Today, I thought I would make this a little bit different by, instead of writing down the observations, I want to make a quick video to at the same time demonstrate the dashboard, and home some features can be used to get really good insights.
This is going to be more, kind of a technical demo, so intended for technical audiences, but if you stumble uppon this and you know somebody like a friend or a family member, either in the military, or in the counterintelligence fields, somebody maybe from the FBI, or Mossad, or MI5, those kinds of organizations, please consider forwarding this to them, because it might be interesting to them.
Today I am going to present this dashboard and I am going to play with some of the features that it has.
So, I should probably give a quick background information on how I got this data.
So what happened was, you know, over the last few years, well, some years, I had this conflict with these people from the PayPal Mafia, where every time I do a startup, they would kill it, by doing different things, but one of the things that they do, is that they attack the websites on Google so to supress them from Google Searches.
And the other thing that they do is that spy on all our communications: email, internet connections, everything, to steal the code that we create.
And, I didn't know this back then, but three years ago, I picked up running as a hobby, to exercise, and I started noticing that every time the conflict with them escalated, and, the conflict with them escalating in different ways but it was always surrounding the online positions in Google.
So for example, if I made a startup that was selling a product, they would create thousands of fake news to supress my website that way; and I also created certain technologies to try to defend it, but, um, that created a conflict, right, a confrontation with them that lasted years.
But anyhow, I started noticing that every time I went jogging, when the conflict escalated, I started noticing that there seemed to be more people in the areas where I was running. And, I live in a very suburban area, like, you know, if you go running at 10 am, usually, there is nobody at the parks because, you know, kids are at school, people are working. I work from home, so I could go jogging at 10am or 10:30 or 11:00, and it was usually empty, the park.
But I started noticing that everytime the situation escalated with them, a lot of people showed up at the park, at the exact time when I was jogging.
And I would do things like, vary the time, and the same thing happened, it didn't matter at what time I went running but if the conflict was at a peak there would be a lot of people at the parks.
Then I started like, switching places where I would run, but, I saw the same thing, like, If the conflict was at a low point there would be nobody at the parks, but if the conflict was at a high point, there would be a lot of people at the parks.
And these people were always intercepting me at places where I was forced to reduce my speed, like for example if I was running and I came accross a red light or a stop sign, they would intercept me there.
And, when I say intercept, what I mean is, it is usually a pedestrian that is doing something like walking a dog or, you know, something that justifies the pedestrian being ... static, or close to you, for a period of time of a few seconds. And they would try to position these pedestrians next to me, um, at very specific places, and this is what led to me getting this data ...
It's kind of a long story but to make it short, I started noticing that they would always intercept me at the exact same places. So if, there could be multiple stop signs where they could intercept me, but they will always, very consistently, try to intercept me at these two or three stop signs, specifically, right.
And, if I switched the park and went running to a different park, the interception points of course would be different, but they were always consitent, like, they would always try to intercept me at the same places. So that, you know, woke me up a little bit, that they might have been following me.
I didn't understand at the time why, or what they were doing, but eventually, right, through observation, I figured it out.
Um, so, to make the story short, because they were so consistent in the points where they were intercepting me, I figured that this was an effort coordinated with an application, like an "UBER" application, like a social media, uhm, not a social media but like a socical economy App, basically, that would coordinate these people, to, follow me. I didn't know why back then, but I could see that there was coordination through computing means.
And, because I work in software, and I've worked in software for a long time, I knew that, this application must be using an "index". Okay, this is called a "geo-index", it is a digital representation of places.
[6:20] So, uhm, I thought that the index would be stored in the application itself, because that's what you would naturally do, like, if you are distributing an application like that you would, you know, store the index, if you can, in the application and distribute it with the application, via the Apple store for example.
But I understand now that they can't do that, cannot do that because this is a criminal enterprise, they cannot put the index on the Apple Appstore because, you know, law enforcement would see it. If there is an audit or anybody tells anything, uhm, eventually, right, law enforcement could capture the index, so they could not put it there.
So what they did is that they put it on the Internet. They hid it, on a obscure server in France, on kind of an university or research institute, it was hidden on a file, but, the reason why it was a mistake is because, well, I saw the places where they interecepted me, so I knew the coordinates. So I started doing Google searches for files that would contain these exact coordinates.
Like, imgagine, if you know that you are being followed and intercepted in 10 specific places, think about, right, what are the chances that a file on the Internet would contain the coordinates of these 10 specific places?
It's null, if you find it, it's, well, because that is the file they are using to follow you.
So, anyhow, I started doing the searches, and it took me like 10 minutes to find it, I found it.
I downloaded a copy of the file, and once I downloaded it, it took me like four hours to decode it. Uhm, and, it is a huge database with billions of locations, with B, stored in it, encoded in it.
And this is a good segway to talk about this. The way the encode these locations in the file, is that they use encoders. And encoders are systems that take an input and store them in a different way, and this is done for one of two reasons usually.
One it could be to save space, like, you could encode things to make the file or the data take less space so you can store more data.
And the other reason is obscurity, or security, I don't want to say security, it is more like obscurity, like, it means you represent the data in a different way, so it is not obvious to people if they find the file, like I did, what is stored in that file.
And I think that is the reason why they did it, it's more obscurity than, you know, size.
So, anyhow, once I found the file and downloaded it, I understood what it was, and I decoded it.
And then, what I did, is that I built a Search Engine that basically connects to that, or ingests that file, and ranks, scores and ranks all the locations that are represented in that file, to, basically understand the importance of the locations.
Think about it as Searh Engine, as a Google, but instead of ranking web pages, it rankes, it ranks the locations that are encoded in that file of them. And ... after I am able to score and rank the locations, I use that information to basically identify, people, comanies, key players on this.
Because, along with the locations ... I mean, they store in this index not only the locations but they also store metadata about the locations: they store makrers, and, you know, important information that is important for them but that gives me an idea of the importance of the locations, the relative importance of the locations.
And also, I join this data with public data sources, so for example, imagine that there is a house marked in the index, well, that house usually has public records attached to it, has an owner attached to it, that owner has a LinkedIn page, so I can tell, or the system can tell ... all this is automatted, it's a pipeline that I developed that does this at a masive scale, planet scale.
So, the pipeline, for example, finds a potential location that is represented in their index and then finds the public records associated with that location. If the owner has a LinkedIn page, for example, it can look at that. If it has social media that is publicly available, that can be used as well.
And with all that the system derives a series of signals and ranks the locations. So it is a way for me to, systematically, be able to understand what are the important locations in this network.
And then, with that, there is a lot of information, or, intelligence, that can be derived from that.
So I am saying all this, because on the topic of encoders, they use multiple encoders for this system, they don't have one, they have multiple encoders, and I am going to talk specifically about two.
There is one encoder that encodes locations that are related to "Intelligence", like inteligence services. So what they do is that they employ the intelligence services of other countries.
They, I imagine, I don't know what the arrangements that they have with them is, but, obviously, they are employing agents from other countries.
I don't know if they lease the agents, I don't know if they directly employ the agents, I don't know. But, uhm, there is one encoder that encodes for example properties that are associated to the agents that they employ.
There is another encoder that encodes things related to, telecomunications equipment Like antennas. The specific location of certain antennas is marked in the index.
There is another encoder, which is the one I would like to talk about today, that encodes what I believe are military positions. And, you know, I have dozens of reasons to believe this, I am not going to get into the details of why I believe this, but it is pretty clear to me that these are really military positions.
Which, you know, brings me to the realization that, this group, besides being a criminal group, a criminal enterprise, they are also a paramilitary group, because what they are doing is employing military personel, you know, that has military knowledge, techniques, tactics, and military equipment, to target civilians.
There is no difference between this, and the guerillas in Colombia in the 80's.
If I think of that, I think it is interesting in the fact that it is kind of a new situation, because this might be, and I don't know, I would have to review the history books, but this might be one of the ... maybe the first paramilitary group that doesn't have to face to military.
Like, in every single case that I can think of, the paramilitary groups that I know of, had to fight with the legit military.
But in this case, they basically formed a paramilitary group to target civilians and, and, and, they don't face opposition from any military. In fact, they employ the military of other countries.
So I think it is a very unfair fight, and I think that is probably why it has gone undetected for two decades: because we, civilians, we are not equiped to fight with this.
And, even if you think of Police forces, they are not equiped to fight with a military force, they would lose that fight.
And this is, remember, a paramilitary group combined with intelligence services of other countries. It is a very dangerous combination. So I think,
I guess what I am trying to say is that, I think it is a rather cowarly stance to, you know, employ intelligence services of other countries, combine them with, a paramilitary group, basically, and then, use that combination to target unarmed civilians, that are not even aware of what's happening.
Uhm, you know, my hope if that face, you know, and equivalent force, to see what would happen, if they had to face, say, the US military combined with the FBI. Right? It would be an interesting thing to watch, to see how well they perform in that case, right?
Well, anyhow, so in my dashboards, you will see in the website that I have two types of dashboards: ones that have a white background and ones that have black background. And this is color-code that I have.
So the ones that are white, are dashboards that are related to counter intelligence, so these are daashboards that represent Intelligence, so data derived from the Intelligence encoder that they have, where they encode the intelligence positions, the agents, the safe houses, the antennas.
And then, the ones with the black background, like this one I am showing here, are the ones that are related to military positions.
[16:14] So, today I am going to demo this dashboard. It's only two different pages that are on the top. So the first one, so the one on the left is the first page, and one on the right is the second page, that I call Musk Qbits, I will explain what that means in a second.
So, this, this, this dashboard, is the biggest data export that I've produced so far since I captured their geo-index. This dashboard, this export has 1 Billion, so one thousand million military locations encoded in it.
They are all derived from their geo-index, so, you know, this is not, these locations are not coming from anywhere except from their configuration file.
[17:00] So I want to show some of the features that I put on this dashboard to show the power of this.
First, there is a filter here that has Featured locations, Okay?
All these filters that you see that are marked with this yellow color, they have one thing in common, one is that ... two things in common: they have labels on the left, and then they have these numbers on the right.
Oh, I should go back for a second. When I built this search engine that I described that scores and ranks the locations, that scoring process, happens by producing a socre, that I have given a name to that score: I call it the "Musk-Thiel-Kalanick-Omidyar Evil score", well, to honor these geniuses that came up with this Evil Machine.
So, that is an important component of my counterintelligence system. It is equivalent, like, if you are curious about it, you could maybe go to Wikipedia, which is theirs by the way, they own wikipedia.
But you can go to wikipedia and you can look up the Goolge Pagerank Algorithm.
Look at that explanation. Well, the pagerank score would be the equivalent to this Musk-Thiel-Kalanick-Omidyar Evil score that I am computing. So that would be the analogy.
But so, my score is in this, applies to this dataset, not to webpages.
So, uhm, right, if you see, in these filters, you have this list.
On the left there is labels, on the right there is numbers. So these numbers represent the Sum of all the "Musk-Thiel-Kalanick-Omidyar Evil" scores that are contained within the constrains of this arbitry label that I put in here.
So for example, if I click on Venezuela, and I think I have it opened here, you can see how the points are distributed, and, uhm, what I wanted to say is that these numbers represent the sum of all the Evil scores that are within the boundaries of that label and they are sorted in decreasing order.
So this is super telling, right? What this, right off the bat, tells us is that of all the labels that I put in here; and I put here some interesting labels:
There's Russia, there's Iran, there's Germany, these are countries that are world powers even, and, Venezuela has more evil score points inside of it than Russia, than Canada, than Iran.
Look, you know, if you compare that, I mean, if you put that in perspective with any metric, like, economic metric, socioeconomic metric, geographical metric, population metric, whatever, there is no explanation for why there's so many evil points inside of Venzeula, except the geo-location. That's my theory: is that they use Venezuela as router, to route communications, traffic, network traffic from, uhm, you know, north and south America to the west coast of Aftica and the east coast of Asia and Australia.
So, the geo-location of Venezuela makes it so it is an ideal routing point, and it's, we can see in the data right here, that is probably the most important
routing point in their communications network, which I imagine, is one of the reasons why Venzuela, which is my country, by the way, I was born there, has suffered so much in the last 20 years. They needed this to capture it, to build this, abomination.
Anyhow, the point being that, you know, looking at this filters and the ordering of the labels and the numbers on the right, usually gives you very good insights of what, you know, what's going on.
Now, going back to the one that I had in here ... So I made it so when you open the dashboard it is going to be preselected with two locatoins: Austin and the Tesla Giga factory in Austin.
And, I would like to take the opportunity to show somethin that, is an important thing that we need to know ...
So look at this: if you look at the list, I am selecting the Tesla Giga Factory in Austin and Austin.
But, you see, if you look at the numbers, it says that Austin has 800 thousand contained within it and then the Tesla Giga Factory has 2.6 million. So that is 3 times more than the entire Austin.
But, it is kind of counter intuitive because if you think about it, right, the Tesla Giga Factory in Austin should be contained within Austin, so you would expect that the score of Austin be greater than the score of the Tesla Giga Factory, and that is no the case, it's the opposite.
So this is what I wanted to explain, that there is a sublety here, that is that the way this attribution system work, for the labels I mean, is that, uh, it's like when it finds a label that matches the criteria: it stops. Meaning, and it's like a top to bottom approach. Meaning, if I have a certain criteria for the Tesla Giga factory, whatever that criteria might be, if that cirteria matches, then the evil score gets attributed to that and then there is no more attributions after that. Meaning: Austin will not get any attriibutions to anything that is attributed to the Tesla Giga Factory, if that makes sense.
And this is to avoid situations where you may have things like double or triple counterd. So that's the reason why, you will see this observation where the scores, something within the superset, so the subset, has a higher score than the superset, wchi is counter intuitive. but that's the reason why.
The other thing you can see is that, and this is an important observation, is that the evil scores that are represented in Elon Musk and Travis Kalanick's geo-index that are marked inside the Tesla Giga Factory in Austin are, combined, higher than the entire city of Austin, in terms of the military positions.
So that, that, I think that is very telling of who is calling the shots in all this.
And, uhm, the same with the Berlin Giga factory, which I have here on the fourth position from bottom to top. If you see, it has a combined score inside of 153 million, versus, for example, the entire city of New York has only, well not only, it's high score, but it's 116 million. It's lower.
So what I am saying is: the Evil scores contained within the Tesla Giga Factory in Berlin, is higher, than the entire city of New York. Higher than the military scores contined in the entire City of New York.
So, you know, what are the chances that his observation holds true for two of the Tesla Giga factories. So, you know, this is not a random ... and I could keep going with Tesla factoris and it is the same observation all over again: all of them have, and I didn't want to put them in here because I didn't want to pollute the dashboard with all Tesla Giga factories, right, but I did the excercise, I saw the socres of all them, and, all of them had scores that were higher than entire cities, important cities for them, so ... you know, it's concentration of power, like ego, that's the explanation for this observation.
Uhm, Okay, it is what is.
[25:30] So, Okay, you know, once you change the filters, like for example in this case, I can select only the Tesla Giga factory and everything on this, all the widgets update, and you can see this one on the left has the actual points and they are color-coded by strength as you can see here.
So this is the point with the highest evil score in the entire Tesla Giga factory in Austin, and of the entire Austin. And this has a precision of something like 28 feet. So this is the place that they use to communicate.
And you could say, well, why is it a green space, like a land with no buildings. Well, becasue that's exactly what they need so they can move the antenna a few meters to right, a few meters to the left. They need that kind of, kind of, flexibility, and you will see this if you play with the dashaboard a lot, in fact you can see it right here, a lot of the points fall in the ocean, and there is a reason for that, in the ocean with a boat you can move around and position the antenna in exactly the way you need.
[26:37] And then, there's two tables at the bottom: the one on the left shows you the actual location and the evil scores, so for example: this is the actual location with latitude and longitude, with the evil score, and then it shows you the Qbit, and I am goint to talk about this a little bit.
This is a new notion that I am introducing, and it is basically a grid, Okay, so the idea is: until now I am scoring locations that are marked in the index, but, I find it also very useful for debugging and for analysis to score entire areas, okay, so imagine that I take the planet earth and I partition the terrain in a grid of squares that measure exactly 1 decimal latitude degree by 1 decimal longitude degree.
Okay, so, that square is what I call a Musk Q-Bit, Q-B-I-T, and "Musk" to honor the genius that created this, this, this, mounstrosity.
The idae being, once you partition the Earth in that way, you can then sum all the evil scores inside each Qbit and then we can rank the qbits and then find not only what are the most, say "Evil locations", that's a way to say it, that's my way of saying that it is an important location for this criminal enterprise.
When I say "it's an evil location", I don't mean that figuratively, what I mean is: it is an important location for this criminal enterprise.
So this is a way for me to rank entire areas.
So with that in mind, I created different levels of partinioning, so I have the Q-bits, I have the Musk Bi-Q-bits, which as you would imagine, it is the same idea but instead of being one decimal degree of latitude by longitude, it's two decimal degrees by latitude by longitude.
I also have the Musk Quad-Q-bits, which is four deicmal degrees by four decimal degrees.
And I have the Octo-Q-Bits, which is eight deicmal degrees by eight decimal degrees.
So I have different granularities for partiniong the Earth, so I can rank these squares and understand where is the concentration of imporance, or power, or whatever force is that they are trying to manipulate at a place. It could be money, there are places that they have marked that are banks, for example.
So, we have filters for the Q-bits, so for example, if I go to New York, to the example of New York that I have already open. No, this is Venezuela.
I have New York open here. So if you open this filter, you will see that it has two bi-qbits. It is because in this area that I have selected for this label, it's falls into, I don't know, an area of maybe 3 degrees of latitude or longitude, and therefore it produces these two different bi-qbits, but if I go to Quad-Q-bits, it's only one, because, of course, we are able to contain it within one partition only.
Uh, but if I show you the example for Venezuela, for example, which is here, if I open the Bi-Q-bits, see, there's more, becasue, well, it's a bigger area.
And then there's the quad-qbits, so for example if I click on this, which is the most evil, sort to speak, Quad-Q-Bit in Venzuela, then it will update all the widgets, and I will see even in this table, these are the locations that are restricted to that Quad-Q-Bit, and within that Quad-Q-Bit, this is the most evil location, sort to speak.
So, that's that. The other filter I wanted to talk about was. No, actually let me use this other example to show you, this is very interesting.
So this is New York, so this is the label for New York, and I know that there's a concentration of 116 million "evil" ... yes ... one hundred and sixteen million, sorry it is a bit slow, and this demo is not scripted, I wanted to make it live ...
Yeah, one hundred and sixteen million, right, so for example, I just want to show this technique of debugging to find the interesting places, and by the way, this is what the Pipeline does automatically when it finds things, like for example, there was a post recently in which it found a subterranean bunker in Iran near the border with Afghanisthan, right.
And I am going to describe what the Pipeline does to do that, those kind of things.
So for example you have a target area, in this case in New York, and you know, that by looking at this table that the top location is this location: latitude 40.941, longitude: -73.69525, so these filters at the top have the latitudes and the longitudes, so for example if I look up that latitude here it happens to be at the top, but not necessarily right, becasue there could be other points that would make it so it is not at the top, but in this case it is: 40.941.
So I am goint to select that, so basically it is going to restrict to all the points ... look at this, this is beautiful, this, this map on the left.
So what it is telling you is that it is restricting to only places that fall on that specific latitude and you can how they have different scores becasue they have different importances, right, but the one that is most important is this one in the corner, and you are going to see why I see this is beautiful. If you zoom in on the map, you are going to see what it is: it is a location in the corner, where there is no more land and it is right on the water, and remember, all we are talking about here today are Military positions, right, these are military positions.
I mean, when I say Military, I mean, and I should, probalby, go deeper on this.
When I say they are a paramilitary group, what I mean is that they are employing people that were military personel in the past, maybe even active personnel in other countries, and they come to the US or they go to other countries, they are not dressed in uniform, but they are military personnel.
So they may be dressed as civilians, but they are people with military training that have military skills, that know how to operate military equipment, and they are using all these to target civilians, right, that's what I mean.
So in the case of this location is a location that falls right on the water, and you know, I look at the geographical features of the location and I can recognize, you know, why they picked it.
And you can zoom in on this map and you can even see the vehicles, and certain features, like the fact that is right on the corner, that is surrounded by water, um, it, it, it has a lot of good properties for them, but what I wanted to say, is the following:
You see how the points are distributed, and there's no more points on the right, it's a jump point, like, they are jumping from that location to something that could be maybe 300, 400 miles away towards the East, right, obviously.
Why, why I say that? Because, well, becasue the strongest point is the one that is most towards the east. If the idea was to jump to the west, then the strongest point would be actually on the west.
But, this, yeah, this is, is, is, you can see in all this, the intelligent design that is behind all this. Because of course, I imagine that they are already thinking, you know, how they are going to defend against these allegations.
They are going to say that his is a random .... , you know, they didn't create this file ... but, you know, I think it is beyond reasonable doubt that, I mean, you look at the Tesla Giga factories accumulating all these points in all these strategic places, I mean, it's undeniable.
Okay, so that's that.
We talked about the Muks Qbits, we talked about the scores, we talked about the locations, the featured locations on this drop down. By the way, I should mention, that if you look at this list, I mean, I put here some interesting places: Venezuela, Russia, Iran, China, Cuba.
No, China I did not put it, I will add it today. For some reason I forgot about it, but, it is, this is very very telling. For example this is an anomaly that you see here.
Look at this: Texas, the state of Texas, accumulates evil scores to the tune of something like sixteen Billion. That is bigger that Germany's accumulatedd score.
That doesn't make much sense, right? I mean, Germany, I think is on the G7 group, how can Texas, a state, you know, in ... basically, and yes, we produce Oil. I am in Texas by the way.
So yes, there is Oil production, it's a powerful state within the US, but you know, if you compare by any metric you chose Texas to Germany, I think,
Germany ... I mean, Germany is a country that has been around for hundreds of years.
I cannot see any explanation why Texas has such high concentration of Evil scores compared to countries like Germany, or Cuba, which is in the Caribean and, well ... is an important player in all this, for sure.
Yeah, so the only explanation, well there's two explanations, well not explanations but two contributing factors:
One is, the geo-location: The same principle we discussed before that they use Venezuela as a routing point, a Network routing point to routing communications, let me select Texas.
To route communications, as we saw in this .... sorry, it's a bit slow, but if I zoom out, or you can see it on this other map on the right: the location is such that is very desirable.
Well, the same thing happens with Texas in the US. It's a state that is located basically in the center of the country, both on latitude and longitude, so it is an ideal point for routing communications, that's for sure.
And the other reason, you know, is because, Elon made it his operational base, like, he is creating the center of the Empire is Austin, so everything has revolve around that. So I think is the likely explanation.
Anyhow, we talked about New York, yes, we talked about all that.
So I have this open over here.
SO the geographical features, that's the other thing I wanted to mention. So if you look at the Tesla Giga factories, or in general, if you look at the imporant places that have this Military-type locations marked in the geo-index, uh, they have one thing in common: is that they are always surrounded by agricultural land, or extensions of land that are considerable like footbal stadiums, and golf courses, things like that.
And, I believe that's a measure to protect their locations from the same thing they are doing to the rest of the world. And I have seen this in many different examples, I call it, I even have a name for it, I call it the "Immune Entities Pattern". That's what I call it.
And what that means is that: when they know that they are attacking, or that they have a method, a systematic method of attacking something, they always seem to make sure they are protecting their properties, or their people, or whatever is that they are attacking on the rest of the world, against that same type of attack.
And I've seen this in many many different instances, even some of them are documented on the legal record. There is a famous, or infamous, I don't know, case, in which Travis Kalanick got sued on 2014, among other people from Uber, he got sued by Lyft, and the CEO and all those people, becasue they are spying on Lyft on the same way that they are spying on us today.
And in the lawsuit, and I have this published on the website already, in the lawsuit it is mentioned that, the allegations well, that Lyft made: they said that Travis Kalanick, he figured out that the Lyft application had a weakness, that is that the Driver's ID, so each driver on the system had an ID, and identifier. They said that the identifier was "stable", meaning that the ID of the driver, say, it's number, say Reinaldo is a driver and the number is 1234, that's my ID.
So, in Lyft, my ID was always 1234, and this was a weakness, becasue when Travis Kalanick and their spies, what he would do: he would send spies to follow the drivers of lyft and once they were close to the dirver, physically close, with an interception device, they would capture the traffic from his phone or her phone, and with that traffic, once they decrypted it, they were able to see the identifier.
And, because they could see the plate on the car, they had a way to tie the car to this identifier that never changed.
So the lawsuit alleges that they identified that weakness, and they made sure that when designed UBER they made it so the identifiers changed everything six hours, or every ten hours, I don't recall the exact number of hours, but they made sure that UBER identifiers changed over time, so to not have the same weakness.
What I am trying to say is that they do have this way of thinking that when they identify that they can damage something, that they can attack something in a certain way, they always make sure to protect their interests so they cannot be attacked in the same way.
And this is a pattern that I have seen many many times, just mentioning one example to explain what I mean, but I have seen this at least 12 to 14 times on very important things.
I am saying this because in these locations that are important to them, there's always a lot of land that is always right next to the location.
And I belive that this is just a way so people cannot eavesdrop. So, if the FBI, the Mossad or whoever is, you know, might be interested in investigating them, they won't be able to eavesdrop as easily, because, well, if a car stands in there, they have the place under surveillance and they will see there is a car in there that is not their car, and they will look into it.
So I think it is a protection measure, but if you look at those places, like the New York, the water and the things that it has around, it has all those features.
And if you look at the Giga factories, they look a lot like, the sourrounding areas look a lot like that area of West Los Angeles where Robert Gates has his house, or had his house, because I know there was a fire there recently, so I don't know if it is still there.
But the area, right, the geographical features of the area, it's, it's, it's something that repeats, it's something that can be detected with technology.
Anyhow, I will stop it for now, but just wanted to, you know, give people an idea of what can be done, and some tips on how to exploit this technology.
Also I want to say, I am going to make, so the data that is powering this dashboard is of course going to be avaialble, for free, it's open, an open dataset that I am going to publish on the website on a downloadable form, and also I am going to publish it, it's already published actually, as a Bigquery Dataset that is open and free and anybody can query it in Bigquery, so you know, if you want to play around with it, you know, go crazy.