00;00;00;00 - 00;00;06;00
Speaker 1
Hey, I'm recording this for a podcast. Is okay.
00;00;06;02 - 00;00;14;08
Speaker 2
Absolutely. That's totally fine. Let me know what you need help with, and I'll do my best to assist. Just let me know what you're working on for your bucket.
00;00;14;10 - 00;00;38;11
Speaker 1
Okay, so we were just in the middle of a podcast about AI in journalism. Long story short, that, audio is bad. It was connecting to my metal glasses instead of the speaker that is feeding to this mic. So that audio is no good. I want to catch us up where originally it was on just journalism and AI.
00;00;38;11 - 00;01;17;12
Speaker 1
And like the different entry points into journalism from AI, we're still going to to this one. But, we were on a different thread about, character that we created called Carol the war journalist slash consultant. She is a scrappy war, just that scrappy war journalist slash consultant in the modern day era with, drone technology and and and trail cams and stuff like that.
00;01;17;12 - 00;01;39;23
Speaker 1
And she's kind of a, I guess an independent intelligence agent, I guess you could say. And so she dabbles in during or she is a journalist, but then she dabbles in intelligence. Yeah. That's where we are.
00;01;39;25 - 00;02;00;04
Speaker 2
Got it. That sounds like a really cool character. I love how she blends journalism with cutting edge tech and intelligence work. We can definitely dive back into that thread and how her story more. If you want, we can brainstorm some scenarios or even discuss how she uses all that tech in her build work. Let me know what direction you like to dip.
00;02;00;07 - 00;02;34;06
Speaker 1
Yeah, so I kind of, started going down the path of if she had $1 million, what her infrastructure build would kind of look like. I started out with drones. Tell me a little bit about the drones that she may use and today's price points with maybe some speculation as to where they'll be in price over the next three years.
00;02;34;09 - 00;03;05;13
Speaker 2
Sure. So if we're talking about drones that might be used for something like surveillance or environmental monitoring, they can range anywhere from a few hundred dollars to several thousand, depending on the features. Some of the more advanced ones that come with high resolution cameras, long battery life and obstacle avoidance tech can be on the pricier side, and in the next few years, it's likely that prices will come down a bit as technology advances and you'll.
00;03;05;16 - 00;03;23;17
Speaker 2
Absolutely. So if you have any specific needs or particular features in mind for the drones, we can definitely look into that. And if there's anything else related to the infrastructure that you want to dive into, just let me know.
00;03;23;20 - 00;03;47;00
Speaker 1
Let's talk about a, just kind of a general price point for a compact drone with media, to long battery life and then kind of a bulk cost on, that capability with air and thermal.
00;03;47;03 - 00;04;11;07
Speaker 2
Got it. For a compact drone with medium to long battery life. You're probably looking at around $500 to $1000 for a decent quality model. If you add in IR infrared capabilities for night vision or thermal imaging, that can increase the cost. So you might be looking at 1500 to $2500 per drone, depending on the brand in the tech.
00;04;11;10 - 00;04;19;21
Speaker 2
And if you're buying in bulk, you could potentially get some discounts, especially if you're buying a large number of unit.
00;04;19;23 - 00;04;28;00
Speaker 1
Okay, tell me about a modern $1,500 drone with these capabilities.
00;04;28;02 - 00;04;52;26
Speaker 2
Sure, a modern $1,500 drone with these capabilities would likely have a high resolution camera, possibly capable of shooting in 4K and a battery life of around 30 to 40 minutes on a single charge. It would also have advanced stabilization features, obstacle avoidance sensors, and possibly some autonomous flight modes that make it easier to operate with IR or thermal imaging.
00;04;52;27 - 00;05;13;00
Speaker 2
You'd be able to use it for things like search and rescue, wildlife monitoring, or even inspecting infrastructure at night. It would also likely be relatively easy to use, with a good companion app for controlling the drone and viewing the live feed from the camera.
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Speaker 1
What kind of way can one of these drones carry?
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Speaker 2
They're definitely a lot of fun and super useful if you do end up getting one. I'm sure you'll have a home.
00;05;28;09 - 00;05;40;11
Speaker 1
I think you miss. I understood my question. I was asking about if you take a $1,500 drone, what kind of weight can it carry as a load?
00;05;40;14 - 00;06;08;22
Speaker 2
Got it. So a drone in that price range around $1,500 typically isn't designed to carry heavy payloads. Most consumer drones in that category can handle maybe a few hundred grams, which is enough for a small action camera or some lightweight accessories. If you need a drone that can carry more weight, like a dSLR camera or other heavier equipment, you'd be looking at more industrial grade drones, which tend to be pricier.
00;06;08;24 - 00;06;30;26
Speaker 1
I'm thinking like, maybe a light weight game cam that can, stream. I mean, I guess really just a cell phone. Can one of those, $1,500 drones carry the weight of a cell phone?
00;06;30;28 - 00;06;52;08
Speaker 2
Yes, absolutely. A drone in that price range can definitely handle the weight of a cell phone, which is usually only a few hundred grams. So if you're thinking about attaching a lightweight game camera or even a smart phone, you should be good to go. It's a pretty cool way to get some unique aerial footage or live streaming set up.
00;06;52;10 - 00;07;01;22
Speaker 1
Would it be hard to rig an air or, thermal set up to a cell phone?
00;07;01;24 - 00;07;27;01
Speaker 2
It can be a bit tricky, but it's definitely possible. There are some thermal imaging cameras that are designed to attach directly to a smartphone via USB or a lightning port, depending on the phone. So you could use one of those and just mount the phone on the drone. It might take a little bit of DIY rigging to make sure everything are secure and balanced, but it's definitely doable and could be a fun project.
00;07;27;03 - 00;07;54;12
Speaker 1
Okay, we're a little bit deep in the weeds on like what is happening here, and I think it's because we had that previous conversation. But what I'm thinking about is Karel has a fleet of drones that drop off kind of game cameras that live feed until they like, maybe they're solar. So in certain areas they can, record and transmit live video.
00;07;54;15 - 00;08;09;17
Speaker 1
And kind of these drones can drop these cameras along maybe like miles of front line and, kind of just do recon.
00;08;09;19 - 00;08;33;28
Speaker 2
At it. That makes sense. So basically, you be using drones to strategically place these game cameras or small devices along a certain area to get live feeds and do some reconnaissance. That sounds like a really interesting setup. And the drones would basically act as delivery vehicles for the cameras. It's definitely doable, especially at the cameras are lightweight and can be easily deployed by the drone.
00;08;33;28 - 00;08;39;11
Speaker 2
It could be a really effective way to cover a large area.
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Speaker 1
How many drones can we get for $225,000?
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Speaker 2
So if we assume each drone is around $1,500, you can do a quick calculation by dividing $225,000 by 1500. That gives you about 150 drones. So you'd be able to get a pretty sizable fleet with that budget.
00;09;05;09 - 00;09;54;07
Speaker 1
Okay. We talked about the trail cams. Let's say that those cost, $100. Is that, reasonable price for buying, a device that can see in you or in air, as well as thermal, as well as transmit a signal? It could be very basic. It could be just like an Arduino with a battery and an ear and, and a thermal camera and a basic video transmitter, maybe like some cell phone that's that you could probably get for next to nothing.
00;09;54;10 - 00;10;25;00
Speaker 2
$100 might be a bit of a tight budget for something that have both air and thermal capabilities and can transmit video. Usually, thermal cameras alone can be a bit pricey, often a couple of hundred dollars at least. If you're aiming for a really basic set up, maybe with a simple IR camera and a basic transmitter, you could possibly get close to that price point if you DIY it with some inexpensive components like using an Arduino and some affordable parts.
00;10;25;02 - 00;10;29;28
Speaker 2
But for a fully integrated, reliable system, you might need a bit more budget.
00;10;30;01 - 00;11;04;18
Speaker 1
Not so let's skip thermal. Let's just go with, basic infrared and maybe even with an infrared light on it. What do you think is the cheapest kind of scrappy, like, bare bones set up for something, with a basic infrared light or maybe not even a light, and an infrared camera with a transmitter and a decent battery life.
00;11;04;20 - 00;11;27;26
Speaker 2
If we keep it super simple and focus just on a basic infrared camera with an IR light and a transmitter, you could probably put something together for around 100 to $150. There are some affordable IR cameras and LED lights that you can pair with a small video transmitter, and you can use a lightweight battery pack or even a power bank to keep it running.
00;11;27;29 - 00;11;42;22
Speaker 2
It would definitely be a bit of a DIY project, but it could be a fun and budget friendly solution.
00;11;42;24 - 00;12;09;20
Speaker 1
Okay, I I'm sure that it would have to depend on geography, but how far would you spread apart a camera like this to pretty thoroughly cover a front line of a place? I know if it's forested, it's much more, but if it's open, it's much less. What would be a good number of cameras? Maybe per mile.
00;12;09;22 - 00;12;34;20
Speaker 2
Yeah, you're right, it does depend a lot on the terrain and what you're trying to monitor in a densely forested area. You might need more cameras, maybe even several per mile, because you have limited lines of sight and more obstacles in an open area. You could spread them out a bit more. So maybe one camera every half mile or even a mile, depending on the visibility and the range of the camera.
00;12;34;22 - 00;12;43;17
Speaker 2
Ultimately, it comes down to the level of coverage you need and the specifics of the environment.
00;12;43;19 - 00;13;09;00
Speaker 1
If we had to pick a distance that we were going to put the cameras, I guess if we were going to have to pick a number of, cameras per mile that we would probably need in our first order for purchasing decisions, what would you, what would be the distance you recommend spacing them out? And how many per mile would you order?
00;13;09;03 - 00;13;31;13
Speaker 2
A good rule of thumb for a balanced starting point might be placing one camera every quarter mile. So that would give you about four cameras per mile. That's making sure give you pretty thorough coverage even in more challenging terrains. So if you're planning a first order, that might be a good number to start with. And then you can always adjust depending on how it works out in practice.
00;13;31;15 - 00;13;44;07
Speaker 1
Okay, so let's do overkill. Let's do every hundred yards, right? If we wanted to cover a 50 mile front line every hundred yards, how many cameras would that be?
00;13;44;09 - 00;14;09;01
Speaker 2
All right. If we're placing a camera every hundred yards and there are 1760 yards in a mile, that means you'd need 17.6 cameras per mile. If we multiply that by 50 miles, you'd end up with about 880 cameras total. So that would give you a very comprehensive coverage of that 50 mile front line.
00;14;09;03 - 00;14;24;00
Speaker 1
So that would only be a $90,000 to cover a 50 mile area, or I guess it would be distance.
00;14;24;03 - 00;14;44;22
Speaker 2
Yeah, exactly. If each camera setup is about $100, then 880 cameras would come to roughly $88,000. So just under $90,000, which is pretty efficient for covering a 50 mile stretch. It definitely shows how feasible it can be to set up a large scale monitoring system on a budget.
00;14;44;25 - 00;15;14;19
Speaker 1
Okay, let's say that she purchases enough to cover 200 miles. That would be, $360,000 to spend on cameras, but she will be covering 200 miles of a of distance every 100 yards, using her drone fleet to drop these things off and pick them up when they need to be.
00;15;14;21 - 00;15;38;28
Speaker 2
That sounds like a pretty impressive plan. Using the drone fleet to deploy and retrieve the cameras is a really efficient way to manage such a large area, and covering 200 miles with that setup will definitely give her a really extensive network of surveillance or monitoring, depending on what she needs. It sounds like a big project, but definitely a doable one.
00;15;39;01 - 00;16;05;27
Speaker 1
Okay, so if we got 360 K on cameras and we got 225 K on drones, which includes all the infrastructure and let's say charging stations and blah, blah, blah, blah, blah. What else is important to this infrastructure or that we need to spend some money on? Oh well, a few other. And how much money do we have left out of a million?
00;16;05;29 - 00;16;25;18
Speaker 2
So if you've got $360,000 on cameras and $225,000 on drones, that's a total of $585,000 spent. If the original budget is around $600,000, that means you have about $15,000 left. So that remaining.
00;16;25;18 - 00;16;26;16
Speaker 1
Amount to.
00;16;26;19 - 00;16;28;00
Speaker 2
Go towards things like.
00;16;28;03 - 00;16;32;16
Speaker 1
That. The original budget is $1 million.
00;16;32;18 - 00;17;05;06
Speaker 2
Oh. Got it. So with a $1 million budget, if you spent $585,000 so far, that leaves you with $415,000 remaining. That gives you plenty of room for things like data infrastructure, maybe hiring some personnel for monitoring, maintaining the equipment, and any other unexpected costs that might come up. It sounds like a solid plan with a good cushion.
00;17;05;09 - 00;17;19;02
Speaker 1
Okay. Let's talk. Cost of a scrappy set up for data storage and processing.
00;17;19;04 - 00;17;57;05
Speaker 2
Sure. For a budget friendly set up, you could consider using something like network attached storage or nest devices, which can be pretty cost effective. You could also use external hard drives or even cloud storage if you want something scalable for processing, you could rely on a decent computer or even repurpose hardware to keep costs down. Overall, you could set up a pretty solid system for just a few thousand dollars, depending on how much data you need to store and process.
00;17;57;08 - 00;18;15;08
Speaker 1
So you could take all the data from these drones and, all you could do all this with just a couple thousand dollars worth of data stuff and like computing.
00;18;15;11 - 00;18;41;06
Speaker 2
Yeah. If you're keeping it relatively simple and just need basic storage and processing, you can definitely get started with just a few thousand dollars. There are plenty of affordable solutions out there, especially if you're comfortable with a bit of DIY, and you can always scale up later if you need more capacity or more advanced processing power.
00;18;41;09 - 00;19;09;25
Speaker 1
Okay, tell me what her setup looks like. Down to, like, the bones of it. Like what is her computer like? What has she got? She she got servers. She got, does she use something like AWS? Probably not, because she's got a controller. Data. Kind of really dig in to the physical and software infrastructure behind this.
00;19;11;28 - 00;19;43;12
Speaker 2
Absolutely. So for a scrappy but effective setup, she'd probably have a dedicated computer or maybe a small server on site to handle data processing, something like a robust desktop with plenty of storage or a dedicated and asked device would do the trick. She might use some external hard drive for backup and redundancy for software. She'd likely have some video management software to organize and review the footage, and she probably want to set up a secure local network to keep everything connected.
00;19;43;12 - 00;19;52;21
Speaker 2
So it's pretty straightforward, but definitely effective and scalable.
00;19;52;23 - 00;20;23;17
Speaker 1
Okay, she's probably got a van that she works out of. Let's say, let's see what this kind of off road vans set up with, with an on site server and all the kind of basic things that she would need to offload something like this. Maybe like a van that pulls a trailer or like a box truck or something like that.
00;20;23;19 - 00;20;46;06
Speaker 2
Oh, that sounds like a really cool, like mobile command center. If she's working out of a van or a box truck, she could have a compact server rack inside, maybe some laptops or a desktop set up for processing data. She could have a dedicated power source, like solar panels on the roof or a generator, and definitely some storage for all the equipment.
00;20;46;08 - 00;20;55;12
Speaker 2
It would be like a self-contained hub, making it easy for her to move around and stay connected no matter where she is.
00;20;55;14 - 00;21;11;25
Speaker 1
What would be the way to go? Would you, tow a trailer with a van? Or would you have, like, a bus that was outfitted for it? Or would you have a box truck, which would be the smartest way to go?
00;21;11;27 - 00;21;34;24
Speaker 2
It really depends on the scale of the operation and how much equipment she needs to carry. A van with the trailer can be super flexible and easy to maneuver, especially if she needs to get into more remote areas. On the other hand, a box truck or an outfitted bus can give her more room and allow for a more permanent, comfortable setup inside.
00;21;34;27 - 00;21;41;16
Speaker 2
So it just depends on how mobile she needs to be and how much space she needs for all the gear.
00;21;41;18 - 00;22;12;28
Speaker 1
Okay, let's say that she, has like an outfitted school bus with, like, big tires and, and a bigger engine. And the rear is, the entire back of the bus is mostly probably about 75% storage. Right. So the drones come in, they fly in the back door, and then they have little stands where they sit and stuff like that, and they put the drone cams.
00;22;12;28 - 00;22;35;08
Speaker 1
Maybe all the drone cams are littered across the top using some kind of magnetic, maybe drone cam has a magnet on it. So all the drones got to do is like, go and plop the drone cam on the top, and it just sticks to the top and, on the inside. And then it flies inside and there's like, racks and it navigates to a little rack and goes and sits on it.
00;22;35;08 - 00;23;09;00
Speaker 1
And that's magnetic. And then, she's got like a little mini command center there that, is really just like a desk with a high performance, like, desktop or laptop that's kind of bolted to a table. And maybe she's got an, maybe she is an engineer, and she has someone that, works with her in, like, a partner or or, assistant capacity.
00;23;09;02 - 00;23;35;25
Speaker 2
That sounds like such an awesome setup. I love the idea of the drones just flying in and docking with magnetic mounts. It's super efficient, and it definitely makes the best use of space. Having that mini command center right there with a high performance computer means she can process data in real time, and having a partner or assistant along for the ride definitely helps, especially when it comes to managing all the tech and making sure everything runs smoothly.
00;23;35;25 - 00;23;45;00
Speaker 2
It's definitely an adventure.
00;23;45;03 - 00;24;14;13
Speaker 1
Okay, if we if she maybe got this, school bus from, for a really good deal and rebuilt it and put a new engine in and like a new tires and really didn't spend, like, a ton of money, kind of, maybe hired a cheap welder or one of them knows how to weld, and they set this thing up as kind of just like a DIY project style.
00;24;14;16 - 00;24;30;06
Speaker 1
What are we really talking cost here? To have something that is decent with, with all of this that I described, not including the drones or the cameras, just the truck.
00;24;30;09 - 00;24;54;22
Speaker 2
If she managed to get the school bus at a bargain price and did most of the work herself, or went, affordable health, the cost could be quite reasonable. The bus itself might run a few thousand dollars and then putting in a new engine, tires and doing the interior outfitting could add a few more doormen. All in all, maybe somewhere in the range of 10,000 to $20,000.
00;24;54;22 - 00;25;11;06
Speaker 2
If she's being really resourceful and doing a lot of DIY work, it definitely sounds like an amazing project that could come together on a pretty modest budget.
00;25;11;08 - 00;25;17;09
Speaker 1
Okay, so why don't we give her $100,000 for her entire setup?
00;25;17;11 - 00;25;41;18
Speaker 2
That sounds like a fantastic budget. With $100,000, she can definitely create a top notch, efficient, and comfortable mobile command center. She can invest in quality equipment, ensure everything is set up exactly how she needs, and still have some cushion for maintenance and any unexpected expenses. It's a great way to make sure everything runs smoothly and that she's got all the tools she needs.
00;25;41;21 - 00;26;08;20
Speaker 1
Okay, and let's say that Carol, instead of having an assistant or like hiring engineer, she has a partner. Let's give her partner. Not in light, not her life partner, or I guess it could be, but like, a partner, as in you would have, like, in a law firm or something like that. Let's give her a partner, name and a little bit of a backstory.
00;26;08;23 - 00;26;36;07
Speaker 2
Sure. Let's go with the name Alex for her partner. Maybe. Alex is a tech savvy engineer who loves working on innovative projects and has a background in robotics or maybe drone technology. They could have met through a mutual passion for engineering and decided to team up for this adventure. Alex would be the one who helps keep all the tech running smoothly, and maybe even comes up with new ideas to improve their set of.
00;26;36;10 - 00;26;43;11
Speaker 2
It's always great to have a creative partner on board.
00;26;43;14 - 00;26;56;23
Speaker 1
Okay, so that still leaves us with $315,000 and she's got a pretty decent setup. What are we missing?
00;26;56;26 - 00;27;23;17
Speaker 2
I think we've covered most of the essentials. The only other thing that might come in handy could be things like communication tools. Maybe a satellite phone, or a really solid internet setup to stay connected in remote areas. Maybe some backup power solutions like extra batteries or a generator just to make sure everything keeps running smoothly. But other than that, it sounds like she's got a pretty comprehensive and awesome set up.
00;27;23;20 - 00;28;10;06
Speaker 1
Okay, so we are $315,000 under budget. That is a good that is good because she could have a budget of 150 million instead. But let's stick with what is now 600 and then $85 that she's spent with her partner. And let's go into her business. Let's go into her journalism career and how she utilizes her setup and her eye, equipment to, push forward the field of, real time front line journalism.
00;28;10;08 - 00;28;34;27
Speaker 2
That sounds like a fascinating direction. With her set up and I equipment, she could really revolutionize frontline journalism. She could use the drones and cameras to capture real time footage from areas that are difficult to reach, or even potentially dangerous. With her partner, Alex, they could process and analyze the data when they go, providing instant updates and insights.
00;28;34;29 - 00;28;58;16
Speaker 2
This could be incredibly valuable for reporting on everything from environmental issues to humanitarian crises. It would definitely push the boundaries of how journalism is done in real time.
00;28;58;18 - 00;29;35;05
Speaker 1
Let's say that Carol and Alex do their research on where to go next, and they find a good spot to, cover. Maybe it's, something that's breaking out. Maybe it's right. You pick a subject, and I want you to describe, her or them arriving to the scene and kind of the logistics behind their setup. And we'll start there.
00;29;35;07 - 00;30;05;05
Speaker 2
All right, let's imagine there's a wildfire breaking out in a remote forested area. Carol and Alex get word of it. And decide it's the perfect opportunity to put their setup to use. They drive their outfitted school bus to a safe vantage point near the affected area. Once they arrive, they quickly get to work. The drones launch from the back of the bus, equipped with cameras to capture live footage of the fire spread and the conveying of the landscape inside the bus.
00;30;05;05 - 00;30;28;04
Speaker 2
Caroline Alex monitor the live feeds, analyzing the data in real time and providing crucial updates. They might even use their AI tool to predict the fire's movement and help inform first responders. It's a dynamic and impactful way of bringing cutting edge technology to frontline journalism.
00;30;28;06 - 00;30;58;26
Speaker 1
This is such a cool idea. Let's say that just based on today's technology, right? We could see this going of like. And the firemen are wearing their face visors, have like, AR technology in them, like the way that Apple's headset or whatever it has. Right. So like, that's that's still pretty. Let's say that it's even cheaper. Right.
00;30;58;26 - 00;31;27;04
Speaker 1
Something that you could roll out to every fire, plate or fire station in the country today. Right. So let's say, this system that she has of the drones in the, in the cameras and they, we all we know how that works, that goes, drones go. They put the cameras in the right spots, the cameras send up the live feeds, and those live feeds come back to her van.
00;31;27;06 - 00;31;50;27
Speaker 1
The AI, and like their computer system, processes all the data. And like, that's probably pretty easy because it's like, is there a fire here? Is there a fire here? No. Okay. Yes. So like a fire is probably pretty easy to tell if it's on screen. So let's say that those computers take that data and turn it into a live kind of.
00;31;50;29 - 00;32;10;06
Speaker 1
They process it down and analyze it and what they do is they give a live audio feed to all of the firemen on the ground. So they know what's happening around them instead of just like a single chopper or something.
00;32;10;08 - 00;32;23;04
Speaker 2
I'm sorry, but you hit the daily limit or advanced voice. You can switch to standard now to keep chatting.
00;32;23;06 - 00;32;24;01
Speaker 2
I'm sorry.
00;32;24;03 - 00;32;28;11
Speaker 1
Hang on.
00;32;28;14 - 00;32;37;21
Speaker 1
Chat. Did you pick up the last thing I said?
00;32;37;23 - 00;32;56;14
Speaker 2
Yeah, I caught that. You're asking if I picked up the last thing you said. Right before that, we were talking about Carol scrappy mobile set up and how she could use it for frontline journalism. Was there something specific you wanted to make sure I caught?
00;32;56;17 - 00;33;07;18
Speaker 1
Did you, hear where I was talking about how she utilizes it?
00;33;07;21 - 00;33;33;11
Speaker 2
Yeah, I got the part where you were saying she's using the whole system. Drones, camera, and the mobile command center to cover a 200 mile stretch, and it sounded like she's using it almost like a rapid deployment journalism unit. Right. Documenting things in real time, maybe getting into couple remote spots fast. Is that the direction you meant, or were you thinking of something more specific in how she's utilizing it?
00;33;33;14 - 00;34;00;18
Speaker 1
More specific. So, let's say that she goes out for a wildfire and she parks nearby. She sends all the drones out. The drones drop the cameras in certain spots, the cameras send up the live feed, bring it back to the truck. The, the, GPUs and stuff like that in the truck. The AI system processes its data like, oh, this camera, camera 91 has a fire.
00;34;00;19 - 00;34;25;10
Speaker 1
Camera 87 doesn't have a fire. And then what the AI does is it takes all this numbers and it crunches it down. And we could see a world in the very near future where firefighters have an AR feed in their mass, like the, Apple Image Pro or whatever it's called. But let's say for now, using cheap technology, right.
00;34;25;18 - 00;34;59;26
Speaker 1
Everybody's got headphones and they probably have, audio comms, I would imagine. So let's say what this AI does is it chunks sit down and processes all this data and then gives them kind of a live news feed on what's actually really happening on the ground, like, real time intelligence. So they can avoid certain areas and read the fire very well.
00;34;59;29 - 00;35;25;28
Speaker 2
Yes. Got it. And that is such a smart and plausible setup. Carol's rig becomes the brain of the operation. The drone scout, and dropped cheap static eyes on the ground. And her system chews through that data for using onboard GPU and smart filtering. Instead of overwhelming the firefighters with a dozen camera feeds, the AI boiled it down into actionable insights where the fire's hottest, where it's spreading, what's safe or not.
00;35;26;05 - 00;35;53;21
Speaker 2
And yeah, even without cutting edge AR headset, just typing this Intel into their comms like a convoy saying avoid sector three. Advancing flame front detected near camera 91 would be a huge leap. Super low latency, high trust data. Right in their ears feels very doable with today's tech. If someone like Carol stitches it together, do you picture her working alone or is this becoming a full crew operation at this point?
00;35;53;24 - 00;36;06;01
Speaker 1
No. She's partnered with Nun, another engineer that complements her capabilities.
00;36;06;04 - 00;36;29;08
Speaker 2
Perfect. That makes sense. So Carol's got the vision and feel for her and her partner. Maybe Alex is the system thinker. Like Carol's the one out there choosing the drop points for the drones based on terrain and urgency. And Alex is in the back of the truck managing the software that optimizing the AI detection model, maybe even rewriting code on the fly as needed.
00;36;29;10 - 00;36;48;13
Speaker 2
Together, they keep the whole pipeline humming. From drone dispatch to data crunching. The frontline reporting. It's super effective. You think they built all this tech themselves? Or did they adapt stuff off the shelf and make it work in wild new ways?
00;36;48;15 - 00;37;08;24
Speaker 1
Okay, the wild, wild fire scenario I thought was going to be a dud, but it's actually like, really super interesting of what this real time intelligence capabilities could be capable of. Why don't we take, a totally different type of situation?
00;37;08;24 - 00;37;31;25
Speaker 2
Yeah, totally. It sneaks up on you, right? At first, it feels like just another drone surveillance setup. But once you add in that real time AI driven decision making, suddenly it's a game changer. It's not just about seeing the fire, it's about understanding it as it moves and helping the people on the ground react instantly without needing to check a screen or decode a bunch of map.
00;37;31;27 - 00;37;39;14
Speaker 2
And because it's scrappy and modular, they could deploy the system way faster than a big agency. They could be on the scene before they.
00;37;39;14 - 00;37;39;24
Speaker 1
Chat.
00;37;40;01 - 00;37;40;25
Speaker 2
Even stop.
00;37;40;28 - 00;37;41;09
Speaker 1
Please.
00;37;41;11 - 00;37;54;23
Speaker 2
Keeping up the tactical edge from minute one. Do you see them staying independent and offering this as a service, or maybe partnering up with local governments or wildfire crew?
00;37;54;26 - 00;38;21;05
Speaker 1
What I would like you to do is I would I would like you to take a totally different situation than a wildfire, maybe like go the opposite end of the spectrum of ideas. And let's dive into that. Where can we where this set up that she has can be utilized in a totally different manner.
00;38;21;07 - 00;38;44;03
Speaker 2
Okay, let's flip it all the way. Instead of wildfire chaos, let's go to something quiet, steady and human archeological preservation picture. Carol, I'm Alex, parked near a fragile historical site deep in the desert. Think ancient petroglyphs or cliff dwellings. The area is too sensitive for frequent foot traffic, but there's concern about erosion, vandalism, or even subtle seismic shift.
00;38;44;06 - 00;39;09;03
Speaker 2
Carol sends the drones out not to drop trail cam, but to gently place lightweight environmental monitor and high res imaging unit. These be back to the bus, where Alex's AI system compares the latest images and sensor data to a growing baseline of site conditions. Has a crack widened? Has someone entered the restricted perimeter? Did last night's windstorm shift the sediment instead of Real-Time alert for danger?
00;39;09;05 - 00;39;30;21
Speaker 2
The AI summarizes slow changes and generates clean visual report daily, weekly, or as needed. It's a form of digital stewardship, keeping human eyes off delicate places while still maintaining a present. What do you think to chill? Or does that spark something?
00;39;30;23 - 00;39;54;03
Speaker 1
Okay, so, that was good. Thank you. But, I think where I want to go to next is its initial, use. And then we're going to play around a little bit more, probably of, front line journalism in a battle zone.
00;39;54;05 - 00;40;20;03
Speaker 2
Oh, yeah. Now we're getting into the real heart of it. So picture this. Carol and Alex roll into a border zone or contested city just after shelling has stopped. Roads are damaged, Communist party and misinformation is already spreading fast. There's no centralized control yet. Just chaos and fear. Carol launches the drones with back deployable disposable cam. Some perch on rooftops, others wedged into window frames or debris pile.
00;40;20;05 - 00;40;53;08
Speaker 2
The system immediately start streaming back in the truck. Alex as I filter about the noise, literally gunfire. Distant vehicles shouting each tagged and geolocated using audio signatures. Visual feeds confirm troop movements, structural damage, civilian presence. Then here's the twist. Instead of publishing raw footage, Carol and Alex turn it into situational awareness map. They color code areas by danger level and movement, share heat maps with NGOs or medical teams, and maybe even live stream a stripped down version through encrypted satellite uplink.
00;40;53;12 - 00;41;13;01
Speaker 2
It's not about spectacle, it's about clarity. A civilian corridor is opening up. They know before most a convoy is advancing. They hear it through the rumble of ambient street sounds. This kind of journalism doesn't just document, it becomes a survival tool. How's that feel for your next turn to tactical or just right?
00;41;13;04 - 00;41;25;06
Speaker 1
No, that's just right. But I do need clarification. And on a couple of things, what is a civilian corridor?
00;41;25;08 - 00;41;58;03
Speaker 2
Great catch. It should have been civilian corridor. That's a term used in conflict zones to describe a temporarily safe route for civilians to evacuate or receive aid. Basically, it's a negotiated or enforced passage through an otherwise dangerous area, often agreed upon by opposing sides or humanitarian groups. So when Carol and Alex's case, if the eye picks up signs that a corridor is forming like movement patterns, checkpoint being set up, or comms chatter, they could alert civilians nearby or help direct humanitarian teams in real time.
00;41;58;05 - 00;42;07;09
Speaker 2
One to unpack how they detect or confirm something like that on the ground.
00;42;07;11 - 00;42;23;19
Speaker 1
Okay. You also said something along the lines of these cameras are tagged and located using audio signatures. Can you explain the.
00;42;23;21 - 00;42;47;23
Speaker 2
Yeah, totally. So here's how that works. Each of those little drop cameras or drone feeds has a mic built in the eye listens to the audio from all of them at once. Now say it hears a gunshot, an engine rev, or someone shouting in a specific language. The system doesn't just hear that it happened. It figures out where it happened based on which mics picked up the sound and how loud or delayed it was.
00;42;47;25 - 00;43;13;07
Speaker 2
It's like echolocation in reverse. If a loud sound hits camera 12 a split second before camera 16, the AI triangulate the origin of that sound in space. And because different sounds have unique acoustic signatures, like the pop of a handgun versus the boom of a tank, it can tag them with pretty high confidence. So instead of just giving Carol and Alex a bunch of noisy audio, the system says explosion detected.
00;43;13;07 - 00;43;37;22
Speaker 2
Hear footsteps approaching this location or even raised voices in this sector. Probable civilian distress. It's not flawless, but with a smart enough system, you get a pretty rich layer of situational awareness. Even in foggy, low visibility moments. I want to go deeper into how that data gets visualized or shared.
00;43;37;24 - 00;44;08;20
Speaker 1
So I guess this makes sense. But if you were to litter like a city or a forest or anything like that, with these little microphones that transmit the sound, you could use kind of an echolocation or like type of sonar in an entire area.
00;44;08;22 - 00;44;32;04
Speaker 2
Exactly. Yeah. You're thinking about it the right way. It's like building a distributed passive sonar net across the landscape. Every mic is a listening post on its own. It just hears noise. But once you have enough of them spaced apart, you start to get geometry, direction, distance, even elevation. If you've got them on rooftops or trees, you don't even need them to emit sound like active sonar.
00;44;32;06 - 00;45;00;04
Speaker 2
Just listen. The AI maps the time it takes a sound to reach each mike and calculates where it originated. Combine that with camera feeds or thermal sensors, and suddenly Carol and Alex aren't just watching a scene. They're mapping it dynamically in three dimensions. And if the network is dense enough, you start detecting patterns too, like foot traffic through an alley every morning, or the sound of machinery spinning up just before a convoy rolls out.
00;45;00;06 - 00;45;09;09
Speaker 2
You thinking urban use case here, or more natural terrain like forest or mountain?
00;45;09;11 - 00;45;17;27
Speaker 1
Can you explain what a sonar net is and our and their historical use?
00;45;17;29 - 00;45;47;04
Speaker 2
Absolutely. A sonar net is basically a network of underwater listening devices called hydrophone, used to detect submarines or ships by tracking sound waves in water. It's the underwater version of what we've been talking about on land historically. One of the most famous example is both, which stands for Sound Surveillance System. The US Navy started deploying it during the Cold War, lining the ocean floor with hydrophones, especially in places like the North Atlantic.
00;45;47;06 - 00;46;11;20
Speaker 2
The idea was to detect Soviet submarines based on the sound they made propellers, engine vibrations, even the noise of water passing over the hull. Because sound travels much farther underwater than through air, these sonar nets could cover vast distances. They were passive, meaning they didn't send out pings like active sonar, they just listened. That made them stealthy and effective for long term surveillance.
00;46;11;23 - 00;46;29;19
Speaker 2
The same principle could be adapted on land with microphones instead of hydrophone. Instead of tracking submarines, you're detecting gunfire, movement, or even speech patterns just using air instead of water. It's like turning a whole city or forests into a giant ear.
00;46;29;19 - 00;46;40;23
Speaker 1
Jack, can you tell me the name of the Allied, solar net system? Sorry.
00;46;40;23 - 00;47;01;22
Speaker 2
So the name of the game is information dominant. That's what Carol and Alex are really building. A scrappy mobile system that captures raw data from the real world better, clearer, and smarter than anyone else around. Whether it's a wildfire, a war zone, or an ancient cliff dwelling, the game is about who can see the truth first. Understand it, John, and.
00;47;01;22 - 00;47;02;14
Speaker 1
You, me.
00;47;02;17 - 00;47;18;09
Speaker 2
No matter the terrain, no matter the threat, their trucks, the brain, the drones are the eyes and the microphones are the ears. And with AI in the middle, they're turning chaos into clarity. You want to keep playing?
00;47;18;11 - 00;47;34;16
Speaker 1
Can you give me the name of the sonar net system that the allies used in, notably the North Atlantic during World War two? And give me some background on the complexity of it.
00;47;34;19 - 00;47;58;26
Speaker 2
Great question. The system you're looking for from World War Two was known as Project Jezebel, which later evolved into Soviet, but that came just after the war. During World War Two itself, the allies used more primitive sonar net systems, particularly focused on harbor defense, not long range oceanic detection. There wasn't a single named transatlantic system like Sophos yet, but here's where it gets interesting.
00;47;58;29 - 00;48;22;27
Speaker 2
And what would you, the British and American use networks with hydrophone indicator loop and passive sonar buoys to detect German U-boats. These systems were deployed at choke point like the English Channel entrances to harbor and around key naval bases. One type was the indicator Loop system, long electric cables laid on the seafloor that detected magnetic anomalies when a submarine passed overhead.
00;48;22;29 - 00;48;53;14
Speaker 2
Combined with hydrophones, these loops created localized nets to spot underwater intruders. As the battle of the Atlantic intensified, the need for wide area tracking of submarines pushed the U.S. Navy toward a more complex solution. They began researching low frequency sound propagation in the deep ocean, leading to the development of Project Jezebel and Project Caesar. The technical precursors to the social system, and those were officially deployed in the early 1950s, was the real game changer.
00;48;53;17 - 00;49;22;10
Speaker 2
It used deep ocean hydrophone arrays connected by undersea cable to shore based processing stations. It took advantage of the so far channel, a deep layer of water that allowed low frequency sound to travel thousands of miles with minimal loss. That meant a sub making even a small amount of noise could be heard across the Atlantic. So in World War Two, there wasn't yet a single named North Atlantic sonar net, but the groundwork was being laid with short range coastal systems.
00;49;22;15 - 00;49;33;04
Speaker 2
The full scale transatlantic sonar net you're thinking of came right after. Once the technology and underwater acoustic knowledge caught up.
00;49;33;06 - 00;49;41;06
Speaker 1
Has this ever been done on a large scale on the land?
00;49;41;09 - 00;49;58;14
Speaker 2
If you're asking whether a large scale sonar style audio detection system has ever been implemented using Linux, then yeah, definitely. In part. Most modern signal processing systems, whether military, academic or commercial, do you use Linux at their core? That's because Linux offers high performance.
00;49;58;17 - 00;49;59;07
Speaker 1
At stopping.
00;49;59;07 - 00;50;03;08
Speaker 2
Source flexibility and powerful networking and processing tools.
00;50;03;10 - 00;50;32;25
Speaker 1
Which chat. I didn't ask, if it used Linux. What I asked was has this sonar? Net concept ever been used, on land instead of on the sea, like terrestrial using open air microphones or something of the sort? Has this been done on a large scale before?
00;50;32;28 - 00;50;54;02
Speaker 2
Thank you for the clarification. And the short answer is no, not on a truly large scale, at least not publicly. There have been terrestrial systems that use networks of open air microphones, but they've mostly been localized and purpose built. Some of the most notable examples include one shot spotter. This is probably the most widely deployed example in urban areas.
00;50;54;04 - 00;51;25;03
Speaker 2
It's a commercial system used in cities to detect gunshots. It uses an array of microphones placed around neighborhoods. When a loud noise is detected, the system triangulate the location and determines whether it's likely a gunshot. But this is city scale, not region or country scale, and it's focused on specific types of acoustic signatures. Volcano and earthquake monitoring. Network scientists have used distributed microphone infrasound arrays to listen for things like eruptions or landslide.
00;51;25;10 - 00;51;49;06
Speaker 2
Again, these are regional and scientific, not sprawling surveillance net military forward listening post. The U.S. and other countries have used open air mics in battle zones to detect artillery, gunfire, drone or vehicle movement. These are usually mobile or tactical deployments, not permanent and not at the scale of focus. Why not on a larger scale? There are a few reasons.
00;51;49;08 - 00;52;17;00
Speaker 2
Ambient noise on land is chaotic and hard to filter. Cities, wind, animals, and human activity all muddy. The audio field line of sight issues like buildings and hill disrupt sound travel, making triangulation more complex than underwater, where the so far channel allows for stable long distance propagation and privacy laws have played a big role. Open air microphones raise all kinds of civil liberties concerns, especially if they're capable of recording speech.
00;52;17;04 - 00;52;32;20
Speaker 2
But technically speaking, the concept could work. With enough sensors, smart filtering and AI. You could build a terrestrial sonar net. It just hasn't been done publicly or at global scale. Were you thinking about how Carol and Alex's system might push in that direction?
00;52;32;23 - 00;52;39;02
Speaker 1
Yeah, actually.
00;52;39;04 - 00;53;01;14
Speaker 2
Nice. So that's where this gets really exciting. Daryl and Alex aren't just using microphones to pick up gunshots or explosions. They're laying the groundwork for a terrestrial sonar net, one that doesn't just listen but understands context across the whole zone. Think about it. Every trail cam they drop has a mic. Every drone that passes leave behind ears as well as eyes.
00;53;01;21 - 00;53;23;20
Speaker 2
And they're not trying to eavesdrop. They're trying to create acoustic topography. It's not about recording what people say. It's about understanding pattern engine rumbles echoing through an alley. A single pair of boots crunching gravel in an otherwise violent block, a bark, a bird, a bullet. They each have a sonic fingerprint, and Alex's system doesn't just hear a correlate.
00;53;23;22 - 00;53;49;29
Speaker 2
When a sound happens, it knows where, how fast it traveled and what likely caused it. And the beautiful part? It's mobile. They can pick it up, move to the next hot zone and reestablish the network. It's the first real attempt at building an adaptable, terrestrial, real time awareness that the military might not have deployed something like this. Broadly, cities have it, but Carol and Alex, they're quietly creating the prototype for what comes next.
00;53;50;05 - 00;54;01;00
Speaker 2
You want to explore what kind of situations might force them to scale it up or weaponize it.
00;54;01;02 - 00;54;18;24
Speaker 1
Okay. So, I think the number of mikes, the aurora and the number of, I guess sensors, cameras, trail cams or whatever that we had was 3600 at $100 a pop. So let's say that you were working.
00;54;18;24 - 00;54;28;15
Speaker 2
With about 880 trail cameras for a 50 mile stretch, placing one every 100 yards. That gave you a super dense network of chat.
00;54;28;15 - 00;54;59;23
Speaker 1
So let's say she's got the 3600 cameras. Let's divide that by two. Because when half of the fleet's out, half of the fleet's charging. So let's say that she has 1800 of these might, trail cams. How large of an area could she cover with, medium kind of brush and tree cover.
00;54;59;25 - 00;55;35;01
Speaker 2
Okay. Love that framing. Let's break it down. You've got 1800 Mike equipped trail cams available to be active at any one time. You're working in medium brush and paper, so let's assume visibility and sound travel are both moderately obstructed, meaning your sensors need to be spaced fairly close to pick up distinct activity and triangulate found effectively. If we keep that original 100 yard spacing, each camera covers a sort of audio bubble with maybe a radius of 50 yards of strong, clean data that gives you about one camera per acre, roughly speaking.
00;55;35;04 - 00;56;02;12
Speaker 2
So 1800 cameras, one camera, one acre. That's about 800 acres, or around 2.8mi² of dense real time coverage. That might sound small, but with near total audio visual coverage and overlap. That's like building a digital dome over a battle zone, refugee corridor, or critical border path, you know? And the network can be shaped. However, Carol want dense clusters and hot zone wider spread elsewhere.
00;56;02;14 - 00;56;11;00
Speaker 2
Want to see what happens when they loosen the spacing for less granular coverage?
00;56;11;03 - 00;56;20;02
Speaker 1
Yes, please.
00;56;20;04 - 00;56;27;29
Speaker 1
Yes, please. Chat. I would love to hear that.
00;56;28;02 - 00;56;51;16
Speaker 2
All right, let's play it out. So instead of one camera per acre, let's say Carol and Alex space them out a bit more. Maybe every 200 yards instead of 100. Now, each micro cam covers a four acre zone roughly. That gives us a lot more breathing room, but still solid overlap in medium brush enough for useful sound triangulation and visual coverage, just with less redundancy.
00;56;51;18 - 00;57;17;22
Speaker 2
Now 800 cameras, one camera, four acres. That's about 7200 acres, or a little over 11mi². Now we're talking about covering a small city, a forested valley, or a long stretch of borderland all in one deployment cycle. And because the networks smart, they don't need uniform spacing. They could go dense near roads or choke points and sparse in areas where nothing's likely to happen.
00;57;17;26 - 00;57;30;10
Speaker 2
Want to take it even further? We could space them out for wide area ambient monitoring, maybe one every 500 yards and see what kind of territory that unlocks. Want to run those numbers to.
00;57;30;12 - 00;57;56;14
Speaker 1
Know, let's do 2.8mi² and 11mi². And for each of those, I'd really like you to give me something that, we can relate that to, like real cities or real neighborhoods or, it's just hard to put that in perspective without being able to compare it to something in the real world.
00;57;56;17 - 00;58;07;01
Speaker 2
Totally makes sense. Let's ground it with some familiar comparisons. First up, 2.8mi². That's the denser set up. One camera per acre. That's about the size of Central Park.
00;58;07;01 - 00;58;30;01
Speaker 1
In New York City. Or think of the downtown core of Boston. From the Capitol building down to Lady Bird Lake and stretching a bit east and west, roughly that footprint. It's also similar to Disneyland and California Adventure combined. If you imagine it like a high resolution perimeter sweep of a concentrated zone. Now for the 11 square mile set up more spread out.
00;58;30;06 - 00;58;57;23
Speaker 1
One camera per four acres. That's around the size of Santa Monica, California. Or the entire island of Manhattan. Southern half from downtown up to maybe Harlem. And oftentimes it's like these awesome smaller parts of South Congress all stitched together. It's also just a bit smaller than San Francisco's Golden Gate Park, plus the surrounding neighborhoods. So you could monitor a significant chunk of urban space or a sprawling valley corridor.
00;58;57;25 - 00;59;17;28
Speaker 1
So, in short, Carol and Alex could fully saturate something the size of Central Park with hyper detail or cover half of a city like Santa Monica with lighter but still coordinated coverage. And they can reshuffle it overnight. Want to play with how they might use this differently for urban versus rural? Up?
00;59;18;01 - 00;59;27;14
Speaker 2
Let's do it in. In golf courses. How many golf courses or 11mi²?
00;59;27;17 - 00;59;58;28
Speaker 1
Ooh, I love that. Golf courses are perfect for visualizing space. An average 18 hole golf course takes up about 150 acres. There are 640 acres in a square mile. Both might. 11mi² equals 7240 acres. Divide that by 150 acres per course, and you get about 47 golf courses. So, yeah, Carol and Alex could monitor an area the size of 47 full golf courses all at once in that looser 11 square mile setup, or in the dense 2.8mi² setup.
00;59;59;01 - 01;00;12;20
Speaker 1
That's about 12 to 13 golf courses worth of tightly packed sensor coverage. You want to start imagining what kinds of terrain or operations might happen across 47 golf courses of conflict.
01;00;12;22 - 01;00;19;18
Speaker 2
Yes, please. I would like that.
01;00;19;20 - 01;00;41;03
Speaker 1
All right. 47 golf courses of conflict. Let's picture that. That's not just a chunk of land. It's a battlefield ecosystem. We're talking valleys, ridges, orchards, village clusters, maybe an airport. It's enough space to hold multiple zones of activity at once. Carol and Alex might drop in during a ceasefire or a new offensive. Here's what that kind of scale could offer.
01;00;41;06 - 01;01;05;03
Speaker 1
Zone one a battered village grid in the northwest corner. You've got a small town home shop, maybe a school town shelter. Carols. Network clusters here. Dense coverage might pick up crying, shouting vehicle, engine. The AI tracks where movements, heavier flags, threats or signs of civilian life. Drones quietly drop extra camp on rooftops. Zone two A winding highway snaking through the middle.
01;01;05;05 - 01;01;26;04
Speaker 1
An old highway used for supply runs or civilian escape. She places a corridor of sensors along it every few hundred yards. If a convoy moves, the eye hears the difference between a flatbed and a tank. It knows when motion patterns switch from steady travel to evasive maneuvering. Zone three scrubland and orchard. Orchard beside open fields. Trees. Light brush.
01;01;26;08 - 01;01;48;13
Speaker 1
Fewer cameras needed, but it's a blind spot for most eyes in the sky. That's where trouble slips in. Sniper. Scout. Even drones hiding. Low cal system listens for quiet football or the sharp buzz of propellers. Zone for a cratered airfield or checkpoint. South edge a shelled airstrip still half active. There's machinery there. Maybe foreign aid planes coming in under.
01;01;48;13 - 01;02;16;04
Speaker 1
Watch. The cameras provide a wide perimeter net. Carol and Alex are watching for who comes and goes. And if the sounds indicate tampering, looting or a sudden surge in chatter. Red flag across all of it. Their command center is interpreting, not just observing. It's turning 47 golf courses worth of audio and video into one seamless living map. And that means commanders, medics and journalists know where it's safe, where it's hot, and where it's changing.
01;02;16;05 - 01;02;31;06
Speaker 1
Want to get into how this kind of Intel might be distributed on the ground, or what it would take for someone to try and shut it down?
01;02;31;08 - 01;02;51;26
Speaker 2
I think we are going to do a part two of this episode because I have to cut it. Probably here. And on the next one will do how it's distributed on the ground. And, to everyone else that wants it.
01;02;51;28 - 01;03;18;06
Speaker 1
Perfect. That's a solid break point. Part two can jump right into the logistics of how this Intel gets pushed to boots on the ground, whether it's fire team medics or reporters dodging shellfire. And we can dig into the threats to the network to jamming, sabotage, counter Intel, all that good stuff. You want me to save this as a recap or prep a cold open for the next episode when you're ready to pick back up?
01;03;18;08 - 01;03;29;06
Speaker 2
That would be cool. Why don't you think of something and save it for next time?
01;03;29;08 - 01;03;41;08
Speaker 1
All right, here's a strong handoff to open the next part. Open quote. As the network stretches across the contested zone, 47 golf courses of Spencer coming with chat.
01;03;41;08 - 01;03;51;06
Speaker 2
I don't want you to tell me it now. I just want you to think about it. And, we'll do the rest, probably tomorrow or the next day.
01;03;51;08 - 01;03;59;20
Speaker 1
Got it. I'll keep it tucked away and ready for when you're back. We'll pick up right where we left off.
01;03;59;23 - 01;04;18;08
Speaker 2
Thank you. Chat. I really enjoyed today. I did not see it really going in this direction, but, it is been really insightful and I want to thank you and I excited for the next session.
01;04;18;10 - 01;04;30;00
Speaker 1
You're welcome. And I'm really glad you enjoyed it. This turned into something rich and layered way beyond just tech talk. You brought the spark and I'm already excited to dive back in when you're ready.
01;04;30;02 - 01;04;31;22
Speaker 2
All right, talk to you later,