who were the major investors in it, I wonder? That's a great question. Who were the major investors? Yeah. Who is going to be— who's left holding the bag?

Leo Laporte [01:57:52]:
Yet another—

Paris Martineau [01:57:53]:
Andreessen Horowitz, Lightspeed Venture Partners.

Leo Laporte [01:57:56]:
The usual.

Paris Martineau [01:57:57]:
Raycroft, Nat Friedman, Elad

Leo Laporte [01:58:01]:
Gil. Another literary prize winner seems to be AI according to The Atlantic, the prestigious Caine Prize.

Jeff Jarvis [01:58:07]:
So let me stop you here for one second because I got in a little trouble for this. The— I don't like prizes as a rule because I think they're exclusionary. And so when this came up, I said, weird that AI could kill the prizes. This is a prize for Black talent in Africa.

Leo Laporte [01:58:26]:
That would be terrible.

Jeff Jarvis [01:58:28]:
So that would be bad. Uh, and so I picked, I picked on the wrong prize to say that. But the other interesting thing is that the attacks then on winners of prizes who are doubted because of AI tend to be people of color who are the, who are the objects of those witch hunts.

Leo Laporte [01:58:46]:
All right. Well, you know, let's— because you cannot determine authoritatively the provenance of something, let's not cast aspersions at this point.

Jeff Jarvis [01:58:54]:
A. B, again, I think prizes are exclusionary. Only one wins. And that's the problem with them. I want a wider, wider pipe. Leo, have you checked behind your monitors at home to see if you have any toilet paper wrapped things?

Leo Laporte [01:59:06]:
That's a story. Go ahead. Tell us that story.

Jeff Jarvis [01:59:10]:
You're the one who spotted it. You tell the story first.

Paris Martineau [01:59:12]:
I mean, I'm trying to find the link right now. Hold on here. OpenAI tweeted yesterday that it had addressed basically a security incident going on with one of its employees and that more information would soon be available from the DOJ. Basically, what ended up happening was that an OpenAI engineer was arrested in bugging an executive's conference room.

Leo Laporte [01:59:41]:
Where secret discussions were held.

Jeff Jarvis [01:59:44]:
It's a special conference room where the big boys met.

Paris Martineau [01:59:48]:
He basically was arrested in suspicion of hiding an audio recording device in this conference room where executives were meeting, having kind of top secret or at least like really sensitive meetings. They had kind of pinned this or tracked this back to him, Alfonsan, who worked as an engineer at OpenAI since last July.

Leo Laporte [02:00:14]:
There was video of him coming back to retrieve it.

Jeff Jarvis [02:00:18]:
Allegedly.

Paris Martineau [02:00:18]:
So it was—

Leo Laporte [02:00:19]:
No, that's not it, by the way. This is a fake. This is a fake, I say, a fake. That was Dario Mode looking behind the TV in the OpenAI conference room. Why did he wrap it in toilet paper? That seems to be kind of—

Jeff Jarvis [02:00:33]:
That's what I can't figure out. It's not exactly as if that's good camouflage at a conference room. It's

Leo Laporte [02:00:37]:
not a red flag, but it's a white flag.

Paris Martineau [02:00:39]:
It was plugged into— the device was plugged into a USB port in the back of the television and then wrapped in toilet paper for some reason. OpenAI personnel found it on September 30th, but the company determined after they analyzed it that it had been installed just 2 days earlier and its content content were encrypted. Here's where it gets kind of interesting, though. The OpenAI and prosecutors have said in statements that, quote, a chatbot tied to Alphazon's spouse, which shared identifiers with his own account, submitted prompts between September 24th and September 30th about building, installing, obscuring, and remotely accessing a recording device, including how to build a continuous audio recorder and encrypt the SD card. And then on September 27th and 28th, right before it was allegedly installed, this account, which belonged to this OpenAI employee's spouse, asked about the specific device that was later found, including its unique MAC address. So one, it seems like These people presumably were using ChatGPT to try and spy on ChatGPT.

Jeff Jarvis [02:01:58]:
And then they bragged about how ChatGPT nailed them.

Leo Laporte [02:02:02]:
This is really pretty much—

Paris Martineau [02:02:03]:
I mean, it would be pretty easy to nail, I guess.

Leo Laporte [02:02:05]:
The story from beginning to end. Yeah, I do love the deepfake videos we're getting. Here's yet another one. This is, uh, Dario Amodei sneaking in to wrap— put the toilet paper-wrapped microphone into the TV. And here comes Sam Altman with his security outfit on, and he's got a bug scanner. And I think Sam has detected—

Jeff Jarvis [02:02:29]:
Resting Sam face.

Leo Laporte [02:02:32]:
It came in a plastic bag.

Jeff Jarvis [02:02:34]:
It's a $14 item.

Leo Laporte [02:02:36]:
And now for some reason, Sam's in a tuxedo and he's wrestling Dario Amodei to the conference room table and he's throwing him through the glass And oh my God, the humanity. Uh, yeah, I think that's probably exactly how it happened. I'm just saying. All right, last break and then picks of the week and then Mom. You're watching Intelligent Machines with the wonderful Paris Martineau of Consumer Reports fame, Jeff Jarvis.

Jeff Jarvis [02:03:07]:
I envision Mom out there and people say, do you need help, madam? Is there a problem? You're Well, my daughter is on a podcast.

Leo Laporte [02:03:13]:
She's on a podcast. She's on a podcast.

Jeff Jarvis [02:03:17]:
I'm so proud of her.

Leo Laporte [02:03:21]:
Okay. Yeah.

Jeff Jarvis [02:03:22]:
Sorry, we're doing an ad.

Leo Laporte [02:03:27]:
Sorry. I actually was trying to remember a joke, but I forgot it. I'm not good at joke telling. Any word from mom?

Paris Martineau [02:03:36]:
She will be arriving within minutes.

Leo Laporte [02:03:39]:
If you have to leap to your feet and get Go right ahead.

Paris Martineau [02:03:43]:
She's not being very communicative. Oh, she's text— she's typing. 6 minutes.

Leo Laporte [02:03:48]:
Okay, good. You have just enough time.

Paris Martineau [02:03:49]:
I've got just enough time for your pick of the week, and I don't know which one to pick, honestly. I guess I'll do this one quick, which is that Nic Cage this week said he's probably not working with Amazon again. Why? Because of AI.

Leo Laporte [02:04:04]:
What?

Paris Martineau [02:04:06]:
Um, he said at Comic-Con Amazon's a very AI-friendly company to the tune of $50 billion invested in OpenAI, and I'm not an AI-friendly actor. So it's like, connect those dots. I'm not a member of their club. I didn't sign the waiver to let them use AI on Spider-Noir, and I'm probably not going to be working with that studio again. But that's okay.

Leo Laporte [02:04:28]:
They did cancel Spider-Noir. That might also have something to do

Paris Martineau [02:04:31]:
with it. Cancelled it right before it won, or it got 11 nominations at the Emmy Awards, which is the most of any Prime Video program this year. And It amassed 2.6 billion minutes viewed in its first 6 weeks and got praise from critics and audiences alike.

Leo Laporte [02:04:48]:
Watch the black and white version, because I really love the noir black and white of it. I think it's great.

Paris Martineau [02:04:53]:
I really liked that Cage kind of followed this up by saying, though, I've got to get with these young directors who are Gen Z. They're going at it their own way, whether it's their YouTube channel or whatever it is. They're doing it from scratch and by hand. And I want to get with them.

Leo Laporte [02:05:06]:
Good for you, Cage. It sounds like do a True Detective, which would be great.

Paris Martineau [02:05:10]:
Yeah. He also said he might be doing True Detective, which would be interesting.

Leo Laporte [02:05:14]:
Well, we love Nic Cage, and whatever you decide to do, Nic, all is forgiven.

Paris Martineau [02:05:18]:
A hyper-local pick, I will also say, if you're in the New York area and you hear this before the weekend, Nighthawk is showing Vampire's Kiss at Prospect Park on Saturday. Another classic. Which is a phenomenal Nic Cage film.

Jeff Jarvis [02:05:33]:
What are we all seeing this weekend though? What do we all have to see this weekend? What's opening tomorrow?

Paris Martineau [02:05:38]:
Oh, what I'm seeing tomorrow is, um,

Leo Laporte [02:05:41]:
no,

Paris Martineau [02:05:42]:
you can see everything.

Jeff Jarvis [02:05:43]:
You can see everything.

Leo Laporte [02:05:44]:
Is that tomorrow?

Paris Martineau [02:05:45]:
I mean, I think it, I think it opens, uh, globally or nationwide on Friday, but I'm seeing it tomorrow. They're doing like—

Leo Laporte [02:05:53]:
Did you see Andrew Wang's video takeoff of that movie with the muse, fluffy muse creatures? No. Yes, I won't play it. It was funny. All right, my pick of the week, Sorry, I'm in a Meeting. Uh, this is a— yes, it's an AI website, uh, In a Meeting, iminameeting.com, where you get, they say, plausible meeting audio to put behind your phone call. What, what are you in? Are you in an IT incident?

Jeff Jarvis [02:06:26]:
This has been around.

Leo Laporte [02:06:28]:
Oh, is Is it?

Jeff Jarvis [02:06:28]:
Yeah.

Leo Laporte [02:06:30]:
Well, it's the first time I saw

Jeff Jarvis [02:06:32]:
it.

Leo Laporte [02:06:32]:
Is this really old?

Jeff Jarvis [02:06:34]:
Yeah,

Leo Laporte [02:06:34]:
because I don't know what this is, but it's AI generated.

Jeff Jarvis [02:06:37]:
Oh, well, that's what's new about it.

Jeff Jarvis [02:06:39]:
Okay, so here's what I'd suggest,

Leo Laporte [02:06:41]:
because you can

Jeff Jarvis [02:06:42]:
add your name to it. 2 clinics right away. Ah,

Leo Laporte [02:06:46]:
so you can really personalize it. It's not very good.

Jeff Jarvis [02:06:50]:
No, honestly, your call, but I'd pick your—

Leo Laporte [02:06:53]:
I guess if we're in the background as you were talking, Brenda complains about— maybe it would sound real.

Leo's Laptop Audio [02:06:57]:
That's Dayton. That's easy.

Leo Laporte [02:06:59]:
To me, it sounds pretty AI. Yeah, so it's new. New, not old.

Benito Gonzalez [02:07:05]:
They need to— all right, well, you don't like that? You're muted. You need to unmute. They need to throw that in there.

Leo Laporte [02:07:09]:
That would be more— yeah, to the— and, uh, all right, I'll give you one more. This is really cool. I have already installed it. It's very interesting. It's— remember we were talking about ArtCraft, those, uh, vibe-coded reverse-engineered Adobe tools? Well, get ready. There's going to be a lot more. There's a new skill out for AI called REA, Reverse Engineer Anything. It combines a bunch of tools that have been around for a while, including Ghidra, which is a decompiling program from the NSA, plus some other really useful tools.

Leo Laporte [02:07:43]:
But in the past, you've needed to know what you were doing to decompile a program and reverse engineer it. But now these tools are put together. It gives your agent the information it needs, plus the reverse-engineered code, and it makes it possible for you to reverse engineer anything. And I think it's pretty— By software. Yeah, I think it's really, really interesting. R-E-A, reverse engineer anything. All right.

Jeff Jarvis [02:08:11]:
All right, first, first, before Paris leaves, as a service to Benito, can the 3 of 4:35 PM, all systems green. Okay, thank you, thank

Benito Gonzalez [02:08:26]:
you.

Leo Laporte [02:08:26]:
That was Ripley, my security officer.

Paris Martineau [02:08:29]:
And let my mother in my building.

Leo Laporte [02:08:30]:
All right, Paris, thank you. Paris Martineau, everybody.

Paris Martineau [02:08:33]:
Bye, sorry to leave early.

Leo Laporte [02:08:35]:
Reports. Bye, say hi to Mom.

Jeff Jarvis [02:08:39]:
So I watched Cupertino. You watched Cupertino last week?

Leo Laporte [02:08:42]:
Uh, it came out, didn't it? I did not.

Jeff Jarvis [02:08:44]:
It did come out. Yes. It's—

Leo Laporte [02:08:45]:
what did you think?

Jeff Jarvis [02:08:46]:
I liked it. It's from the creators of The Good Wife, which is a great show, Fight, which were great. So this is 2 Silicon Valley attorneys who get fucked up and they fight back in the first episode. Uh, and we'll see how it goes from there, but I liked it. So Thursday nights on CBS. That's one.

Leo Laporte [02:09:04]:
I haven't watched network television in quite some time.

Jeff Jarvis [02:09:06]:
I haven't either. I haven't either. I had to record it. Yeah.

Leo's Laptop Audio [02:09:08]:
Cusco, and 5 fleet approvals are pending in Buzz. No issue.

Jeff Jarvis [02:09:13]:
That's your agent talking to you.

Leo Laporte [02:09:14]:
That's Ripley.

Jeff Jarvis [02:09:16]:
And then my favorite thing of the week is Zuck's Guinness face.

Leo Laporte [02:09:21]:
Oh yeah, when he takes a sip of Guinness.

Jeff Jarvis [02:09:24]:
And so he takes a sip of Guinness. Play the video. No sound. You don't have to play sound.

Leo Laporte [02:09:28]:
This actually is a podcast which has a clever premise, which is you sit in a fake pub. Here he is. There he is with Andrew Wang. And that's Mark taking his first sip. Whoa, that's pretty strong stuff, says Mark. Actually, it's a good interview.

Jeff Jarvis [02:09:50]:
So then Mark responded to that, to that post and just said, that's just my face.

Leo Laporte [02:09:56]:
He might actually— I wouldn't be surprised if he likes Guinness. He says he had 2 beers during the interview.

Jeff Jarvis [02:10:02]:
So what's the podcast?

Leo Laporte [02:10:03]:
Uh, it's a pub. What's it called? Pub? I can't remember.

Jeff Jarvis [02:10:08]:
Um, so it's a fake pub?

Leo Laporte [02:10:11]:
Well, I'm assuming it's a fake pub. I don't know if you'd be able to take over a pub and do a podcast there like that. Anybody know the name of the podcast? It was a good video. I watched it because, I mean, it's a plug for Muse. That Andrew Wang is the— what is he, 23, 24-year-old that they hired for billions of dollars? Who made Muse effectively. And Mark Zuckerberg, I think, has something to do with Facebook. It's a pretty good interview. They, they, you know, making a pitch.

Leo Laporte [02:10:41]:
This is why I don't interview people like that, because they're too well trained. They're never going to say anything but plug the product. Somebody like our great guest, Martin Woodward, he's just great. He's real. He's real. Yeah. Yeah. Jeffrey, it is time for us to say good night to all our family.

Leo Laporte [02:11:00]:
Oh. Jeff Jarvis, professor— well, he was at CUNY. He's now doing curricula for Montclair State University in Montclair, New Jersey, and SUNY Stony Brook. Are you going to ever take to the classroom again? Are you going to ever—

Jeff Jarvis [02:11:14]:
I don't think a full class. I don't think so.

Leo Laporte [02:11:16]:
Maybe a little seminar?

Jeff Jarvis [02:11:18]:
I talk to classes and I volunteer. When people, you know, if anybody teaches my book, Hot Type, I'm happy to Zoom into the class. I do that. I've done that a few times.

Leo Laporte [02:11:26]:
The book Hot Dime is available at jeffjarvis.com. There are even some signed copies.

Jeff Jarvis [02:11:30]:
Signed copies from the Montclair Book Center. You can find the link at my redesigned page, jeffjarvis.com.

Leo Laporte [02:11:36]:
And he is working on a new series of books about AI. How's that going?

Jeff Jarvis [02:11:40]:
Going well. We've signed 4 writers so far, and I think we're about to sign 2 more.

Leo Laporte [02:11:47]:
And what is the premise of this series?

Jeff Jarvis [02:11:50]:
The premise is that this is not a technological series. It's not technical. It is inviting writers from many disciplines to reflect on AI and how AI reflects on society. That's interesting. So the first, Rumen Chowdhury, who we had on, was writing about what is intelligence. Matthew Kirshenbaum, who we've also had on, about the techpocalypse. The author of Black Software, Charlotte McIlwaine, is writing an optimistic take on AI and Black technologists. We just signed another book, from, I think we're signed in, I think I can say this, Jonas Ivarsson from Sweden, about what happens when AI makes life too damned easy, takes away all the friction.

Leo Laporte [02:12:31]:
Wow.

Jeff Jarvis [02:12:32]:
Which is a great idea. Another book I'm hoping to be able to sign is about taking back the definition of progress from the technologists.

Leo Laporte [02:12:42]:
Oh, I love that.

Jeff Jarvis [02:12:43]:
So really kind of thoughts like that. I mean, I thought of another idea today is I want AI in war. We need AI and Doom. Oh

Leo Laporte [02:12:48]:
yeah,

Jeff Jarvis [02:12:49]:
AI and God, AI and creativity. So that's, that's the kind of what it is. It'll be books that are independent books as part of the series to reflect on, um, all these changes that are coming to society through AI. Thank you for the plug.

Leo Laporte [02:13:00]:
We live in an amazing world, and that's what we cover. This show is more of our philosophical look at AI. Some of it's what's happening, but a lot of it's about the philosophy.

Jeff Jarvis [02:13:10]:
We need to be contemplating skulls on

Leo Laporte [02:13:13]:
AI and Society. We also do the AI User Group, which is much more down and dirty and doing stuff and using stuff. And that's always a lot of fun. We're now doing that. We're going to try to do that twice a month. So watch that if you're a club member, you'll get an announcement automatically.

Jeff Jarvis [02:13:30]:
And then Micah's show.

Leo Laporte [02:13:32]:
Oh, and Micah's doing Hands-On AI. Yeah, it's, that's really good because he's talking about You know, I am in some sort of weird AI rabbit hole, but he's talking about using AI in a reasonable fashion. Now, I'm going to be gone for 3 weeks. Who is hosting the show, Benito? I don't even know. Mr. Lou Maresca. Lou Maresca. Maybe, maybe not.

Leo Laporte [02:13:56]:
We weren't sure.

Benito Gonzalez [02:13:56]:
No, no, Lou's next week. Yeah. Lou's next week.

Leo Laporte [02:13:58]:
Lou's definitely next week. He, of course, does AI Copilot for Microsoft. He's the head of engineering there. Jason Heiner, who's the editor-in-chief of The Deep Review, The Deep View, which is an AI journal. And our good friend Mike Elgin, he has his Machine Society newsletter. He's the one who might not be able to make it. Is that right?

Benito Gonzalez [02:14:17]:
Yeah, I'm still— we're still working that out. Yeah, a scheduling issue. Yeah,

Leo Laporte [02:14:23]:
yeah. Uh, we'll have Mike on no matter what anyway. Actually, all 3 of them are great. We've had them on the show before. We will have them on again. I will be back though, uh, 11/11. That's easy to remember. I might be a little jet-lagged because I'm flying in the night of the 9th, but we'll see.

Leo Laporte [02:14:39]:
And, and you can ask me then whether I pulled the trigger on a 512 gigabyte, very, very expensive— we don't even know how expensive. If you have to ask, you can't afford it— Mac Studio. Whoa. I haven't decided. I'm going— I'm, I'm just like teetering.

Jeff Jarvis [02:14:58]:
Don't you want to wait for the— want to wait for the Vera Rubin?

Leo Laporte [02:15:02]:
Well, yeah, but it's not— you know, with computer hardware, waiting is sometimes a bad idea. It's so hard to tell. Actually, this is something I should have talked about more on the show. I've been talking about it on other shows.

Jeff Jarvis [02:15:15]:
What can't you do now that you could do with that?

Leo Laporte [02:15:17]:
Uh, it would just give me one more machine with a large pool of memory. The biggest pool of memory I have is 256 gigs on those Sparks. Uh, it's probably not as good for inference as the Sparks. The Sparks are built for inference. But it does have that large pool of memory. So my goal from day one with this was I want to build something that is a useful assistant for me in my life, especially as I age and my memory goes. But I also want, as far as possible, to do it locally. And I probably have enough horsepower now.

Leo Laporte [02:15:57]:
I have 8 total models running on 4 different machines right now to not use the cloud except for coding. Coding, it's still— you still need the cloud. Still not good enough. But I want to be in position with a large enough machine that if in the next year I'm convinced there will be models that will fit in 512 gigs, and it may not fit in less, but I'm convinced there'll be models that will fit in 512 gigs that will be as good as, let's say, Opus 5.5 today. That's probably 8 months out. I want to be able to use that model when it comes out and I feel like nothing I have right now will be big enough. I also am concerned that it'll get harder and harder to get this stuff. So yes, if there's a Spark 2 with Vera Rubin in it, I will kick myself.

Leo Laporte [02:16:43]:
Actually, I won't because I will be able to sell that Mac for probably exactly what I paid for it, if not more. So that's the other thing. You know, there's a lot of question marks. I've been doing a lot of benchmarking and trying to understand where the world is headed. Between now and the time I come back, models will have come out from Z.AI. There will be GLM-5, which should be amazing. There will be Quen-4 from Alibaba, which should be amazing. Very powerful open weight models.

Leo Laporte [02:17:18]:
I am, you know, day after tomorrow, no, tomorrow, we're going to see the first open weight model in the new Neutron family from NVIDIA. And I have very high hopes for what Jensen Huang and NVIDIA will do. They have been acquiring and partnering with very good companies and creating local models.

Jeff Jarvis [02:17:35]:
We're looking to acquire Reflection.

Leo Laporte [02:17:39]:
Yep. And they just acquired another company. So I think by the time I come back, there will be some strong NVIDIA models as well. And of course those will run best on Sparks or, you know, what I'm hoping really is that Jensen Huang knows that Apple is about to drop this Mac Studio and is gonna make an announcement before that happens.

Jeff Jarvis [02:17:59]:
It's just hard to get memory.

Leo Laporte [02:18:02]:
Well, and that's what I've been talking about the other shows. I think that's a conspiracy. I really don't believe that. I think there's a lot of memory. There's an infinite amount of memory because most of those data centers that the memory is committed to have not been built. True. Most of them will not be built.

Jeff Jarvis [02:18:17]:
True.

Leo Laporte [02:18:18]:
And I think that's another thing to watch carefully is the November election because many, many candidates are running for election that are anti-data center. And if they win, then even more data centers will not be built. Even more memory that is committed now and is not available now will suddenly hit the market. I predict it's very likely in 6 to 8 months there will be a memory glut and a plummet in the price of memory. I think these companies are gouging right now. I think that they, they don't— they could sell the memory for a lower price. The memory's there, it's not been used, right? Where's all that demand coming from?

Jeff Jarvis [02:19:03]:
Yeah.

Leo Laporte [02:19:05]:
So I, I think we're— should— I think shenanigans. I think there's shenanigans going And, uh, and that's why I'm very nervous about everything. I, I made the right move to buy the Sparks when I did. I think that's— I was— I don't want to say I was prescient. I was lucky in buying them when I did, and I'm very happy with them. Um, but I'm not sure what the next move is. So we'll all have answers for that, I bet you, in a month. 11/11, I'll be back.

Leo Laporte [02:19:32]:
We'll talk. Thank you, Jeff. Have a wonderful month without me.

Jeff Jarvis [02:19:36]:
Are you going to be talking to your agents from around the world? Did Lisa set any rules? No,

Leo Laporte [02:19:43]:
she doesn't even— they talk in my hearing aids. She can't tell. Actually, the other day she said, is somebody talking to you? I said, can you hear that? She said, yes. I said, oh, so maybe she will know. I'm gonna, I'm gonna take a little break. I think we have some really exciting stuff to see. We're going to Southeast Asia. I think I'm going to have a lot of fun and probably will not spend much time with my agents.

Leo Laporte [02:20:08]:
They won't be lonely though. They'll find stuff to do. Thank you everybody for joining us. We do Intelligent Machines every Wednesday, 2 PM Pacific, 5 PM Eastern, 2100 UTC. You can watch it live as we do it if you're in the club, in the Club Twit Discord, YouTube, Twitch, x.com, Facebook, LinkedIn, and Kick. We stream on all those platforms. Of course, you don't have to watch live. We record it.

Leo Laporte [02:20:30]:
Amazing. And so you can download the audio or video of every show from our website, twit.tv/im, or go to YouTube. There's an Intelligent Machines channel on YouTube. You can watch any show there, all the video. Uh, or best thing to do, probably subscribe in your favorite podcast client. That way you'll get it automatically, audio or video, and you can listen whenever you're in the mood. I do appreciate your listening. Thank you for being here, and we will see you next time on Intelligent Machines.

Leo Laporte [02:20:58]:
Bye-bye. Bye-bye. I'm not a human being.

Jeff Jarvis [02:21:03]:
Not into this animal scene. I'm an intelligent machine.