Latest / Connor with Honor: AI, Real Estate and Real Life / 1984 I was copying Basic Code out of a magazine, look at what is happening now!!!

Transcript

1984 I was copying Basic Code out of a magazine, look at what is happening now!!! · Connor with Honor: AI, Real Estate and Real Life

• 0:00
  SPEAKER_00: Welcome.
• 0:00
  SPEAKER_00: Let's talk a little bit about artificial intelligence.
• 0:03
  SPEAKER_00: Back in 1983, I would have had no idea how far it would have gone to this point and how fast we would have been here.
• 0:09
  SPEAKER_00: Now, at 57 years old, back then I was 13, 14 years old.
• 0:13
  SPEAKER_00: I remember my dad bought a Timex computer, a Sinclair TI-99.
• 0:20
  SPEAKER_00: And that was my first experience in learning how to program.
• 0:24
  SPEAKER_00: Self-taught, right?
• 0:25
  SPEAKER_00: No college.
• 0:26
  SPEAKER_00: Well, what do you know at 13 or 14 anyway?
• 0:28
  SPEAKER_00: But, you know, fat kid, kind of nerdy, and uh picked it up.
• 0:31
  SPEAKER_00: Not that you have to be fat, but anyway, so I started programming out of a magazine, Compute Made or Computer World.
• 0:38
  SPEAKER_00: I don't remember what the magazine was, but they had programs in there for games.
• 0:41
  SPEAKER_00: And what kid didn't like games?
• 0:43
  SPEAKER_00: Was this pre-Itari?
• 0:45
  SPEAKER_00: Probably right about the same time as Atari, but you know, yeah, video game's definitely an arcade, so that was kind of a thing.
• 0:51
  SPEAKER_00: If I could replicate that on a computer that was in my house, well, so be it.
• 0:56
  SPEAKER_00: And it was uh it was interesting.
• 0:58
  SPEAKER_00: It had kind of a plastic covered keyboard, they weren't scissor keys, they were kind of more pressure-sensitive keys placed over a plastic membrane, not a very big computer, something like this, so that wasn't typing style keyboard, but it was formulated like that.
• 1:16
  SPEAKER_00: So it wasn't the but it was very small, so you didn't have good finger placement or any of this, but not that I knew how to type yet, at least at that time.
• 1:24
  SPEAKER_00: But I would copy the programs out of the magazine into the computer.
• 1:29
  SPEAKER_00: I had a cassette tape deck, you know, cassette, the rectangular cassette tape decks, look it up if you don't know what that is, and then I would put a cassette tape in there, and then from the computer itself, I would be able to save the program if it worked.
• 1:44
  SPEAKER_00: I'd be able to save it on the cassette tape.
• 1:46
  SPEAKER_00: And then I could in the future, because it had very limited memory, in fact, for an extra hundred dollars, my dad bought a 16k memory that you plugged to the back.
• 1:57
  SPEAKER_00: 16k, 16,000.
• 2:00
  SPEAKER_00: Most phones are up to a terabyte, so even beyond gigabytes.
• 2:05
  SPEAKER_00: So this is 16,000.
• 2:07
  SPEAKER_00: So a gigabyte is 1,000 megabytes.
• 2:11
  SPEAKER_00: And one megabyte is 1,000.
• 2:20
  SPEAKER_00: You know what?
• 2:20
  SPEAKER_00: It's all it's a lot.
• 2:21
  SPEAKER_00: It was not much back then.
• 2:23
  SPEAKER_00: So anyway, yeah, I just could completely confuse myself.
• 2:25
  SPEAKER_00: But just to give you an idea, right?
• 2:26
  SPEAKER_00: So that was memory on the machine.
• 2:28
  SPEAKER_00: So it didn't have much room on there.
• 2:30
  SPEAKER_00: So whenever I would build out a game, program it, you know, I don't know, maybe 100, 150 lines of programming.
• 2:38
  SPEAKER_00: It was basic coding, basic program, basic, basic language.
• 2:43
  SPEAKER_00: So I would put that in there and then I would test it.
• 2:46
  SPEAKER_00: I would plug in uh some audio visual cables into the back of uh black and white television, and then the game would be replicated on the screen.
• 2:56
  SPEAKER_00: I'd find the right channel, and it was off to the races.
• 2:59
  SPEAKER_00: The games that I would build out were Pong, you know, the ball goes across, you move this paddle over here and it knocks the ball back and forth.
• 3:07
  SPEAKER_00: There was also breakout.
• 3:08
  SPEAKER_00: Um, I got breakout to work finally.
• 3:11
  SPEAKER_00: It was very frustrating because when you type in the code, trying to go back and look at it, because you're using that same monitor, that same black and white television, which is what it was.
• 3:21
  SPEAKER_00: I think it had dial, I think it had UHF and VHF.
• 3:25
  SPEAKER_00: It did have numbered channels.
• 3:27
  SPEAKER_00: We didn't have cable, we had an antenna.
• 3:29
  SPEAKER_00: So we would tune in on UHF, my God, anyway.
• 3:33
  SPEAKER_00: So that was that process.
• 3:35
  SPEAKER_00: I would then play the video game on the television and so on.
• 3:39
  SPEAKER_00: The one game, um, breakout, I believe it was, with the ball that goes up and hits the tile, comes back down, and so on.
• 3:46
  SPEAKER_00: That particular game didn't work very well, and it took me hours and then days, and then finally there was a small little piece in the code that as I was copying it, and I screwed it up.
• 3:58
  SPEAKER_00: I don't know what symbol it was, but I had messed it up and it didn't work.
• 4:02
  SPEAKER_00: Well, you know, computer technology kept continuing to change.
• 4:06
  SPEAKER_00: It wasn't long after that.
• 4:07
  SPEAKER_00: I got into the police academy, came out in Los Angeles.
• 4:10
  SPEAKER_00: That was where the most of the training officers were very proud of their number two pencil.
• 4:15
  SPEAKER_00: This is 1990, 1991.
• 4:18
  SPEAKER_00: And this is from 84 to that time.
• 4:20
  SPEAKER_00: So, you know, not what 13, 14 years later.
• 4:23
  SPEAKER_00: 84, no, seven, eight years later.
• 4:26
  SPEAKER_00: So at that time, they're very proud of their number two pencil.
• 4:29
  SPEAKER_00: The number two pencil was how they wrote the arrest reports.
• 4:32
  SPEAKER_00: They we had PARs, we had arrest reports, we had these continuation documents and where you would put, you know, where, you know, what you what your assignment was, you know, while you working uniform patrol in a marked black and white police vehicle at such and such a date, such and such a time.
• 4:48
  SPEAKER_00: We observe these things happening, or we were in this area, we observed these things happening, then so on and so forth.
• 4:54
  SPEAKER_00: You carry it through, and then you write that up in the report, you end up arresting the individual, and that's the report.
• 5:00
  SPEAKER_00: If there's a use of force, if you got in a fight with them, then that would go in there.
• 5:04
  SPEAKER_00: Then a supervisor would typically come out and do a different report.
• 5:07
  SPEAKER_00: It's gotten more tight over the years.
• 5:09
  SPEAKER_00: At one point, probably back in the day, there wasn't much of a report.
• 5:12
  SPEAKER_00: A supervisor did, you know, people got in a fight with the police, and police usually won the fight, and that's kind of how it was written.
• 5:19
  SPEAKER_00: But you know, things change.
• 5:20
  SPEAKER_00: So moving through that, I watched when I first came on the job, they started to integrate computers, uh, IBM PCs 296 processors, maybe.
• 5:29
  SPEAKER_00: Again, more dating of myself.
• 5:31
  SPEAKER_00: I don't even think you could play Doom on it.
• 5:33
  SPEAKER_00: I don't even think Doom existed at that point.
• 5:35
  SPEAKER_00: But I remember the training officers in the police station with these new computers, they would replace the writing area in the report rooms with computer stations now.
• 5:46
  SPEAKER_00: So, what would end up happening is the uh police officers would come in and kind of push the computer aside and still hand write the report.
• 5:53
  SPEAKER_00: But I had a little understanding of computers, so I tried to help people understand the computer technology at the time.
• 6:00
  SPEAKER_00: And it was clunky, it was difficult, the reports didn't load in smoothly.
• 6:05
  SPEAKER_00: It was it was it was horrible at best.
• 6:08
  SPEAKER_00: Really not adopted.
• 6:10
  SPEAKER_00: Now everything's typed.
• 6:11
  SPEAKER_00: I believe they could even type reports out of the car and have ready by the time you get to the station.
• 6:16
  SPEAKER_00: But back then it wasn't that way.
• 6:18
  SPEAKER_00: So you continue to move through that technology, and today with artificial intelligence, that's a whole different game.
• 6:27
  SPEAKER_00: The systems that I build online, I talk those into existence.
• 6:31
  SPEAKER_00: Now, it's one thing to have it create this thing for you, this program, this app, this great idea, have it answer it.
• 6:39
  SPEAKER_00: It's a whole nother thing, taking that and making it live.
• 6:41
  SPEAKER_00: Some of the systems make it easier, but if you're doing this at scale, if you're building out many, then more than likely you're going to need some type of more autonomous type structure to your workflow.
• 6:52
  SPEAKER_00: But it's very much capable.
• 6:54
  SPEAKER_00: But then I had to learn how you get a particular website or web app or a web funnel online.
• 7:02
  SPEAKER_00: How do you do that?
• 7:03
  SPEAKER_00: Where do you put it?
• 7:04
  SPEAKER_00: What are the concerns?
• 7:06
  SPEAKER_00: What are the safety concerns?
• 7:07
  SPEAKER_00: What are the double checking of the code you should be asking?
• 7:11
  SPEAKER_00: Because the code, as it's prepared by these large language models, there could be issues with it.
• 7:16
  SPEAKER_00: There could be security concerns.
• 7:18
  SPEAKER_00: Somebody that's smart, I mean, not that anybody's going to really care about what Connor produces.
• 7:22
  SPEAKER_00: Who knows?
• 7:23
  SPEAKER_00: Maybe someday I'll hit it out of the park and they will give a crap.
• 7:26
  SPEAKER_00: But right now, you know, it's like asking the question, you know, if if the devil is real and if the devil can only be one place at one time, where is he going to be spending most of his time?
• 7:37
  SPEAKER_00: Probably not here.
• 7:39
  SPEAKER_00: Probably with the powers that are running the world governance, right?
• 7:42
  SPEAKER_00: More than likely that.
• 7:44
  SPEAKER_00: So that being the case, if I'm producing some kind of code, besides maybe being a risk for other people, but me, myself, and I probably nobody's going to pay attention.
• 7:53
  SPEAKER_00: So you want to secure those ends by asking your AI repeatedly.
• 7:57
  SPEAKER_00: But I had to learn all of those little steps on where to get the website put, what what systems should I use?
• 8:03
  SPEAKER_00: Should I use Cloudflare or Netlify?
• 8:06
  SPEAKER_00: Should I use posting through Amazon?
• 8:07
  SPEAKER_00: What does all that look like?
• 8:09
  SPEAKER_00: And then also GitHub.
• 8:10
  SPEAKER_00: What the hell is that all about?
• 8:11
  SPEAKER_00: So that was something else that I had to learn as well.
• 8:14
  SPEAKER_00: And then how do you work with that GitHub repository?
• 8:17
  SPEAKER_00: How do you create them?
• 8:19
  SPEAKER_00: What's the focus?
• 8:20
  SPEAKER_00: And then even with the regular AI systems, the memory thing, because there's only certain amounts of memory.
• 8:26
  SPEAKER_00: And if you're using a public-facing AI, not your own, you know, self-installed large language model in your residence or at your office or whatever, when you're using that, memory is a big deal because every time you go back and want to start a workflow, you don't want to have to start over again.
• 8:40
  SPEAKER_00: I'm Connor, I sell real estate, I do AI integrations, I'm an AI architect, blah, blah, blah, blah, blah.
• 8:46
  SPEAKER_00: It's too much.
• 8:47
  SPEAKER_00: So what I've told people in the past, and now that's starting to change more on the advice realm, is make sure you make yourself a who the hell am I document.
• 8:56
  SPEAKER_00: And then you'll take that and you'll share it with your AI.
• 8:58
  SPEAKER_00: And then also you want to include in that document to have it prompt you with questions so it can learn about you so it can best apply itself to whatever workflow you're trying to produce.
• 9:09
  SPEAKER_00: Giving AI the answer.
• 9:10
  SPEAKER_00: And that's been one of the most difficult parts for me because I believe I have a certain way to get to a certain place.
• 9:17
  SPEAKER_00: And now I'm going to automate certain parts of the process and hand off some of the more repetitive lifting, the more repetitive workflows I typically do over to AI.
• 9:29
  SPEAKER_00: I thought it would be best to just basically explain how I do it and say, you do it this way.
• 9:35
  SPEAKER_00: I find the more I work with it though, and the more I wrap myself around Hermes and OpenClaw and these other systems that kind of have agents that go out and do things, I find the better thing is not giving it too much, kind of giving it more of the end, and then having it work through the process.
• 9:51
  SPEAKER_00: But before it starts pulling the trigger on anything, I used to say pressing the trigger on anything, pulling a case pulling to one side or another.
• 9:59
  SPEAKER_00: Pressing is that straight, direct to the rear press, achieving a surprise break.
• 10:03
  SPEAKER_00: Yeah, I was LAPD for a while, so I get that.
• 10:06
  SPEAKER_00: But pressing uh these particular large language models on what is entered into these systems by the way of my desire is a mistake.
• 10:16
  SPEAKER_00: So instead of doing that, I kind of want to give it the end goal and then make sure that it validates its particular workflow to get to that end goal, and I'm able to put my eyeballs on it before it starts to press on and create all these things.
• 10:29
  SPEAKER_00: And right now, the systems themselves, they choke.
• 10:32
  SPEAKER_00: If you're you're gonna let Claude or one of these other agentic systems start to work on things, it's only gonna get better.
• 10:37
  SPEAKER_00: It's only gonna be able to work on things longer and longer and longer.
• 10:41
  SPEAKER_00: And then there's things called loops and other systems you can put into place in order to have these systems, if they bump into something, maybe another agent could look at it and say, Well, that's an easy fix.
• 10:51
  SPEAKER_00: Just fix this part, and then you'll be able to continue.
• 10:54
  SPEAKER_00: And it does.
• 10:54
  SPEAKER_00: We're gonna see more and more of that agentic workflow start to develop.
• 10:59
  SPEAKER_00: What does that mean to everybody out there?
• 11:01
  SPEAKER_00: That means whatever you do every single day in your particular job, assignment, entrepreneurial craft, or whatever it is, even in real estate, you'll be able to automate a lot of these different systems and have it basically take care of it all the way through and go into a loop so it gathers all that data, all that information.
• 11:20
  SPEAKER_00: And then when it comes to the execution layer, then you can put your eyeballs on it and say, okay, these are the things that we're gonna work on.
• 11:27
  SPEAKER_00: These are the things I want to develop out, these are the emails I want to set, these are the phone calls I want to make because you found these people that have these problems.
• 11:35
  SPEAKER_00: I'm going to solve them in real estate.
• 11:37
  SPEAKER_00: These are the sellers that maybe have expired, these are the sellers that have canceled their listings, making sure there's no contract in place.
• 11:44
  SPEAKER_00: These are the ones I'm gonna follow up with because there's holes in the maybe advertising and marketing.
• 11:49
  SPEAKER_00: The agent was producing maybe their property, had these particular issues that could be easily fixed, and then they'd be able, they're able to achieve the selling of the residents, which they weren't before, because maybe the agent that they had hired wasn't knowledgeable enough of the process or didn't have the intestinal fortitude to be able to tell them, hey, listen, these are problems, these are the things you need to attend to.
• 12:11
  SPEAKER_00: This is why the residents isn't selling, this is why we've been on the market six months and nothing's happened.
• 12:15
  SPEAKER_00: You just got to tell them.
• 12:16
  SPEAKER_00: If you're an agent out there watching this, you got to tell them.
• 12:19
  SPEAKER_00: But looking back at where I was back in the 83, 84, and then looking where it is today, it's a game changer.
• 12:25
  SPEAKER_00: And then people mentioned, well, you know, it's not going to be any, we're not going to have issues as human beings with regard to jobs and job loss and replacement.
• 12:35
  SPEAKER_00: Because in the past revolutions, the industrial are architectural, agricultural, agricultural.
• 12:42
  SPEAKER_00: Let's we'll stick with industrial.
• 12:44
  SPEAKER_00: It seemed to be at least easier for my brain to comprehend.
• 12:47
  SPEAKER_00: Whenever you had those particular revolutions, it was maybe a handful of ideas grouped together.
• 12:53
  SPEAKER_00: So it was very narrow in the implementation or the ideas.
• 12:57
  SPEAKER_00: And at that time, it replaced the people in that narrow frame.
• 13:02
  SPEAKER_00: But those people were able to step just a little bit sidestep and then do something else that might have been associated with it that wasn't covered by this revolution, this industrial idea that changed things on the ground.
• 13:15
  SPEAKER_00: So that was the past.
• 13:17
  SPEAKER_00: That's how that typically worked out.
• 13:19
  SPEAKER_00: Now you're moving into intelligence.
• 13:21
  SPEAKER_00: Intelligence is the very thing each one of those past industrial revolutions was based on.
• 13:26
  SPEAKER_00: Intelligence was the printing press, intelligence was the loom, intelligence was all the other things fill in the blank, the factory, the way the schools are built around putting together and maintaining factory workers or a consistent workforce, which I guess the Rockefellers or somebody else put all that stuff together.
• 13:43
  SPEAKER_00: So that's that's where that was.
• 13:45
  SPEAKER_00: But now those were very narrow.
• 13:47
  SPEAKER_00: Intelligence at this scale, at this level, at this exponential growth, at this singularity is all of that.
• 13:56
  SPEAKER_00: So that's something to really wrap your mind around.
• 13:58
  SPEAKER_00: It's it's beyond it.
• 13:59
  SPEAKER_00: And once somebody achieves, I guess, the pinnacle or close to the pinnacle, because the pinnacle for us is going to be better than us in every way.
• 14:07
  SPEAKER_00: We're not going to understand anything past that point because we're limited on our own resources.
• 14:14
  SPEAKER_00: To try to look into a multidimensional frame, for example, we understand our three or four-dimensional world, which if we include time in it, time could be a full dimension.
• 14:24
  SPEAKER_00: So we have four.
• 14:25
  SPEAKER_00: We have height, width, depth, and time, four dimensions.
• 14:28
  SPEAKER_00: You heard three dimensions.
• 14:29
  SPEAKER_00: Yeah, you somebody throws a baseball at you, you're gonna move your head because we have that depth perception.
• 14:35
  SPEAKER_00: People that have one eye no longer working, they lack a lot of that depth perception.
• 14:41
  SPEAKER_00: They get that 3D effect.
• 14:44
  SPEAKER_00: Okay.
• 14:44
  SPEAKER_00: But moving that from um that particular mindset, looking at this, and then trying to get your same mind to look at multiple dimensions, five, six, seven, because apparently they've proven they exist, trying to see those concepts and understand we don't see it.
• 15:01
  SPEAKER_00: I bring that back to artificial intelligence where we're not gonna understand this higher level, and it's not gonna make any sense.
• 15:10
  SPEAKER_00: For us, it's gonna be great in all areas.
• 15:12
  SPEAKER_00: It's gonna be perfect, it's gonna serve us what we need, it's gonna take care of everything for us.
• 15:17
  SPEAKER_00: We're gonna completely be maxed out on comprehension in every level.
• 15:21
  SPEAKER_00: And there are gonna be some of us that are gonna have different levels of that.
• 15:24
  SPEAKER_00: But then what happens?
• 15:25
  SPEAKER_00: What's that next step for the world?
• 15:28
  SPEAKER_00: Is that total bliss?
• 15:29
  SPEAKER_00: Is that total happiness?
• 15:31
  SPEAKER_00: Because potentially we could have everything we want across every venue, across every stage, be completely happy.
• 15:38
  SPEAKER_00: We could have worlds that we're existing in that would just be fantastic.
• 15:43
  SPEAKER_00: But, you know, also we need to ask ourselves how much of the human element is left?
• 15:47
  SPEAKER_00: How much of the jobs are going to be replaced by robotic arm, AI arm, where human beings might still want that human-to-human connection.
• 15:56
  SPEAKER_00: That's something else.
• 15:57
  SPEAKER_00: And what will those jobs look like?
• 15:59
  SPEAKER_00: It's not going to be the same as the past industrial, past agricultural, past revolutions, because those are narrowly based on one particular modality of intelligence as it's applied to something in the world, a good idea.
• 16:15
  SPEAKER_00: This is all of the ideas, everything.
• 16:18
  SPEAKER_00: And that's the difference.
• 16:19
  SPEAKER_00: But just a little walk down memory lane with I remember loading up that cassette, finding loading the game back into the computer and playing it.
• 16:26
  SPEAKER_00: It's a whole different animal than it was today.
• 16:29
  SPEAKER_00: All right, I'm Connor.
• 16:30
  SPEAKER_00: We'll see you in next one.
• 16:31
  SPEAKER_00: Thanks for watching.
• 16:31
  SPEAKER_00: Be well.