WEBVTT

00:00.000 --> 00:02.000
Oh, you guys, too?

00:02.000 --> 00:04.000
Oh, my God.

00:04.000 --> 00:06.000
That is so awesome.

00:10.000 --> 00:12.000
This is never going to happen.

00:12.000 --> 00:14.000
Yeah, right.

00:14.000 --> 00:16.000
Well, this will be worth it.

00:16.000 --> 00:34.000
You know, I have 250 slides, so I thought we would start our time.

00:34.000 --> 00:38.000
So I'm going to have to talk really fast.

00:38.000 --> 00:40.000
And if you blame, you'll miss it.

00:40.000 --> 00:44.000
250 slides, he says.

00:44.000 --> 00:48.000
Wow, that's a lot of slides.

01:00.000 --> 01:05.000
It's not supposed to be routed back into the auditorium, you think.

01:05.000 --> 01:09.000
This doesn't seem like a hard problem, right?

01:09.000 --> 01:11.000
It's MIT.

01:11.000 --> 01:19.000
Does anybody who is skilled in this particular area want to come and help out?

01:24.000 --> 01:26.000
Pretty weak.

01:26.000 --> 01:29.000
Oh, just from here to out.

01:29.000 --> 01:31.000
Yeah, okay.

01:40.000 --> 01:42.000
I'm not really sure what to say.

01:42.000 --> 01:53.000
I thought I would flash it up really quick and see if I could catch it.

01:53.000 --> 01:56.000
I didn't think they would have technical difficulties.

01:56.000 --> 01:58.000
I apologize for this.

01:58.000 --> 02:02.000
I thought I'd take some notes and see what he says and doesn't say.

02:03.000 --> 02:12.000
Right now, I can't tell whether the New Zealand data is going to just be misrepresented or what the actual reason it's coming out for.

02:12.000 --> 02:24.000
But I think it's worth paying attention to so that we can figure out exactly what is meant to happen here.

02:25.000 --> 02:32.000
I don't ever really believe in whistleblowers and data leaks.

02:32.000 --> 02:37.000
I think first and foremost, we have to assume it's a trap of some kind.

02:37.000 --> 02:41.000
And again, we're playing into their solving their mystery, right?

02:41.000 --> 02:46.000
Doing their math for them and then coming to a conclusion that they want us to come to.

02:47.000 --> 02:50.000
And then coming to a conclusion that they want us to come to.

02:50.000 --> 02:59.000
So just like the lab leak, I want to be very careful here and the fact that the misinformation super spreader.

02:59.000 --> 03:05.000
Steve Kirsch is the one who's bringing this to light at MIT with such pomp and circumstance.

03:05.000 --> 03:08.000
Makes me even more suspicious.

03:09.000 --> 03:12.000
No, we didn't get on.

03:12.000 --> 03:15.000
Didn't get on Kim Iverson today.

03:15.000 --> 03:18.000
No, they don't have that sound.

03:18.000 --> 03:23.000
Holy cow, it's really a joke here.

03:39.000 --> 03:47.000
How's my sink, am I all right?

03:47.000 --> 03:51.000
Wow, we really can't hear them.

03:51.000 --> 03:59.000
He's students for open inquiry and we want to get the dissenters and the heterodox speakers over here because MIT has a real lack of that.

03:59.000 --> 04:08.000
And now it is time to restore MIT back to the land of curiosity and to the land where academic and intellectual freedom can flourish.

04:13.000 --> 04:17.000
So many people have been saying that there's a problem with Kirsch.

04:17.000 --> 04:20.000
He's a fraud. He doesn't know what he's talking about.

04:20.000 --> 04:23.000
Let's see if that holds up tonight.

04:23.000 --> 04:26.000
Welcome everybody, I am Adam Dang.

04:26.000 --> 04:28.000
I am the captain of this enterprise.

04:28.000 --> 04:34.000
I am a senior in mathematics and AI.

04:34.000 --> 04:43.000
Thank you, as you can tell, I am not a specialist in AV equipment, so I do apologize that we had a little bit of problems there, but luckily it's all resolved.

04:43.000 --> 04:45.000
This should be an auspicious omen.

04:45.000 --> 04:47.000
Let's see what we can do here tonight.

04:47.000 --> 04:55.000
So I'm very excited to present to you Spencer, my co-pilot.

04:55.000 --> 04:58.000
Thank you everybody for being here. Thank you Steve for being here.

04:58.000 --> 04:59.000
I'm Spencer Sinnerson.

04:59.000 --> 05:02.000
I'm a freshman studying political science and management.

05:02.000 --> 05:04.000
And so...

05:04.000 --> 05:08.000
Political science and then MIT.

05:08.000 --> 05:14.000
So it was what I was found to restore free expression and intellectual curiosity to the MIT campus.

05:14.000 --> 05:15.000
That's the spirit of science.

05:15.000 --> 05:20.000
A lot of scientific theories, like Darwin's evolution theory, was created.

05:20.000 --> 05:29.000
They came to existence because scientists and theorists back in the days had the audacity to interact with heteronautical views.

05:29.000 --> 05:31.000
They went against the status quo.

05:31.000 --> 05:42.000
Darwin went against the theory of using this use that prevailed the world back in the days and a lot of scientific thinkers came out with breakthrough theories and inventions because that.

05:42.000 --> 05:51.000
And that's why we're keen to restore this to MIT campus, the supposed epicenter of science and technology.

05:51.000 --> 06:03.000
You might know about the MIT Free Speech Alliance and other groups like that that have been working to restore some semblance of normality back to campuses as a place where you can inquire about what you want and see if things hold up.

06:04.000 --> 06:11.000
And one of our goals is we note that there is a lot of theory about what free speech can mean.

06:11.000 --> 06:14.000
But there's not as much action on college campuses.

06:14.000 --> 06:21.000
You rarely get the chance to really see these speakers on the would be fringed, discuss their ideas.

06:21.000 --> 06:23.000
And that's what we're here tonight to do.

06:23.000 --> 06:35.000
The main goals of the MSOI is to take the free speech theory and to give all the MIT community a dose of what it means to tackle intellectual heterodoxy and dissent.

06:35.000 --> 06:40.000
So we are the action arm of those who want free speech.

06:40.000 --> 07:00.000
And to that end, we're platforming distance and heterodoxical speakers who have lost their platforms in the mainstream, in academia, in this world of rampant censorship.

07:00.000 --> 07:02.000
That's unacceptable.

07:02.000 --> 07:10.000
And I think that when you look at the political organizations that have sprung up left wing right wing, maybe they have a certain issue as their cause.

07:10.000 --> 07:11.000
That's one thing.

07:11.000 --> 07:12.000
But we're MIT.

07:12.000 --> 07:15.000
We are the greatest college on the planet.

07:15.000 --> 07:17.000
So what do we do differently, right?

07:17.000 --> 07:21.000
I think what we have as our advantage is a sense of curiosity.

07:21.000 --> 07:24.000
You know, some people call us the goofy school.

07:24.000 --> 07:25.000
And I'm goofy.

07:25.000 --> 07:27.000
You know, I've done some things in my past.

07:27.000 --> 07:29.000
And I think that's the spirit we want.

07:29.000 --> 07:35.000
Ultimately, we're not here just to participate in the political divides or the questions of a zero, some game.

07:35.000 --> 07:36.000
I think we're here to do more than that.

07:36.000 --> 07:39.000
We're here to explore what can we achieve?

07:39.000 --> 07:40.000
Where does our potential lie?

07:40.000 --> 07:42.000
What is our purpose here?

07:42.000 --> 07:50.000
So given the speakers that we've had, I think we're doing more than just, you know, I take this position on an issue and why that position is wrong.

07:50.000 --> 07:51.000
We are also getting shot.

07:51.000 --> 07:54.000
I wish I could pause this because I've just shocked at what this guy's saying.

07:54.000 --> 07:56.000
How can we be the drivers of change?

07:56.000 --> 08:02.000
Because it's all good if we have proclaimed leaders say they're going to do something for us, but one that usually never do it.

08:02.000 --> 08:03.000
And two, what about us?

08:03.000 --> 08:06.000
In our own lives, we got to be the change and we have to drive that forward.

08:06.000 --> 08:09.000
And MSOI wants to make that happen.

08:09.000 --> 08:14.000
We want to bring the ideas so that all of you can spark change in your own lives.

08:14.000 --> 08:17.000
Wow, that is bizarre.

08:17.000 --> 08:20.000
It doesn't sound like MIT.

08:21.000 --> 08:29.000
So it becomes clear now that since the start of 2020 to now, the vaccination issue, the lockdown issue and the mandates have not been discussed.

08:29.000 --> 08:32.000
In fact, they have been swept under the rug.

08:32.000 --> 08:38.000
So, MSOI, we believe that this is a huge problem that has not been addressed at all.

08:38.000 --> 08:40.000
People have just ignored it and said, oh, you know what, it's in the past.

08:40.000 --> 08:41.000
Don't worry about it.

08:41.000 --> 08:43.000
But this cannot happen.

08:43.000 --> 08:45.000
It is a travesty to the people.

08:45.000 --> 08:56.000
And thus, we declare as the MSOI that we condemn the censorship and the punishment of individuals who questioned the establishment narrative.

08:59.000 --> 09:02.000
It's kind of late now, like finding your principles.

09:02.000 --> 09:08.000
And this is especially true for the most recent destruction of our liberties, the COVID narrative.

09:08.000 --> 09:09.000
Who is this guy?

09:09.000 --> 09:11.000
No, we weren't allowed to talk about that.

09:11.000 --> 09:14.000
We weren't allowed to question the lockdowns or the vaccines or the master any of that.

09:14.000 --> 09:17.000
But tonight, this all changes.

09:17.000 --> 09:19.000
Wow, I am.

09:23.000 --> 09:27.000
At MSOI, we believe that medical dissidents have the right to speak their minds.

09:27.000 --> 09:28.000
This is a pressure.

09:28.000 --> 09:36.000
They cannot be censored because no matter what smears are thrown at them, anti-science, crank, altered crepitarian, or not knowing what they're talking about.

09:36.000 --> 09:41.000
Signs, like I said, it's all about inquiring about the status quo.

09:41.000 --> 09:45.000
It's about the spirit of open inquiry and creativity.

09:45.000 --> 09:49.000
And so, so-called health authorities are never infallible.

09:49.000 --> 09:57.000
And we have the right to speak our minds, interact with data, interact with all the stuff that's out there and form our own opinions.

09:57.000 --> 10:01.000
And so, people have the right to question the status quo.

10:06.000 --> 10:13.000
And I would also like to say something about the universities here who have been the most censored happy and the most draconian in their restrictions.

10:13.000 --> 10:23.000
We at the MSOI, we denounce these elite institutions, especially in the academic sphere, the universities, and the governments, including MIT.

10:23.000 --> 10:28.000
We got our lunch eaten by other colleges who are more happy to restore freedom.

10:28.000 --> 10:29.000
He'll know I'm looking at you.

10:29.000 --> 10:42.000
Anyways, I think that we need to have a reckoning about these organizations and the universities, which have been more interested in establishing and strengthening a censorship complex around COVID and the other medical topics.

10:42.000 --> 10:47.000
This has been going on for far too long, and we are going to stop it.

10:47.000 --> 11:09.000
And we're calling upon all organizations, government entities, private entities, everybody, who's engaged in sensorious practices, who's engaged in suppressing medical distance, suppressing free speech, to apologize for their malicious actions and intent.

11:09.000 --> 11:12.000
And make no mistake, it's malicious.

11:13.000 --> 11:25.000
We don't know how many of them are actually going to apologize if they're just going to say that this was all a big mistake and put it behind them.

11:25.000 --> 11:31.000
But the one thing that we do know is we have to take power back into our own hands.

11:31.000 --> 11:36.000
If we say no, then we don't need to worry about whether or not they feel guilty for their actions.

11:36.000 --> 11:50.000
If we can take our power back as we are doing tonight, we will be in prime position to control our destinies and be free as we wish.

11:50.000 --> 11:52.000
I know.

11:52.000 --> 11:55.000
I mean, he's an alum of MIT, 1980.

11:55.000 --> 12:01.000
He invented the optical mouse, so any gamers in here?

12:01.000 --> 12:02.000
All right, we have a few.

12:02.000 --> 12:05.000
You need to be thanking this man.

12:06.000 --> 12:08.000
I own a privacy computer called the Libram 14.

12:08.000 --> 12:12.000
The trackpad is a D minus, so I need to use the optical mouse, though.

12:12.000 --> 12:14.000
Steve has been great on that side.

12:14.000 --> 12:21.000
But there was really an organization, as I said, it's a bit tragic, really.

12:21.000 --> 12:28.000
I mean, in February of 2022, Steve wrote to MIT, wrote to the chancellor, the president, and a few other key figures saying,

12:28.000 --> 12:31.000
hey, you know, I donated $2.5 million to the auditorium.

12:31.000 --> 12:36.000
I would like to come back and talk about why the COVID narrative has errors.

12:36.000 --> 12:37.000
Nobody responded to him.

12:37.000 --> 12:38.000
They all ignored him.

12:38.000 --> 12:42.000
And there's one of two ways to get Steve to come to campus.

12:42.000 --> 12:45.000
One of them is through a faculty sponsor, which none of them accepted.

12:45.000 --> 12:49.000
The other one is to have a student group that will invite him in.

12:49.000 --> 13:07.000
So we, as the MSOI, are very proud tonight to invite Steve Kirsch.

13:07.000 --> 13:15.000
What is this?

13:16.000 --> 13:19.000
Wow.

13:19.000 --> 13:26.000
Okay, this works.

13:26.000 --> 13:34.000
It doesn't work when it's not in the proximity of your mouth.

13:34.000 --> 13:36.000
Okay.

13:36.000 --> 13:37.000
Is it safe?

13:37.000 --> 13:40.000
What the record level data says?

13:40.000 --> 13:42.000
Here we go, ladies and gentlemen.

13:42.000 --> 13:44.000
Let's skip that.

13:44.000 --> 13:50.000
I'm an MIT grad, as you know, class of 78, two degrees from MIT.

13:50.000 --> 13:54.000
Former our serial entrepreneur started a couple of billion dollar companies, which feature on 60 minutes,

13:54.000 --> 13:58.000
written about 1500 articles on my sub-stack about vaccine safety.

13:58.000 --> 14:03.000
And today I'm the world's number one misinformation spreader according to Google.

14:03.000 --> 14:11.000
If you type in, if you type in misinformation super spreader into Google, I am the top hit.

14:11.000 --> 14:13.000
So it is nice to be the best in the world.

14:13.000 --> 14:15.000
It's something.

14:15.000 --> 14:20.000
This wasn't, I have to admit, this wasn't what I planned on being the best in the world at.

14:20.000 --> 14:25.000
But hey, you know, it's good to be the best in the world at something.

14:25.000 --> 14:30.000
Now, the benefits for being a misinformation spreader, which means you are calling for data transparency

14:30.000 --> 14:35.000
and you're just looking for the truth, is that you have lifetime bans on LinkedIn, medium Twitter,

14:35.000 --> 14:40.000
two lifetime bans, Wikipedia, stripped of national carrying award on my Wikipedia page.

14:40.000 --> 14:45.000
Friends abandon you, country club won't take you, corporate boards tell you to shut up or leave.

14:45.000 --> 14:50.000
Your scientific advisory boards quits en masse and so on and so forth.

14:50.000 --> 14:56.000
And the final thing is that MIT will not let you speak in your own auditorium

14:56.000 --> 15:01.000
because they couldn't find a faculty sponsor.

15:01.000 --> 15:04.000
You know, I remember that I was class of 78.

15:04.000 --> 15:08.000
Do you remember this movie, National Lampard's Animal House?

15:08.000 --> 15:12.000
And who can forget the scene in the stables?

15:12.000 --> 15:18.000
It says we have an old saying in Delta, don't get mad, get even.

15:18.000 --> 15:21.000
So this is my night.

15:23.000 --> 15:28.000
This is the alternate title for today's talk, which is Revenge of the Nerd.

15:28.000 --> 15:30.000
That nerd is me.

15:31.000 --> 15:36.000
And the following presentation has been approved for no audiences by the CDC and the FDA.

15:36.000 --> 15:40.000
It is rated M for misinformation.

15:40.000 --> 15:45.000
And most of the material may be considered extremely unsafe.

15:45.000 --> 15:47.000
Okay, I just wanted to make sure.

15:47.000 --> 15:49.000
So if you're...

15:49.000 --> 15:50.000
That's well done.

15:50.000 --> 15:51.000
That's a nice slide.

15:51.000 --> 15:52.000
Right.

15:52.000 --> 15:53.000
Okay.

15:53.000 --> 15:54.000
I like that.

15:54.000 --> 15:55.000
So that's now...

15:55.000 --> 15:58.000
We actually have some MIT undergrads in the audience.

15:58.000 --> 16:04.000
So I'm going to teach a short class here, 6.123, which is Vaccine Safety 101.

16:04.000 --> 16:06.000
This is my first lecture.

16:06.000 --> 16:07.000
It's called the Basics.

16:07.000 --> 16:11.000
There are seven slides and it's a 12-unit course.

16:11.000 --> 16:12.000
Okay.

16:12.000 --> 16:14.000
And I am Professor Kirsch.

16:14.000 --> 16:15.000
Just testing it out.

16:15.000 --> 16:16.000
Thank you very much.

16:16.000 --> 16:17.000
This is the first slide.

16:17.000 --> 16:20.000
Now, I'm not going to go through these slides in detail.

16:20.000 --> 16:27.000
But the main thing here is that if you plot the deaths per day since injection of a vaccine,

16:27.000 --> 16:29.000
and you look at the...

16:29.000 --> 16:32.000
And the x-axis is...

16:32.000 --> 16:36.000
That's the horizontal axis is the days after injection.

16:36.000 --> 16:45.000
What should happen in a safe vaccine that is given to a finite cohort and then you look to see what happens

16:45.000 --> 16:49.000
is that the line should slope downwards over time.

16:49.000 --> 16:56.000
And the more elderly the population is, the greater the downward slope.

16:56.000 --> 17:04.000
If it's a normal population, it'll slope at 0.8% per year is the downward slope.

17:04.000 --> 17:05.000
Okay.

17:05.000 --> 17:06.000
But it always slopes downwards.

17:06.000 --> 17:11.000
And sometimes it can go up and down if there's something happening in the background and you're not

17:11.000 --> 17:15.000
giving the vaccine over all time evenly.

17:15.000 --> 17:16.000
Okay.

17:16.000 --> 17:17.000
That's...

17:17.000 --> 17:19.000
So, that's a safe vaccine.

17:19.000 --> 17:20.000
Line slopes down.

17:20.000 --> 17:22.000
It never slopes up.

17:22.000 --> 17:24.000
It always slopes down.

17:24.000 --> 17:26.000
This is the most important slide.

17:26.000 --> 17:29.000
If you get this slide, we're done.

17:29.000 --> 17:31.000
Okay.

17:31.000 --> 17:33.000
This is the laws of physics.

17:33.000 --> 17:36.000
If I drop this, it's going to fall down.

17:36.000 --> 17:38.000
It never falls up.

17:38.000 --> 17:41.000
I can drop this many, many times.

17:41.000 --> 17:42.000
It will never go up.

17:42.000 --> 17:44.000
It will always go down.

17:44.000 --> 17:46.000
It's the same thing for people dying.

17:46.000 --> 17:47.000
People die.

17:47.000 --> 17:49.000
They don't suddenly spring from the dead.

17:49.000 --> 17:50.000
Okay.

17:50.000 --> 17:52.000
That's just the way it works.

17:52.000 --> 17:53.000
This is physics.

17:53.000 --> 17:54.000
We're at MIT.

17:54.000 --> 17:56.000
Here's the cheat sheet.

17:56.000 --> 17:57.000
This is the simple thing.

17:57.000 --> 17:59.000
The line should always go down.

17:59.000 --> 18:00.000
Get it?

18:00.000 --> 18:01.000
Okay.

18:01.000 --> 18:02.000
Good.

18:02.000 --> 18:03.000
If...

18:03.000 --> 18:05.000
By the way, these slides are posted on my sub-stack.

18:05.000 --> 18:09.000
If you don't understand any of these slides, you can go back and study them.

18:09.000 --> 18:10.000
Okay.

18:10.000 --> 18:14.000
A safe and effective vaccine before and after.

18:14.000 --> 18:21.000
If it really works and is effective, the line is slightly lower, but it still slopes down.

18:21.000 --> 18:28.000
And the fluctuations for a particular virus will be smaller if the vaccine worked.

18:28.000 --> 18:32.000
This is the sign of a vaccine, which is effective.

18:32.000 --> 18:36.000
Now, this is going to be news to people who work at the CDC.

18:36.000 --> 18:38.000
I'm working on slide two.

18:38.000 --> 18:46.000
Nobody who works at the CDC understands this slide.

18:46.000 --> 18:48.000
Can you believe that?

18:48.000 --> 18:49.000
Yeah.

18:49.000 --> 18:50.000
I can believe it.

18:50.000 --> 18:51.000
Okay.

18:51.000 --> 18:54.000
Now, there's something called the temporal healthy vaccine effect.

18:54.000 --> 18:56.000
And it's gone after 21 days.

18:56.000 --> 18:59.000
We've never seen it happen after 21 days.

18:59.000 --> 19:03.000
I've asked these people who say, oh, healthy vaccine effect cannot last for years.

19:03.000 --> 19:05.000
And I say, show me evidence of that.

19:05.000 --> 19:06.000
They never do.

19:06.000 --> 19:10.000
I've never seen the healthy vaccine effect happen for more than 21 days.

19:10.000 --> 19:16.000
And what this is, is that they don't give the vaccine to people who are going to die tomorrow.

19:16.000 --> 19:21.000
Or people who are going to die today after tomorrow because they're going to die.

19:21.000 --> 19:24.000
And a lot of people, they know that they're going to die in the next 10 days.

19:24.000 --> 19:27.000
So they don't bother giving a vaccine because why would you vaccinate them?

19:27.000 --> 19:35.000
In this case, people would vaccinate people who were going to die in two days because they're afraid of catching COVID.

19:35.000 --> 19:37.000
From someone who's going to die in two days.

19:37.000 --> 19:41.000
So the healthy vaccine effect for COVID is actually smaller.

19:41.000 --> 19:45.000
It's more compressed than the normal effect, but there is an effect.

19:45.000 --> 19:46.000
Okay.

19:46.000 --> 19:48.000
And you'll see that in the curves.

19:48.000 --> 19:53.000
And the other thing you'll see in these curves is this red line here, which is running.

19:53.000 --> 19:55.000
You run out of time to die.

19:55.000 --> 20:00.000
Now, that doesn't mean that everybody's rushing to die before the time goes out.

20:01.000 --> 20:05.000
It's because the studies always end in a certain time period.

20:05.000 --> 20:09.000
You have your observation time window and then you give the dose over, say, the first year.

20:09.000 --> 20:12.000
And then you look at the second year to see what happened.

20:12.000 --> 20:19.000
So after 365 days, it starts going down because people simply can't live that long because the study was compressed.

20:19.000 --> 20:20.000
Okay.

20:20.000 --> 20:26.000
And the other thing you'll see in this is something called 50% of the people leaving for dose three.

20:26.000 --> 20:32.000
So people who got shot two, a lot of these people were duped into taking shot number three.

20:32.000 --> 20:34.000
Half the people, right?

20:34.000 --> 20:40.000
So that's the, you know, it's like half the people have IQs under 100 and half.

20:40.000 --> 20:41.000
Okay.

20:41.000 --> 20:48.000
So anyway, so half the people, it turns out, went for dose three.

20:48.000 --> 20:53.000
And so you can see that, so the line, if you looked at the desk per day,

20:53.000 --> 20:56.000
it's going to start dropping as people go and leave the cohort.

20:56.000 --> 21:02.000
And so if you're just plotting desk per day, it's going to go down by 50% if half the people leave.

21:02.000 --> 21:03.000
Okay.

21:03.000 --> 21:07.000
And then I'm going to slightly confuse you and talk about mortality rate.

21:07.000 --> 21:10.000
Mortality rate is the desperate 100K person years.

21:10.000 --> 21:13.000
So it's not per day, it's person years.

21:13.000 --> 21:17.000
And so that curve tends to remain flat.

21:17.000 --> 21:21.000
You have that healthy vaccine effect, but then you have a flat curve.

21:21.000 --> 21:27.000
And then it starts getting noisy at the end because you're running out of man days and deaths.

21:27.000 --> 21:29.000
And it just becomes erratic.

21:29.000 --> 21:30.000
Okay.

21:30.000 --> 21:31.000
So any questions?

21:31.000 --> 21:32.000
Okay.

21:32.000 --> 21:33.000
Great.

21:33.000 --> 21:34.000
So we have no questions.

21:34.000 --> 21:35.000
So I'm going to just move on.

21:35.000 --> 21:40.000
We don't have time for questions, but we'll have time for questions after the talk.

21:40.000 --> 21:44.000
So MIT professor Robert Langer did not want to see the data.

21:44.000 --> 21:45.000
Oh, sorry.

21:45.000 --> 21:47.000
MIT Institute professors.

21:47.000 --> 21:50.000
This is, they're only like 12 Institute professors.

21:50.000 --> 21:51.000
There are guys at the top of his class.

21:51.000 --> 21:55.000
He has an H index of like 317, which is completely off the charts.

21:55.000 --> 21:58.000
I mean, this guy is academically the smartest guy in the world.

21:58.000 --> 22:00.000
Or, you know, one of the smartest guys in the world.

22:00.000 --> 22:05.000
His papers have been more, you know, read by more people than virtually anyone.

22:05.000 --> 22:11.000
I asked him, I said, do you want to see the data that I'm going to show tonight?

22:11.000 --> 22:13.000
That I'm going to show all of you tonight.

22:13.000 --> 22:19.000
And so I wrote him, and I left my phone number, and I wrote him about it maybe half a dozen

22:19.000 --> 22:25.000
times, and I left phone calls, and the students visited his lab to make sure he knew about

22:25.000 --> 22:26.000
it.

22:26.000 --> 22:28.000
He did not respond.

22:28.000 --> 22:33.000
Now, do you know why he didn't respond?

22:33.000 --> 22:34.000
He had COVID.

22:34.000 --> 22:35.000
No.

22:35.000 --> 22:36.000
No.

22:36.000 --> 22:41.000
If he sees the data, he has a duty as a board member of Moderna to immediately stop to shots.

22:41.000 --> 22:48.000
That's why these people, that's why these people will not, cannot, under any circumstance,

22:48.000 --> 22:52.000
ever see what I'm about to show you today.

22:52.000 --> 22:56.000
If you were to walk up to them and show them the data, they would like run the other way

22:56.000 --> 22:58.000
or call the cops on you.

22:58.000 --> 22:59.000
Okay.

22:59.000 --> 23:04.000
They are not allowed to see this data because if they did, they'd have a fiduciary responsibility

23:04.000 --> 23:06.000
to stop the shots.

23:06.000 --> 23:11.000
Well, it's actually criminal liability is the more, is the more important thing.

23:11.000 --> 23:15.000
He could be held criminally liable if they don't stop the shots once he knows that they're killing

23:15.000 --> 23:16.000
people.

23:16.000 --> 23:23.000
So, this is why none of these people ever want to see the data that I'm showing you.

23:23.000 --> 23:25.000
This will never be shown at the CDC or FDA.

23:25.000 --> 23:27.000
This will never appear in the New York Times.

23:27.000 --> 23:29.000
This will never appear in any media.

23:29.000 --> 23:33.000
No media will cover this because otherwise they're going to have to stop the shots.

23:33.000 --> 23:38.000
So, it's just you people in the room and a couple of people watching on the live stream.

23:38.000 --> 23:45.560
Now, remember the prep app only protects you from civil liability, not criminal liability.

23:45.560 --> 23:51.560
This is why they can't afford to see any of this data that I'm showing you tonight.

23:51.560 --> 23:56.560
Now, they basically have to act if they know that there are kill shots.

23:56.560 --> 24:04.560
So, Moderna and Pfizer were asked by a prominent medical journalist if they wanted to see the

24:04.560 --> 24:09.560
data and they said, no, we don't want to see any of the data.

24:09.560 --> 24:10.560
That's the way it works.

24:10.560 --> 24:14.560
Now, does anybody want to take over loading a phone mic?

24:14.560 --> 24:16.560
Because I haven't seen tonight, I asked this earlier.

24:16.560 --> 24:18.560
According to my meters, I haven't said pretty well.

24:18.560 --> 24:19.560
Anybody?

24:19.560 --> 24:21.560
I'm not peeking out on my hand.

24:21.560 --> 24:22.560
I think it's him.

24:22.560 --> 24:23.560
What?

24:23.560 --> 24:24.560
We have a guy?

24:24.560 --> 24:27.560
Are you serious?

24:27.560 --> 24:30.560
You would.

24:30.560 --> 24:31.560
Oh, you would.

24:31.560 --> 24:32.560
Okay.

24:32.560 --> 24:33.560
So, okay.

24:33.560 --> 24:36.560
Well, I'm not great.

24:36.560 --> 24:37.560
Okay.

24:37.560 --> 24:40.560
So, I'm going to have you come up here after.

24:40.560 --> 24:41.560
I'm going to do my presentation.

24:41.560 --> 24:42.560
Oh, it's a challenge.

24:42.560 --> 24:43.560
Explain it to us.

24:43.560 --> 24:44.560
Sweet.

24:44.560 --> 24:45.560
Okay.

24:45.560 --> 24:46.560
Oh, boy.

24:46.560 --> 24:47.560
Okay.

24:47.560 --> 24:48.560
Okay.

24:48.560 --> 24:49.560
All right.

24:49.560 --> 24:53.560
So, I'm going to show you some data from Medicare.

24:53.560 --> 24:55.560
Medicare is the gold standard data.

24:55.560 --> 25:03.560
And I'm going to show you this data that has never before been made public.

25:03.560 --> 25:07.560
And it's the same time period for all these graphs.

25:07.560 --> 25:12.560
We're looking at the same time period, the same population, the only thing that's different,

25:12.560 --> 25:14.560
is the vaccine.

25:14.560 --> 25:15.560
I wonder where this is.

25:15.560 --> 25:16.560
Florida Medicare.

25:16.560 --> 25:17.560
Florida Medicaid.

25:17.560 --> 25:18.560
What is this?

25:18.560 --> 25:19.560
Okay.

25:19.560 --> 25:20.560
Where did he get this from?

25:20.560 --> 25:22.560
This is a pneumococcal vaccine.

25:22.560 --> 25:26.560
This is your typical unsafe vaccine.

25:26.560 --> 25:29.560
I have never met a vaccine that was safe.

25:29.560 --> 25:31.560
I'm still looking.

25:31.560 --> 25:34.560
So, a pneumococcal, not a safe vaccine.

25:34.560 --> 25:39.560
And the reason is that little dot up there.

25:40.560 --> 25:47.080
Over my head, where on day zero, it kills a ton of people.

25:47.080 --> 25:50.840
This may look like a small number, but this is basically a ton of people.

25:50.840 --> 25:53.080
This means it is a very unsafe vaccine.

25:53.080 --> 26:02.240
A safe vaccine is only supposed to kill somewhere on the order of one person per million shots.

26:02.240 --> 26:06.480
This thing you can clearly see on day zero is killing a ton of people.

26:06.480 --> 26:11.800
And in fact, the flu shots do the same thing.

26:11.800 --> 26:18.160
And you know, so when we talk about, oh, a lot of seasonal death in the elderly, I wonder

26:18.160 --> 26:25.240
if it's the flu shots causing the seasonal death in the elderly.

26:25.240 --> 26:27.240
Nobody ever looks at that.

26:27.240 --> 26:28.800
And I did a calculation.

26:28.800 --> 26:35.600
And there are actually more people who die from the flu shot itself than who die from

26:35.600 --> 26:36.600
the flu.

26:36.600 --> 26:41.800
The vaccine is killing more people than the disease.

26:41.800 --> 26:45.480
So anyway, this is pneumococcal.

26:45.480 --> 26:48.720
It falls 11% over 365 days.

26:48.720 --> 26:49.720
You can see it's a straight line.

26:49.720 --> 26:53.480
It's just like I taught you in the earlier lecture.

26:53.480 --> 26:56.560
See what I teach you is actually happens in practice.

26:56.560 --> 26:59.760
It's even a Poisson distribution.

26:59.760 --> 27:04.080
6041, right?

27:04.080 --> 27:07.240
So these dots have effectively this noise.

27:07.240 --> 27:10.760
So if you look at the standard deviation and calculate standard deviation, you'll find

27:10.760 --> 27:15.120
that it's the same as a Poisson distribution because the Poisson distribution, the variance

27:15.120 --> 27:16.120
is equal to the mean.

27:16.120 --> 27:17.560
You all knew that, right?

27:17.560 --> 27:18.560
Okay.

27:18.560 --> 27:19.560
Good.

27:19.560 --> 27:20.560
How many people knew that?

27:20.560 --> 27:21.560
Okay.

27:21.560 --> 27:22.560
Good.

27:22.560 --> 27:23.560
Good.

27:23.560 --> 27:24.560
Good.

27:24.560 --> 27:25.560
Awesome.

27:25.560 --> 27:26.560
Okay.

27:26.560 --> 27:27.640
So now this vaccine is a very unsafe vaccine.

27:27.640 --> 27:32.720
The slope goes up monotonically for 365 days straight.

27:32.720 --> 27:33.960
This is unprecedented.

27:33.960 --> 27:40.440
I have a leaker in HHS who has never seen anything like this before in her life.

27:40.440 --> 27:44.080
And she does this every single day.

27:44.080 --> 27:49.200
This is like if I were to drop this, it would go up.

27:49.200 --> 27:51.400
It's not supposed to happen.

27:51.400 --> 27:56.000
What this means is that it means the vaccines are killing people.

27:56.000 --> 27:57.840
They're not helping people.

27:57.840 --> 27:59.720
They are killing people.

27:59.720 --> 28:01.680
All you need is this one chart.

28:01.680 --> 28:03.640
You don't need a control group.

28:03.640 --> 28:05.880
You don't need to adjust for confounders.

28:05.880 --> 28:08.480
You don't need any of that stuff.

28:08.480 --> 28:13.560
This one chart is the death shot for this vaccine.

28:13.560 --> 28:19.480
And it has all been in plain sight in Medicare since the beginning of the vaccination program.

28:19.480 --> 28:25.680
It's just that nobody wants to look at the Medicare data and the CDC hides it from site.

28:25.680 --> 28:30.880
Going up 26% instead of going down by a 5% percent or more for the Medicare audience.

28:30.880 --> 28:31.880
Well, that's the COVID vaccine.

28:31.880 --> 28:38.280
There's a net difference of a 31% over one year period.

28:38.280 --> 28:41.640
That is astronomical.

28:41.640 --> 28:47.480
This is a killing machine that should be stopped immediately.

28:47.480 --> 28:53.960
And they've had the data the whole time.

28:53.960 --> 28:55.880
This is Medicare.

28:55.880 --> 29:02.680
This is the population that was supposed to be protected by the shots.

29:02.680 --> 29:06.840
And instead, the shots are killing people.

29:06.840 --> 29:10.560
There is no doubt about this.

29:10.560 --> 29:12.320
It didn't save anyone.

29:12.320 --> 29:14.160
It killed people.

29:14.160 --> 29:15.160
No doubt about it.

29:15.160 --> 29:16.800
None whatsoever.

29:16.800 --> 29:21.480
It should be labeled generally regarded as unsafe.

29:21.480 --> 29:23.280
G-R-A-U.

29:23.280 --> 29:28.320
That acronym doesn't even exist for vaccines.

29:28.320 --> 29:31.880
Because they're always safe, even though they're not.

29:31.880 --> 29:36.840
But these things should be, the COVID vaccines especially, but the other vaccines as well,

29:36.840 --> 29:42.640
should be grow generally regarded as unsafe.

29:42.640 --> 29:46.840
And Harvey Rich said, well, I'd like to see the other shots as well.

29:46.840 --> 29:51.840
And so Harvey, Harvey Rich is a famous epidemiologist, he's one of the top epidemiologists in the

29:51.840 --> 29:54.280
United States as well as the world.

29:54.280 --> 29:55.280
And so I showed him the choice.

29:55.280 --> 29:57.200
He says, OK, I'm convinced.

29:57.200 --> 30:00.360
So this just shows, this was not a fluke.

30:00.360 --> 30:05.040
This happens on dose one, dose two and dose three, and I don't have a data for dose four.

30:05.040 --> 30:12.280
It goes up, W-T-E-F, what the crap.

30:12.280 --> 30:13.280
It goes up.

30:13.280 --> 30:15.480
It's supposed to slope down.

30:15.480 --> 30:16.480
Goes up.

30:16.480 --> 30:18.040
And I wrote this.

30:18.040 --> 30:22.760
I found this out back February 25th, 2023.

30:22.760 --> 30:23.760
I wrote an article.

30:23.760 --> 30:24.760
I said, game over.

30:24.760 --> 30:27.800
Medicare data shows the COVID vaccines increase your risk of dying.

30:27.800 --> 30:31.200
I mean, this is like not new news.

30:31.200 --> 30:32.200
February.

30:32.200 --> 30:33.200
What are we in now?

30:33.200 --> 30:35.760
I mean, it's almost like a year later.

30:35.760 --> 30:37.680
These people don't read my sub-stack.

30:37.680 --> 30:42.480
You know, my sub-stack is, if you try to click on my sub-stack, if you work at the

30:42.480 --> 30:46.440
CDC and you try to click over on my sub-stack, it says this web page is regarded.

30:46.440 --> 30:47.440
It is unsafe.

30:47.440 --> 30:50.720
I mean, I'm not kidding.

30:50.720 --> 30:57.320
So do you know why they never spotted any of these safety signals?

30:57.320 --> 31:02.320
Because, yeah, I mean, well, you know, they're looking for something that kills you immediately,

31:02.320 --> 31:03.320
right?

31:03.320 --> 31:04.320
Because most of these vaccines, they kill you immediately.

31:04.320 --> 31:06.440
You know, like these pneumococcal, right?

31:06.440 --> 31:07.600
It kills you immediately.

31:07.600 --> 31:08.600
And then it's slope goes down.

31:08.600 --> 31:11.800
So they're looking for, did it kill you in the first week or two weeks?

31:11.800 --> 31:12.800
No.

31:12.800 --> 31:13.800
Must be safe.

31:13.800 --> 31:16.120
They're looking for that.

31:16.120 --> 31:20.800
Joe Latipo in Florida did this self-control case study, case series.

31:20.800 --> 31:25.200
He's looking comparing the deaths, like in the first, like, I don't know, was it 30

31:25.200 --> 31:28.040
days or whatever versus the next 30 days.

31:28.040 --> 31:32.400
And he's saying, oh, you know, if it really works, it's going to be lower in the first

31:32.400 --> 31:33.400
30 days.

31:33.400 --> 31:34.400
And he found it was.

31:34.400 --> 31:36.280
And so he said, wow, this thing is safe.

31:36.280 --> 31:40.560
No, Joe, slope goes the wrong way.

31:40.560 --> 31:42.080
That's the way the slope goes.

31:42.080 --> 31:48.960
It's just killing people in a way that we had never seen before, unprecedented way.

31:48.960 --> 31:51.680
That's why they were looking for this.

31:51.680 --> 31:56.240
And that's why they never found this, okay?

31:56.240 --> 32:00.840
And I'm a guilty of this because I did something called a ratio analysis and I was confused

32:00.840 --> 32:06.360
because, man, these people, I looked at how they were dying.

32:06.360 --> 32:11.960
And I said, well, if it's unsafe vaccine, then if I look at the time to die for anyone,

32:11.960 --> 32:17.200
if they die at a certain day, I want to find out, like, where in the dates that they

32:17.200 --> 32:19.800
could die did they fall?

32:19.800 --> 32:24.520
There were more than half of them dying in the first half versus the second half of the

32:24.520 --> 32:26.960
time period left to die.

32:26.960 --> 32:31.160
Little hard to explain here, but basically it was weird.

32:31.160 --> 32:34.520
I was seeing that, wow, these vaccines look safe.

32:34.520 --> 32:40.720
And so even I was fooled because I didn't realize that the curve was weighted such that you

32:40.720 --> 32:46.280
were dying six months to a year after you got the shot, and that's how it killed people.

32:46.280 --> 32:48.080
So even I was fooled.

32:48.080 --> 32:54.480
Now this is the most damaging Medicare curve I have seen so far.

32:54.480 --> 33:00.160
Now these are people who got exactly two shots, not three shots, not four shots.

33:00.160 --> 33:03.120
They got exactly two shots.

33:03.120 --> 33:13.280
And so the reason why this is so deadly is because what happens is, remember I taught

33:13.280 --> 33:18.960
you earlier in our course, I said, you know, if you abandon, if 50% abandon for the next

33:18.960 --> 33:22.760
dose, the thing's going to go down and it's going to drop to 50%.

33:22.760 --> 33:29.880
So you can see this baseline level here, baseline two, that is at 200 deaths per day.

33:29.880 --> 33:35.680
So that means because 50% left, that means the baseline, original baseline is 400, okay?

33:35.680 --> 33:40.000
You just double 200, because you have twice so many people.

33:40.000 --> 33:46.280
And look, all those things, they're above the baseline from like, you know, it starts

33:46.280 --> 33:50.320
above the baseline and keeps going higher and higher and higher.

33:50.320 --> 33:54.400
There's no doubt, once again, confirming it.

33:54.400 --> 33:57.600
And of course, my favorite is the anecdotes.

33:57.760 --> 34:02.520
You know, the scientists always say, oh, anecdotes, the plural of, of, anecdote is

34:02.520 --> 34:07.080
anecdotes and it's not data, bullshit, okay?

34:07.080 --> 34:11.240
In a world where information is being withheld or distorted, anecdotes are often the best

34:11.240 --> 34:12.880
source of truth.

34:12.880 --> 34:14.520
Let me give you an example.

34:14.520 --> 34:21.000
Jay Bonner, 57 year old high tech executive, 15 friends died unexpectedly post facts.

34:21.000 --> 34:23.760
All the dead were vaxed.

34:23.840 --> 34:30.560
Prior to the vaxed rollout, Jay had one unexpected death in over 30 years.

34:30.560 --> 34:36.120
And four of the 15 people who died died within 24 hours of a vaccine and three of the four

34:36.120 --> 34:38.560
were under 30.

34:38.560 --> 34:44.320
Within 24 hours of the vaccine, like, hello?

34:44.320 --> 34:51.160
Now let's do your, your Poisson survival function on essentially, it's 14 because it's

34:51.160 --> 34:54.120
of the, the way the Poisson survival function works.

34:54.120 --> 34:55.880
So it's 14 comma like 0.1.

34:55.880 --> 35:03.800
So expecting 0.1 in a period and then you got 14, which is 7 times 10 to the minus 28,

35:03.800 --> 35:10.200
which means this is not something that we didn't get, just get unlucky here, okay?

35:10.200 --> 35:13.960
There's something going on, the vaccine killed his friends, guaranteed.

35:13.960 --> 35:15.920
That's the only way this can happen.

35:15.920 --> 35:20.680
Now the other thing is that 15 of his friends died from the vaccine and zero his friends died

35:20.680 --> 35:24.920
from COVID, but COVID is supposed to kill way more than the vaccine.

35:24.920 --> 35:28.760
I mean, the vaccine is supposed to be around here compared to the, you know, COVID.

35:28.760 --> 35:31.920
Well, he's got the, he's got the reverse.

35:31.920 --> 35:37.600
Well, okay, but so you can do this sort of, the Poisson, uh, cumulative distribution function

35:37.600 --> 35:39.920
and essentially look at it the other way.

35:39.920 --> 35:45.240
Essentially, you were expecting at least 15 COVID deaths that he should have gotten

35:45.240 --> 35:49.920
to match and he got zero or the chances of that, he had pretty small.

35:49.920 --> 35:56.320
So essentially what this tells us is, mathematically, from just this one anecdote,

35:57.920 --> 36:04.000
there's no way that it, that it couldn't be anything less than the cure was far worse

36:04.000 --> 36:06.680
than the disease, okay?

36:06.680 --> 36:11.040
This vaccine was far worse than COVID.

36:11.040 --> 36:14.760
It created deaths, it didn't save anyone.

36:14.760 --> 36:18.800
And you know, the thing is that I always trust the math because the math never lies.

36:18.800 --> 36:22.320
Poisson distributions never lie.

36:22.320 --> 36:24.040
It's not like science.

36:24.040 --> 36:27.200
Science is, you know, people can say, well, you know, I think this and the bad.

36:27.200 --> 36:29.200
We got the theory and the changes.

36:29.200 --> 36:33.320
Poisson distributions never change.

36:33.320 --> 36:36.040
You know, you can't, can't beat that.

36:36.040 --> 36:40.120
Okay, now there's this famous quote from Eric Rubin, New England.

36:40.120 --> 36:42.960
He's the editor-in-chief, New England Journal of Medicine.

36:42.960 --> 36:44.880
He's on the FDA committee.

36:44.960 --> 36:49.720
He's an adjunct professor of immunology and infectious disease at Harvard University.

36:49.720 --> 36:51.480
This guy's an expert, right?

36:51.480 --> 36:53.000
Can't get any better than this.

36:53.000 --> 36:57.280
We're never going to learn about how safe the vaccines are until we start giving it.

36:57.280 --> 37:04.880
You know, he said this, this is not, I'm just like, not out of context,

37:04.880 --> 37:07.040
this is legit stuff.

37:07.040 --> 37:11.920
And he's right because the clinical trials are too small to find these vaccine effects

37:11.920 --> 37:16.880
that happen in smaller numbers than would be detected in these, these trials.

37:16.880 --> 37:18.920
So we now have the safety data, right?

37:18.920 --> 37:23.800
And one small problem, of course, is that normally in public health has ever taken,

37:23.800 --> 37:26.520
ever looked at it very seriously, right?

37:26.520 --> 37:30.440
They've never released the record level data for any vaccine.

37:30.440 --> 37:33.000
No country has ever done that in the entire world.

37:33.000 --> 37:34.200
No state has ever done that.

37:34.200 --> 37:37.840
Nobody has released the record level data.

37:37.840 --> 37:41.640
So we could look at it, we couldn't look at it either because it's not there.

37:41.640 --> 37:43.760
We can't, they're not looking at it.

37:43.760 --> 37:45.400
They could like it, but they're not.

37:45.400 --> 37:47.520
And they're not releasing it to us.

37:47.520 --> 37:49.520
So we can't look at it either.

37:49.520 --> 37:50.560
And that's me.

37:50.560 --> 37:53.160
Oh, well, I was an unhappy camper until I got this data.

37:53.160 --> 37:59.160
And then I was like, singing ding dong, the witch is dead after I got the data.

37:59.160 --> 38:05.080
So nobody can ever figure out the ground truth about any vaccine or any pharmaceutical

38:05.080 --> 38:08.040
because they're always hiding the data.

38:08.040 --> 38:13.280
And I'm the guy, you know, calling for data transparency, but nobody's listening to me.

38:13.280 --> 38:19.600
So all that changed for me on November 9th, 2023.

38:19.600 --> 38:25.720
I have a Wasabi server and somebody uploaded the data to my Wasabi server in the cloud.

38:25.720 --> 38:27.840
And it was a whistleblower in New Zealand.

38:27.840 --> 38:30.800
He's, he works for the Ministry of Health.

38:30.800 --> 38:38.920
And I know his name.

38:38.920 --> 38:41.120
I'm one of the very few people in the world.

38:41.120 --> 38:44.360
He has revealed his identity too.

38:44.360 --> 38:46.680
He uploaded the data.

38:46.680 --> 38:49.080
I know that, I know he's legit.

38:49.080 --> 38:51.920
I know he works at the Ministry of Health.

38:51.920 --> 38:57.680
And I know that data is legit because I examined the individual data records themselves

38:57.680 --> 39:04.040
to verify that the data is legit and I ran a whole series of statistical tests on the

39:04.040 --> 39:09.240
data and the data is legit.

39:09.240 --> 39:19.600
This was also released just a day earlier by the people involved in this.

39:19.600 --> 39:26.000
And they called it Operation M-O-A-R, the mother of all revelations.

39:26.000 --> 39:36.280
Because this record level data has never been released by anyone ever before.

39:36.280 --> 39:44.040
This is the first time in world history where we can actually see the record level data

39:44.040 --> 39:47.200
released from a country.

39:47.200 --> 39:50.160
That is stunning.

39:50.160 --> 39:52.120
And now we know why.

39:52.120 --> 39:53.800
They don't release it.

39:53.800 --> 39:56.760
Now, would you like to see that video?

39:56.760 --> 40:00.440
It's an hour video.

40:00.440 --> 40:09.520
But, and it was posted to YouTube, but in less than five minutes, less than five minutes

40:09.520 --> 40:18.200
YouTube took down the video because it violated community standards.

40:18.200 --> 40:24.280
The community standards say you shall not speak about the truth, about the vaccine.

40:24.280 --> 40:25.280
You have to lie.

40:25.280 --> 40:28.160
You have to tell people it's safe and effective.

40:28.160 --> 40:33.040
If you don't do that, you're violating community standards and they remove your video from

40:33.040 --> 40:35.040
YouTube even though you're trying to save lives.

40:35.040 --> 40:36.040
I think we just have to watch.

40:36.040 --> 40:38.640
Because YouTube is not interested in saving lives.

40:38.640 --> 40:42.520
They are interested in promoting a false narrative that is killing people.

40:42.520 --> 40:44.600
It's definitely Geraldo or something like that.

40:44.680 --> 40:50.880
Anyone who works for YouTube should be ashamed of themselves and they should quit their job.

40:56.320 --> 40:57.320
It's 2024.

40:57.320 --> 41:02.200
And if you want to know what I really like big deal, talk to me afterwards.

41:02.200 --> 41:05.040
You can watch it on rumble.

41:05.040 --> 41:06.040
Okay.

41:06.040 --> 41:09.240
And these slides, all of them have hyperlinks so you can check it out.

41:09.240 --> 41:13.560
These slides were posted to my sub stack at six p.m. when I started the doc.

41:13.560 --> 41:16.320
So you can download a PDF version.

41:16.320 --> 41:20.240
You can watch it on Google and so forth.

41:20.240 --> 41:22.400
So we now have the data.

41:22.400 --> 41:27.640
We have the data that nobody was supposed to see.

41:27.640 --> 41:41.560
And it's now available for anyone in the world to download from my wasabi server.

41:41.560 --> 41:47.360
And not only that, today only I am throwing in for free.

41:47.360 --> 41:55.320
For free the data analysis tools that you need to be able to analyze this for yourself

41:55.320 --> 41:56.400
in five minutes.

41:56.400 --> 42:01.440
So you don't have to take my word for it because I'm just a misinformation super spreader.

42:01.440 --> 42:03.120
You can do it yourself.

42:03.120 --> 42:08.320
You get the original data which we have obfuscated so that we don't get into trouble, right?

42:08.320 --> 42:11.400
It's all HIPAA compliant.

42:11.400 --> 42:17.320
But we preserved all of the fidelity of the data so that we have shifted things such

42:17.320 --> 42:25.400
that the statistics are identical even though no record matches anything about any of the

42:25.400 --> 42:26.400
people in it.

42:26.400 --> 42:35.560
So we preserve privacy but we also expose the truth.

42:35.560 --> 42:38.520
Okay.

42:38.520 --> 42:42.440
So this is record level data and that means there's an individual record for every person.

42:42.440 --> 42:44.560
It's official government data.

42:44.560 --> 42:45.800
This is gold standard data.

42:45.800 --> 42:49.880
This is ground zero data and the ministry of truth.

42:49.880 --> 42:57.480
Oh, sorry, the ministry of health and New Zealand cannot say that this is fraudulent

42:57.480 --> 43:01.480
data or that this is not the data from their database.

43:01.480 --> 43:08.440
If they do that, they will be digging themselves into a hole which will lead to criminal prosecution

43:08.440 --> 43:11.360
of the people that lie about this.

43:11.360 --> 43:17.640
This is not something they are going to be able to tell a fact checker and claim that

43:17.640 --> 43:24.040
this was not real data and it doesn't get any better than this.

43:24.040 --> 43:26.800
I told you, revenge of the nerd.

43:26.800 --> 43:34.320
You don't want to mess with an MIT electrical engineering master of science degree.

43:34.320 --> 43:43.240
You know, this is what MIT is all about is to enable people like me who are critical

43:43.240 --> 43:48.640
thinkers to go and use the skills that I learned in MIT, 6-0-4-1.

43:48.640 --> 44:00.880
I had algebraic as my teacher and actually apply those skills to solving real world problems.

44:00.880 --> 44:04.520
And so for a professional misinformation spreader such as my self, this data is the

44:04.520 --> 44:06.360
Rosetta Stone.

44:06.360 --> 44:08.000
It unlocked all the mysteries.

44:08.000 --> 44:11.040
I thought maybe the Medicare data was confounded somehow.

44:11.040 --> 44:14.600
I mean, it's like, well, all these people are telling me it's safe and effective and

44:14.600 --> 44:20.720
I'm crazy and all this, no, I'm not at all.

44:20.720 --> 44:28.560
I we can statistically prove now whether a vaccine is safe or not from record level data.

44:28.560 --> 44:30.720
And the results are objective and easy to interpret.

44:30.720 --> 44:33.640
Does slope go up or to go down?

44:33.640 --> 44:35.920
It's really easy, up or down.

44:35.920 --> 44:38.480
It's not that complicated.

44:38.480 --> 44:41.560
Now, have you ever wondered why it's always kept hidden, right?

44:41.560 --> 44:46.560
It's basically that New Zealand record level data shows about the COVID vaccine.

44:46.560 --> 44:50.040
It's not pretty, you know, 13 million people have been killed worldwide.

44:50.040 --> 44:51.160
That's what it shows.

44:51.160 --> 44:58.280
13 million people were killed worldwide by the COVID vaccines who were the perpetrators

44:58.360 --> 45:03.360
who were the governments, you know, President Biden.

45:03.360 --> 45:12.520
He's got 675,000 Americans blood on his hands.

45:12.520 --> 45:16.080
And they did it for no benefit.

45:16.080 --> 45:17.920
It wasn't like it saved any lives either.

45:17.920 --> 45:20.320
It didn't save any hospitalizations.

45:20.320 --> 45:21.840
No benefit whatsoever.

45:21.840 --> 45:29.840
With one exception, it did provide immunity for the pharmaceutical companies.

45:29.840 --> 45:36.080
That is the only immunity it provided is for the pharma companies.

45:36.080 --> 45:41.720
The shots mean it more likely you would die if you were elderly, which is the very population

45:41.720 --> 45:44.400
that the shots were designed to protect.

45:44.400 --> 45:49.600
Now a safe vaccine, as I said, should have this, you know, flat curve that goes downwards.

45:49.600 --> 45:51.800
This is actually the flu shot.

45:51.800 --> 45:52.800
Look at that.

45:52.800 --> 45:58.160
We see the same pattern for its day zero deaths and the flu shots aren't sloping down like

45:58.160 --> 45:59.160
they should.

45:59.160 --> 46:02.280
They should start to sloping down on day 21.

46:02.280 --> 46:04.160
Look at those flu shots.

46:04.160 --> 46:09.480
Now that's a problem, but, you know, nobody looks at the flu shots, they all assume safe

46:09.480 --> 46:14.760
and effective like, you know, like our pneumococcal.

46:14.760 --> 46:19.320
That's what it should look like, except for that one little whoops, not safe dot.

46:19.320 --> 46:24.360
In fact, if we could get rid of that one little dot in the pneumococcal, it'd actually

46:24.360 --> 46:25.360
be a safe vaccine.

46:25.360 --> 46:30.800
I don't know if it works, but it passes the safe test.

46:30.800 --> 46:32.760
It looks like a safe vaccine.

46:32.760 --> 46:34.160
COVID vaccine, EPIC fail.

46:34.160 --> 46:36.080
This is the New Zealand data.

46:36.080 --> 46:37.560
Nobody's ever seen this before.

46:37.560 --> 46:38.840
This is the estimated baseline.

46:38.840 --> 46:44.760
This is what people should be normally in this cohort dying at and look at what happens.

46:44.760 --> 46:45.760
It goes up.

46:45.760 --> 46:47.280
And then it goes down.

46:47.280 --> 46:53.080
Now this is averaged over five doses, okay?

46:53.080 --> 46:54.320
So this is the average effect.

46:54.320 --> 46:56.880
So each dose is going to have a different response curve.

46:56.880 --> 46:59.600
We saw what the response curve was for dose one.

46:59.600 --> 47:01.240
It went up for 12 months.

47:01.240 --> 47:08.120
So this is the combined response curve from the New Zealand data showing you what happens.

47:08.120 --> 47:09.720
And it is not pretty.

47:09.720 --> 47:11.720
It is killing people.

47:11.720 --> 47:17.840
And at the end, when we get to time 80 weeks since the shot, we're starting to get into

47:17.840 --> 47:20.120
noise because we run out of data.

47:20.120 --> 47:22.920
We run out of time to die, essentially.

47:22.920 --> 47:29.280
So the numerator becomes small, the denominator becomes small, and that creates these large

47:29.280 --> 47:30.280
errors.

47:30.280 --> 47:35.160
Because zero divided by zero is either zero or it's infinity.

47:35.160 --> 47:38.720
So could anything be happening in the background to explain this?

47:38.720 --> 47:41.680
Because this is what, you know, people wonder, well, maybe there's some explanation.

47:42.680 --> 47:48.120
For, you know, things going up and open up for 365 days monotonically.

47:48.120 --> 47:49.960
Maybe there is.

47:49.960 --> 47:52.200
There's no possible way.

47:52.200 --> 47:54.560
This is with a silent F.

47:54.560 --> 47:57.160
No possible way that that could be the case.

47:57.160 --> 47:58.840
And I have looked at this thing.

47:58.840 --> 48:00.840
I have my dashboard.

48:00.840 --> 48:03.960
It's in the V3 spreadsheet here in the dashboard.

48:03.960 --> 48:05.080
You see the dashboard.

48:05.080 --> 48:07.760
The upper graphs are the independent variables.

48:07.760 --> 48:11.480
Y axis, of course, is the dependent variable.

48:11.480 --> 48:17.640
And I look at the actual mortality rates for the four different independent variables.

48:17.640 --> 48:19.520
And then below, I actually show the number of deaths.

48:19.520 --> 48:24.160
So you can see whether you're going to have confidence in the data above.

48:24.160 --> 48:28.680
So with these eight charts, you can tell exactly what's going on in my dashboard.

48:28.680 --> 48:32.200
And then I spend hours changing the variables.

48:32.200 --> 48:33.760
I say, well, let's look at those three.

48:33.760 --> 48:34.760
Let's look at those four.

48:34.760 --> 48:35.760
Let's look at under 60.

48:35.760 --> 48:39.280
Let's look at over 60.

48:39.280 --> 48:40.600
Lots of interesting stuff there.

48:40.600 --> 48:44.120
So under 60-year-olds, what we see is a flat death card.

48:44.120 --> 48:47.520
It looks like a safe vaccine for under 60.

48:47.520 --> 48:48.920
It's not.

48:48.920 --> 48:54.040
But we just don't have enough data to get the resolution on that.

48:54.040 --> 49:00.000
And there's that data anomaly at the very beginning because of the properties of the

49:00.000 --> 49:01.080
data that we got.

49:01.080 --> 49:03.960
We only got people who are vaccinated.

49:03.960 --> 49:06.920
And we only got it when they got their first vaccine.

49:06.920 --> 49:09.560
And so people ramped up.

49:09.560 --> 49:15.240
We didn't get a nice curve that starts on a specific time.

49:15.240 --> 49:18.960
So that's why we have that data strangeness there.

49:18.960 --> 49:22.600
And then 80 to 90-year-olds, you can see the slope goes up.

49:22.600 --> 49:23.600
OK.

49:23.600 --> 49:26.800
So if you're elderly, you don't want to get these shots.

49:26.800 --> 49:31.120
And if you're a young person, you don't want to get the shots either because we just don't

49:31.120 --> 49:35.600
have enough resolution to show you just because we don't have enough young people dying.

49:35.680 --> 49:41.280
But it shows as a control group because it says that we got the right calculation because

49:41.280 --> 49:45.200
we're getting a constant death rate for the young.

49:45.200 --> 49:48.040
Because the young are less affected by this vaccine.

49:48.040 --> 49:50.400
The vaccine primarily kills elder people.

49:50.400 --> 49:53.600
And that's why we got this constant rate.

49:53.600 --> 49:55.360
So we have confidence.

49:55.360 --> 49:59.440
This gives us confidence in our tools that the tools worked.

49:59.440 --> 50:03.960
And also the data is correct, that there's nothing wrong.

50:04.040 --> 50:09.160
We would have seen something weird if any of these things were broken.

50:09.160 --> 50:12.440
So it's another quality control thing that you can look at.

50:12.440 --> 50:16.000
And so there are artifacts and we talked about that.

50:16.000 --> 50:17.720
So here's the killer.

50:17.720 --> 50:21.200
Everybody says, oh, Steve, you're a misinformation spreader.

50:21.200 --> 50:23.280
And you're an engineer and you're not a doctor.

50:23.280 --> 50:25.640
And you don't know statistics.

50:25.640 --> 50:26.120
OK.

50:26.120 --> 50:26.600
All right.

50:26.600 --> 50:28.360
You know whatever.

50:28.360 --> 50:31.040
So here's a guy who knows statistics.

50:31.120 --> 50:36.640
This guy is the worst enemy for the health authorities.

50:36.640 --> 50:43.200
This guy, Norman Fenton, is a professor of statistics and information risk and information

50:43.200 --> 50:48.760
management in Queens, Mary University of London.

50:48.760 --> 50:56.440
And he is the only guy in the world who discovered that the UK data was bogus.

50:56.480 --> 51:01.160
And he wrote a letter to the UK Office of National Statistics saying you're wrong.

51:01.160 --> 51:02.480
Your stats are wrong.

51:02.480 --> 51:04.720
And they said you're right.

51:04.720 --> 51:11.040
So this guy is the biggest enemy of the state because he's a true teller.

51:11.040 --> 51:13.320
And he understands math.

51:13.320 --> 51:14.680
There you go.

51:14.680 --> 51:17.320
And it was acknowledged by the ONS.

51:17.320 --> 51:21.280
Nobody else in the world has those credentials.

51:21.280 --> 51:23.600
So this guy is the top dog.

51:23.640 --> 51:30.800
And he said, there is now no doubt the vaccine is increasing the mortality rate of older

51:30.800 --> 51:35.720
people, the very population it was meant to serve.

51:35.720 --> 51:39.880
So you don't have to take my word for it anymore, folks.

51:39.880 --> 51:42.680
Norman Fenton has said it.

51:42.680 --> 51:45.920
And nobody can challenge that guy.

51:45.920 --> 51:46.760
Nobody in the world.

51:46.760 --> 51:49.800
That's why nobody will debate him.

51:49.800 --> 51:58.000
Even better, Harvey Rich has an H index of 110.

51:58.000 --> 52:06.240
There are very few people in the world with an H index of over or anywhere close to 110.

52:06.240 --> 52:14.440
What this means is that you have published 110 papers that have 110 citations or more.

52:14.440 --> 52:20.080
This is asymptotically harder and harder to achieve as the numbers go up.

52:20.080 --> 52:24.360
It's easy to achieve an H index of 6 or 7.

52:24.360 --> 52:29.520
And like what I have, it's very hard to get into the hundreds, very few people in the

52:29.520 --> 52:31.880
world ever get into the hundreds.

52:31.880 --> 52:37.360
And if he wrote on more popular subjects, his H index would be even higher.

52:37.360 --> 52:42.400
This guy is one of the top epidemiologists in the world.

52:42.400 --> 52:47.040
And he's also one of the top epidemiologists, of course, in the United States.

52:47.040 --> 52:53.040
And what he said, in way too many words, because this is how they talk, you know, if there

52:53.040 --> 53:00.360
was a mass shooting and everybody died, the epidemiologists would say, well, let's look

53:00.360 --> 53:06.760
at confounders and we have to have the medical history of everybody who died and, you know,

53:06.760 --> 53:13.200
let's run some analysis, Cox's regression, you know, survival analysis, hazard ratio

53:13.200 --> 53:18.280
analysis on these people and, you know, we can't really say for sure whether they were

53:18.280 --> 53:23.520
killed by the gunman, you know, I mean, this is how they talk, okay.

53:23.520 --> 53:27.720
What he said is, you've made a very strong case.

53:27.720 --> 53:31.400
In other words, your case is airtight, I'm going to translate for you.

53:31.400 --> 53:35.320
He says, appreciably increase mortality rates, okay.

53:35.320 --> 53:39.360
What he means is mortality rates are off the charts, but I have to say, I have to tone

53:39.360 --> 53:41.000
it down.

53:41.000 --> 53:45.800
And then he says, I was not aware of actual evidence that the increased mortality that

53:45.800 --> 53:47.640
you've been shown has a different cause.

53:47.640 --> 53:52.440
In other words, it's a slim dunk, it was the vaccine, but I just can't say that, okay.

53:52.440 --> 53:58.600
This is, this is, I hope that translation was helpful from science to, you know, what they

53:58.600 --> 54:01.000
really mean, because that's, that's the way it goes.

54:01.000 --> 54:06.040
Yes, he has to talk in funny language like that, because otherwise his peers say, no,

54:06.040 --> 54:08.480
you can't say that, okay.

54:08.480 --> 54:11.520
So the question is by how much?

54:11.520 --> 54:15.960
And so we can, we can now use this New Zealand data to make that calculation.

54:15.960 --> 54:22.120
And when I did that, I found about, on average, for all the doses, 216 excess deaths per

54:22.120 --> 54:24.120
hundred k person years.

54:24.120 --> 54:30.000
And if you assume that there are normally two doses of vaccine per year, because everybody's,

54:30.000 --> 54:35.320
you know, upping, getting it every six months, this is, this is a crude estimate, okay.

54:35.320 --> 54:42.640
So it turns out that if you do that and you realize two doses per person year and do the

54:42.640 --> 54:47.600
math, the answer is one excess death per thousand doses.

54:47.600 --> 54:53.640
So if there were 675 million doses given in the US, that means 675,000 people died in

54:53.640 --> 54:54.640
the US.

54:54.640 --> 54:59.200
If there are 13 billion doses given worldwide, it means 13 million people died worldwide,

54:59.200 --> 55:00.720
and they were killed by the vaccine.

55:00.720 --> 55:03.720
I see we lost our MIT students, sorry.

55:03.720 --> 55:29.160
Yep, got to go, okay, yeah, yeah, okay, we, okay.

55:29.160 --> 55:36.040
Right, we've got to later, okay, the, um, the killed estimate, 13 million people worldwide,

55:36.040 --> 55:45.240
675,000 Americans, ethical skeptic, looked at survey data, estimated 1.39 million excess

55:45.240 --> 55:51.480
deaths in the US, far larger than I, so he used a different method.

55:51.480 --> 55:54.080
And the same thing, of course, is happening in other countries.

55:54.080 --> 55:58.720
A dentist ranked court estimated, not 13 million people died, but 17 million people died.

55:58.720 --> 56:00.280
So he's getting the same numbers.

56:00.280 --> 56:04.240
We're basically in the same ballpark, we're basically kind of rearranging dentures on

56:04.240 --> 56:05.240
Titanic.

56:05.240 --> 56:09.880
The UK ONS data, if you look at that, the AC, the all-cause mortality significantly higher

56:09.880 --> 56:13.920
in the vaccinated for 20 consecutive months, pretty stunning.

56:13.920 --> 56:16.320
This is, this data has been out there.

56:16.320 --> 56:21.120
And if you, if you plot it, you'll see that, look at that upward slope, upward slope over

56:21.120 --> 56:22.120
time.

56:22.120 --> 56:26.120
So as people are getting these vaccines, they're dying at a higher and higher rate over time.

56:27.120 --> 56:30.080
UK ONS has never plotted this.

56:30.080 --> 56:35.120
Somebody who basically thinks I'm wrong, sent this to me and I said, great, I'm going to

56:35.120 --> 56:40.560
be using this slide in my presentation because it shows the upward slope and it slows upward

56:40.560 --> 56:44.200
and then it slows down just like you saw in the New Zealand data.

56:44.200 --> 56:49.280
And look what happened in the Philippines, backstests were five times the COVID-19 deaths.

56:49.280 --> 56:54.880
Okay, and the Philippines excess deaths skyrocket right after the vaccine went out.

56:54.880 --> 56:59.360
There was nothing, no excess deaths prior to the vaccine and look what happened in the

56:59.360 --> 57:00.360
Philippines.

57:00.360 --> 57:01.680
They have an investigation going now.

57:01.680 --> 57:11.280
And this is from, from my friend Ben at US mortality and he wrote on Twitter, it's as

57:11.280 --> 57:14.080
clear as it gets.

57:14.080 --> 57:22.920
Flat line, in terms of excess deaths, they roll out the vaccine and voila, man, it just

57:22.920 --> 57:26.960
peeks right up there, doesn't it?

57:26.960 --> 57:32.840
And of course, I did some surveys of my readers and found that the vaccine killed three and

57:32.840 --> 57:34.960
a half times more Americans than the COVID virus.

57:34.960 --> 57:37.520
So this is all consistent with the data you've seen before.

57:37.520 --> 57:44.040
You can do your own surveys on Twitter, sounds too hard to believe, but, you know, this is

57:44.040 --> 57:46.400
the result of the survey that I did.

57:46.400 --> 57:52.880
And the thing about my followers is that they're pretty much unvaccinated and their family

57:52.880 --> 57:54.760
studies are unvaccinated too.

57:54.760 --> 58:01.120
So this is their vaccinated, and this was asking about a lost family member due to COVID or

58:01.120 --> 58:02.120
the vaccines.

58:02.120 --> 58:05.520
And these people are under vaccinated, right, because they're my followers.

58:05.520 --> 58:06.600
They don't believe in vaccines.

58:06.600 --> 58:10.320
So they're under vaccinated and yet look at the numbers and they have no incentive to

58:10.320 --> 58:11.320
lie on this.

58:11.320 --> 58:12.640
Here's the United States Medicare.

58:12.640 --> 58:14.360
We saw this before, slow upwards.

58:14.360 --> 58:15.360
This is the Maldives.

58:15.360 --> 58:18.480
I have record level data from the Maldives.

58:18.480 --> 58:19.480
Same thing.

58:19.480 --> 58:23.480
Times up after you give the shot.

58:23.480 --> 58:24.480
Climes up.

58:24.480 --> 58:27.720
Oh, and this is my favorite, Israeli data.

58:27.720 --> 58:29.680
This is from the Israeli Ministry of Health.

58:29.680 --> 58:32.560
So let's look at the baseline calculation for Israel.

58:32.560 --> 58:34.520
How many people die in Israel a day?

58:34.520 --> 58:38.800
It's 28.4 people died per day in Israel.

58:38.800 --> 58:43.480
This is, I asked Bart, because I wanted to have a neutral source, and Bart is very what

58:43.480 --> 58:44.840
we call a blue pill.

58:44.840 --> 58:46.960
Bart thinks the vaccines are safe, okay?

58:46.960 --> 58:51.240
So if you do a Bart Korean, it'll say, you know, about the vaccine.

58:51.240 --> 58:55.320
And here are the results of breakdown in terms of the age group.

58:55.320 --> 58:58.240
So 65 plus, those are the people who are more likely to die.

58:58.240 --> 59:04.120
It's 83.7%, but that's as of 2023.

59:04.120 --> 59:13.320
So we're going to take it by 83%, 28.4 deaths is basically around an estimate of 23.5 deaths

59:13.320 --> 59:15.080
per day.

59:15.080 --> 59:16.920
That's what the red line represents.

59:16.920 --> 59:21.840
Look what happens in the first 60 days in Israel.

59:21.840 --> 59:26.000
This is record level data from Israel summarized by the Israeli Ministry of Health.

59:26.000 --> 59:30.080
This has been publicly available for a long time on the internet.

59:30.080 --> 59:35.520
Look what happens to the death count in Israel.

59:35.520 --> 59:39.400
And look what happens in the first seven months after dose two is given.

59:39.400 --> 59:47.080
The peak is 3.8 times from the baseline, and nobody notices except for me.

59:47.080 --> 59:54.680
This has been in public view since March 7th at 2023, when MIT professor Redzef Levy posted

59:54.680 --> 59:57.720
it on Twitter.

59:57.720 --> 01:00:01.560
Nobody cares how many people the vaccine kills or injures.

01:00:01.560 --> 01:00:06.280
The medical community does whatever they're told to do.

01:00:06.280 --> 01:00:11.200
They are incapable of independent thought, and this is proof of it.

01:00:11.200 --> 01:00:13.560
It does not matter how many people die.

01:00:13.560 --> 01:00:17.600
If the medical community is told by the CDC that the vaccines are safe, the vaccines

01:00:17.600 --> 01:00:22.400
are safe, and it doesn't matter how many people have to die, the vaccines are safe, and they

01:00:22.400 --> 01:00:26.280
will continue to recommend them, otherwise they will be stripped of their licenses.

01:00:26.280 --> 01:00:30.440
They don't want to lose their job, and they don't want to lose their board certifications,

01:00:30.440 --> 01:00:35.280
and so they remain silent while the killing happens, and even the whistle blowers.

01:00:35.760 --> 01:00:40.040
I get these messages saying, so and so, yeah, they're seeing all this stuff, but they don't

01:00:40.040 --> 01:00:46.200
want to go on record because they need their job, so they're keeping their mouth shut.

01:00:46.200 --> 01:00:53.480
Dr. Pete McCulloch didn't keep his mouth shut.

01:00:53.480 --> 01:00:59.280
He's one of my heroes, he's one of the top cardiologists in the world, and he just had

01:00:59.280 --> 01:01:05.560
his board certification revoked for saying that the COVID vaccines are killing people.

01:01:05.560 --> 01:01:11.280
He had his board certification revoked because he told the truth.

01:01:11.280 --> 01:01:17.720
Here's the letter telling him, unless he wins his appeal, his board certification so he

01:01:17.720 --> 01:01:24.800
can practice medicine is revoked because he has been saying the truth.

01:01:24.800 --> 01:01:27.200
That's why people don't speak out.

01:01:27.200 --> 01:01:34.640
Here's another courageous, this US Navy medical officer spoke out, heart failure is

01:01:34.640 --> 01:01:41.880
up by 973% in the military.

01:01:41.880 --> 01:01:49.680
That is, if that's a train wreck, I mean, the words cannot describe this.

01:01:49.680 --> 01:01:55.400
So they tell their superiors, and the superior says, okay, you know, no problem, you know,

01:01:55.400 --> 01:01:59.320
this will continue to give the shots.

01:01:59.320 --> 01:02:05.160
They don't change, they do whatever they're told, it doesn't matter how many people need

01:02:05.160 --> 01:02:06.160
to die.

01:02:06.160 --> 01:02:13.400
Now, if this, this is, there are two figures here, once the top showing what actually happened,

01:02:13.400 --> 01:02:18.400
okay, the other figure below it is what was supposed to happen if the vaccines worked.

01:02:18.400 --> 01:02:24.320
See the difference, big difference, the debate's over.

01:02:24.320 --> 01:02:33.120
I'm going to, I guess we can just check in here to see how we're doing on the polls

01:02:33.120 --> 01:02:40.960
because we asked if the COVID vaccines are safe and we, if you voted before and you want

01:02:40.960 --> 01:02:43.600
to change your vote, you can do so now.

01:02:43.600 --> 01:02:48.480
The polls are open and I'm going to flip over here and see how we're doing.

01:02:48.480 --> 01:02:53.080
Maybe some people, I've convinced people the vaccines are safe.

01:02:53.080 --> 01:03:02.360
So let's present here and it looks like we, we got some people on our side, but we got

01:03:02.360 --> 01:03:06.240
3% leaning safe now, isn't that interesting?

01:03:06.240 --> 01:03:08.600
Oh, people are changing.

01:03:08.600 --> 01:03:09.760
Oh, okay.

01:03:09.760 --> 01:03:15.200
So we're moving, we're, we're still got some, some stragglers there, okay.

01:03:15.200 --> 01:03:22.000
So I'm going to flip off of this, let's go back and I'm doing my best.

01:03:23.000 --> 01:03:24.640
I'm trying to show the data.

01:03:24.640 --> 01:03:28.280
Maybe they're waiting to download the data so they can see it themselves.

01:03:28.280 --> 01:03:31.640
Okay.

01:03:31.640 --> 01:03:38.960
So my opposition doesn't want to talk about it at all, the $10 million offers to get them

01:03:38.960 --> 01:03:45.160
to show up here, you know, Langer was offered 10 million, I donate 10 million to MIT, wouldn't

01:03:45.160 --> 01:03:46.160
show up for that.

01:03:46.160 --> 01:03:47.160
You know why?

01:03:47.160 --> 01:03:52.320
And he could have named a team of people to have appeared in his place, instead he chose

01:03:52.320 --> 01:03:56.200
to ignore it and he didn't even give me the courtesy of a response.

01:03:56.200 --> 01:04:00.920
I went to Moderna, I went to the, because I'm in the press, I'm a journalist, so I contacted

01:04:00.920 --> 01:04:06.920
the press liaison, I left a message on her cell phone, I know who she is, I left many

01:04:06.920 --> 01:04:13.840
messages on her cell phone saying, hey, do you want to defend your vaccine?

01:04:13.840 --> 01:04:21.080
And the answer was, nothing, no response, just as you expected, you know what these

01:04:21.080 --> 01:04:25.160
are, crickets, that's what I got, 100K reward.

01:04:25.160 --> 01:04:32.160
I even offered any scientist in the world, 100K to come here and if you convinced people

01:04:32.160 --> 01:04:38.160
you were right, you didn't win the 100K, nobody wanted to challenge me live.

01:04:38.160 --> 01:04:43.640
And I even opened it up to the anti-antibaxers, so these are the people who say that I'm

01:04:43.640 --> 01:04:44.760
wrong.

01:04:44.760 --> 01:04:49.080
And I said, if any of you want to come here and challenge me live, I'm opening up the

01:04:49.080 --> 01:04:55.360
offer to them and none of them wanted to reply.

01:04:55.360 --> 01:05:01.240
And so I wrote Pierre Cori a message saying, it's so expensive to find someone to debate

01:05:01.240 --> 01:05:03.480
you nowadays, isn't it?

01:05:03.480 --> 01:05:05.480
And he wrote back agreed.

01:05:05.480 --> 01:05:07.880
I mean, that's stunning.

01:05:08.880 --> 01:05:16.560
Now, if the CDC was honest, here are the ads that they should be running.

01:05:16.560 --> 01:05:20.600
COVID-19 vaccines increase her risk for infection serious disease and death.

01:05:20.600 --> 01:05:21.600
We made a mistake.

01:05:21.600 --> 01:05:23.400
We shouldn't have recommended it.

01:05:23.400 --> 01:05:24.400
People got the shot.

01:05:24.400 --> 01:05:27.080
Sorry about that.

01:05:27.080 --> 01:05:29.000
Where is the apologies?

01:05:29.000 --> 01:05:38.280
It should say all vaccines are unsafe and Andrew Wakefield was right.

01:05:38.280 --> 01:05:50.800
And I hope I will live long enough to see this ad being promoted by the CDC.

01:05:50.800 --> 01:05:56.400
I may have to live to a thousand years old before I see that.

01:05:57.400 --> 01:06:03.640
Okay, so the New Zealand data, it's 4 million records, 33% of all vaccine records in New

01:06:03.640 --> 01:06:04.640
Zealand.

01:06:04.640 --> 01:06:06.160
There are 12 million vaccine records.

01:06:06.160 --> 01:06:08.960
It's vaccinated people only dead or alive.

01:06:08.960 --> 01:06:14.120
So anybody who got a vaccine, if they died, it's recorded, if they're living, and I personally

01:06:14.120 --> 01:06:19.360
authenticated all of this, and this is my favorite.

01:06:19.360 --> 01:06:24.000
Even the most local supporters of the vaccine can see that the New Zealand data is highly

01:06:24.000 --> 01:06:25.000
accurate.

01:06:25.000 --> 01:06:28.080
So here it is, folks, it's just the real truth.

01:06:28.080 --> 01:06:29.080
It doesn't get here.

01:06:29.080 --> 01:06:30.080
We go than this.

01:06:30.080 --> 01:06:31.080
The real truth.

01:06:31.080 --> 01:06:36.720
My sort of like arch nemesis that if I go into the truth, we can't even get one word.

01:06:36.720 --> 01:06:41.840
He's like, what about a sweet, you know, and he will not let you talk.

01:06:41.840 --> 01:06:43.440
But I caught him in a tweet.

01:06:43.440 --> 01:06:49.360
He said New Zealand tracked data very accurately with a capital V.E.R.1.

01:06:49.360 --> 01:06:53.760
So this guy is basically saying that data is legit.

01:06:53.760 --> 01:06:55.320
There's nothing better than this.

01:06:55.320 --> 01:06:58.920
This is from the guy who's promoting the vaccine.

01:06:58.920 --> 01:07:03.880
He's admitting the New Zealand data is they tracked it very accurately.

01:07:03.880 --> 01:07:05.920
You don't get any better than this, folks.

01:07:05.920 --> 01:07:08.120
This is as good as it gets.

01:07:08.120 --> 01:07:11.200
Now, here's what the headlines are saying.

01:07:11.200 --> 01:07:17.040
New Zealand records the biggest increase in registered deaths in 100 years.

01:07:17.040 --> 01:07:19.280
Alarming acceleration in New Zealand.

01:07:19.280 --> 01:07:24.280
Biggest deaths, latest official figures of 70% on last year.

01:07:24.280 --> 01:07:26.640
Does that sound like a safe vaccine?

01:07:26.640 --> 01:07:29.360
60 year olds fail.

01:07:29.360 --> 01:07:35.880
This is the New Zealand data over time, death rate over time for dose two, dose three and

01:07:35.880 --> 01:07:36.880
dose four.

01:07:36.880 --> 01:07:39.880
We have less info on dose four.

01:07:39.880 --> 01:07:42.880
For seven year olds, dose two, dose three, dose four.

01:07:42.880 --> 01:07:47.200
This is when COVID started in New Zealand, though, that's the problem.

01:07:47.200 --> 01:07:49.320
They didn't have COVID before this, according to that.

01:07:49.320 --> 01:07:50.840
It was two dose three, dose four.

01:07:50.840 --> 01:07:51.840
It's really a problem.

01:07:51.840 --> 01:07:55.120
All ages, dose two, dose three, dose four.

01:07:55.120 --> 01:07:57.360
The curve should be flat.

01:07:57.360 --> 01:08:01.120
Does anybody think those curves are flat?

01:08:01.120 --> 01:08:02.120
Okay.

01:08:02.120 --> 01:08:09.440
Because if you did think the curves are flat, there was a job for you in the CDC, in the

01:08:09.440 --> 01:08:11.600
safety department.

01:08:11.600 --> 01:08:16.600
They are looking for people just like you, if you thought they were flat.

01:08:16.600 --> 01:08:21.100
And then we have our sanity check where we looked at calendar months, and it's a

01:08:21.100 --> 01:08:25.760
flat line for people under 60.

01:08:25.760 --> 01:08:27.720
So was it caused by the vaccine?

01:08:27.720 --> 01:08:31.800
You know, because they always say, oh, it's a confounder and we have to look at 60 different

01:08:31.800 --> 01:08:32.800
variables.

01:08:32.800 --> 01:08:33.800
We have to look at the ages.

01:08:33.800 --> 01:08:34.800
We have to look at whether they have diabetes.

01:08:34.800 --> 01:08:38.440
We have to look at whether they have heart disease, no, no, you don't need to do any of

01:08:38.440 --> 01:08:39.440
that.

01:08:39.440 --> 01:08:40.440
There's no other viable alternative.

01:08:40.440 --> 01:08:42.640
It meets all the bad for tilt criteria.

01:08:42.640 --> 01:08:43.920
Nothing else could cause such a change.

01:08:43.920 --> 01:08:47.680
Here are the five red for tilt criteria, and it meets the consistency, strength of

01:08:47.680 --> 01:08:53.600
association, specificity, temporal relation, and biologic plausibility.

01:08:53.600 --> 01:08:59.160
In other words, is it plausible that we could massively increase the death rate if we injected

01:08:59.160 --> 01:09:00.880
you with a poison?

01:09:00.880 --> 01:09:02.720
The answer is yes.

01:09:02.720 --> 01:09:04.720
That could do it.

01:09:04.720 --> 01:09:06.600
That could massively increase it over time.

01:09:06.600 --> 01:09:09.000
I mean, you know, that's what it's about.

01:09:09.000 --> 01:09:13.560
Big picture, massive increase in all-cause mortality.

01:09:13.560 --> 01:09:15.160
This is Robert Malone.

01:09:15.160 --> 01:09:16.160
The...

01:09:16.160 --> 01:09:17.160
No, it's not.

01:09:17.160 --> 01:09:22.920
He's credited as inventing the technology behind these MRAs.

01:09:22.920 --> 01:09:24.400
That's Danny Rancor.

01:09:24.400 --> 01:09:31.840
And he has been a vocal critic of these vaccines, even though he has patents on the underlying

01:09:31.840 --> 01:09:33.000
technology.

01:09:33.000 --> 01:09:38.160
And he says, basically, it's not 14 million lives saved like they projected.

01:09:38.160 --> 01:09:39.160
Wow.

01:09:39.160 --> 01:09:41.840
But over 17 million dead from the mRNA COVID vaccine.

01:09:41.840 --> 01:09:44.880
That one, Robert Malone, said interesting.

01:09:44.880 --> 01:09:50.160
Another Twitter quote, quote, I'm going to skip, go fast here, because I'm getting short

01:09:50.160 --> 01:09:52.520
of time.

01:09:52.520 --> 01:09:53.520
This is from...

01:09:53.520 --> 01:09:55.440
Chris Martin, he's a gold figure.

01:09:55.440 --> 01:09:58.440
And these are of malignant neoplasm of the breasts.

01:09:58.440 --> 01:10:01.600
In other words, cancer of the breast, cancer of the brain, blah, blah, blah.

01:10:01.600 --> 01:10:09.760
And you can see how these things are going up dramatically after the vaccine rollout.

01:10:09.760 --> 01:10:13.440
And embalmers, it's totally upside down.

01:10:13.440 --> 01:10:16.280
Embalmers used to rarely see clots.

01:10:16.280 --> 01:10:19.040
It was like 10 or 15 percent.

01:10:19.040 --> 01:10:23.280
Now they rarely see someone without a clot who dies.

01:10:23.280 --> 01:10:24.280
It's the other way around.

01:10:24.280 --> 01:10:26.760
It's like 80 percent have clots.

01:10:26.760 --> 01:10:31.440
The world has turned upside down.

01:10:31.440 --> 01:10:35.120
And the CDC, of course, will say that there's no evidence of harm because they refuse to

01:10:35.120 --> 01:10:42.800
investigate or view any of these adverse event data that is found by these people.

01:10:42.800 --> 01:10:43.800
And there are...

01:10:43.800 --> 01:10:47.080
So the bottom line is the four facts you need to know about the COVID vaccine.

01:10:47.080 --> 01:10:49.040
It increases your risk of infection.

01:10:49.040 --> 01:10:53.840
It will not do anything to your risk of hospitalization from COVID.

01:10:53.840 --> 01:10:54.840
It is complete...

01:10:54.840 --> 01:10:56.320
It's a zero on that.

01:10:56.320 --> 01:11:02.400
It will increase your risk of death from COVID, and it will also increase your risk of death

01:11:02.400 --> 01:11:04.800
from all cosmortality.

01:11:04.800 --> 01:11:07.120
Who would take a shot like this?

01:11:07.120 --> 01:11:08.120
There's probably...

01:11:08.120 --> 01:11:12.920
There's one person in the audience who would, but most people shouldn't.

01:11:12.920 --> 01:11:14.720
Nobody should be getting any of these shots.

01:11:14.720 --> 01:11:15.720
Nobody.

01:11:15.720 --> 01:11:16.720
There's no...

01:11:16.720 --> 01:11:19.800
Like, kids aren't dying.

01:11:19.800 --> 01:11:24.480
Now, here's the thing.

01:11:24.480 --> 01:11:26.840
Why aren't we told this?

01:11:26.840 --> 01:11:29.640
It's a systemic defect.

01:11:29.640 --> 01:11:33.200
The CDC does not have the record level data.

01:11:33.200 --> 01:11:38.200
So they don't get the vaccination records coming in from the states.

01:11:38.200 --> 01:11:45.880
So they cannot do a cohort time series analysis like I did because they don't know when people

01:11:45.880 --> 01:11:46.880
were vaccinated.

01:11:46.880 --> 01:11:48.800
All they know is when people died.

01:11:48.800 --> 01:11:54.400
They don't know the dates when individual people die, and when those people who died

01:11:54.400 --> 01:11:55.600
were vaccinated.

01:11:55.600 --> 01:12:00.840
The CDC cannot do the analysis, they don't have the data, and they don't want to ask

01:12:00.840 --> 01:12:06.200
for the data either, because if they ask for the data, then I could FOIA it, and I would

01:12:06.200 --> 01:12:07.200
know.

01:12:07.200 --> 01:12:13.080
So the CDC basically says, nope, we don't want to know, but the vaccine is safe.

01:12:13.080 --> 01:12:14.240
Trust us.

01:12:14.240 --> 01:12:15.800
And so the states...

01:12:15.800 --> 01:12:21.040
Now the states have the record level data, but they don't do the analysis because why

01:12:21.040 --> 01:12:22.040
should they?

01:12:22.040 --> 01:12:23.800
The CDC says it's safe.

01:12:23.800 --> 01:12:27.760
So they got the records, but they don't look at them.

01:12:27.760 --> 01:12:30.520
It's a catch-22.

01:12:30.600 --> 01:12:33.720
Nobody basically analyzes the data.

01:12:33.720 --> 01:12:37.040
It's all, you know, this systemic defect.

01:12:37.040 --> 01:12:43.880
So infection, we have a paper from the Cleveland Clinic, which is one of the top places in

01:12:43.880 --> 01:12:50.000
the world, medical organizations in the world, and it shows that if the more recently you

01:12:50.000 --> 01:12:53.120
got a vaccine, the worse off you are.

01:12:53.120 --> 01:12:56.360
You are better to be under vaccinated to protect you from COVID.

01:12:56.400 --> 01:13:02.520
And in fact, there's another chart that shows that every time you get a vaccine, it increases

01:13:02.520 --> 01:13:06.480
your risk of getting COVID.

01:13:06.480 --> 01:13:11.480
And that's in the peer-reviewed, published in the peer-reviewed literature, was not revoked,

01:13:11.480 --> 01:13:15.600
was not retracted, thank you.

01:13:15.600 --> 01:13:16.600
Here's another paper.

01:13:16.600 --> 01:13:17.600
This is in Java.

01:13:17.600 --> 01:13:18.600
This is my favorite paper.

01:13:18.600 --> 01:13:19.600
This is my favorite paper.

01:13:19.600 --> 01:13:20.600
One of my favorite...

01:13:20.600 --> 01:13:21.600
Probably my favorite paper of all time.

01:13:21.600 --> 01:13:23.880
It's a research letter, April 6, 2023.

01:13:23.880 --> 01:13:29.080
This is from a top epidemiologist inside the VA.

01:13:29.080 --> 01:13:31.200
And this guy is an H-index of like 77.

01:13:31.200 --> 01:13:32.200
I mean, he's...

01:13:32.200 --> 01:13:33.360
This guy's a smart guy.

01:13:33.360 --> 01:13:36.280
And he's doing his study and he's showing, hey, let's look at the...

01:13:36.280 --> 01:13:37.280
I'm going to do...

01:13:37.280 --> 01:13:40.480
I'm going to look at people hospitalized for COVID versus people hospitalized from the

01:13:40.480 --> 01:13:44.960
flu, and I'm going to compare these groups and I'm going to see how they do in terms

01:13:44.960 --> 01:13:45.960
of death.

01:13:45.960 --> 01:13:47.600
So they said, this is my baseline.

01:13:47.600 --> 01:13:50.800
And so he looked at the baseline characteristics of people who are admitted to the hospital

01:13:50.800 --> 01:13:55.800
for flu and people who are admitted to the hospital for COVID.

01:13:55.800 --> 01:13:59.160
And he says, look, these cohorts are very, very similar.

01:13:59.160 --> 01:14:00.160
Their ages are similar.

01:14:00.160 --> 01:14:01.960
Their comorbidities are similar.

01:14:01.960 --> 01:14:05.440
And look, even their vaccination status is similar.

01:14:05.440 --> 01:14:07.200
Very similar groups.

01:14:07.200 --> 01:14:08.800
So now, let me do the experiment.

01:14:08.800 --> 01:14:14.640
Now that I've shown you that my groups are very similar, you see the problem here?

01:14:14.640 --> 01:14:20.720
The groups aren't supposed to be similar if you got hospitalized for COVID versus Devax.

01:14:20.720 --> 01:14:25.520
So if the flu and the COVID vaccines protected against hospitalization, you should see a

01:14:25.520 --> 01:14:27.600
dramatic difference between the groups.

01:14:27.600 --> 01:14:31.280
This is what it should look like, and I'm making these numbers up, but the point is there

01:14:31.280 --> 01:14:34.840
should be this dramatic difference between the cohorts.

01:14:34.840 --> 01:14:41.480
And there isn't, there isn't, which means that the flu vaccine doesn't protect against

01:14:41.480 --> 01:14:44.960
infection or hospitalization, zero benefit.

01:14:44.960 --> 01:14:50.320
And it also means the COVID vaccines don't protect against infection and hospitalization.

01:14:50.320 --> 01:14:54.960
This is not zero, ladies and gentlemen, we've known this for years, those two, those three,

01:14:54.960 --> 01:15:00.480
those four, whatever it is, it's new for me, it's new for a lot of us, but the smallest

01:15:00.480 --> 01:15:07.400
impact was for the, if they got a recent booster, zero.

01:15:07.400 --> 01:15:15.120
So, and when you look at in practice, the protection against death is less than the net

01:15:15.120 --> 01:15:17.160
protection from hospitalization.

01:15:17.160 --> 01:15:21.400
It's always true that the biggest, the biggest effect are the protection from infection, and

01:15:21.400 --> 01:15:28.320
then it's a smaller amount for hospitalization and then even a smaller amount for death.

01:15:28.320 --> 01:15:35.000
So when the net benefit of the first two or zero, there's no room for a benefit, it has

01:15:35.000 --> 01:15:38.760
to be smaller than zero, the only thing smaller than zero is zero.

01:15:38.760 --> 01:15:44.440
So this one paper basically proves that there's no benefit from the COVID vaccine or the flu

01:15:44.440 --> 01:15:46.000
vaccine.

01:15:46.000 --> 01:15:48.440
And it's gold standard data from the VA.

01:15:48.440 --> 01:15:52.320
So I asked the author, I said, you should publish this as another research note to say,

01:15:52.320 --> 01:15:55.960
hey, I missed this, sorry about that, but the flu vaccines and COVID vaccines don't

01:15:55.960 --> 01:15:56.960
work.

01:15:56.960 --> 01:16:03.560
He said he didn't have time to write it up, he didn't have time to write it up, he didn't

01:16:03.560 --> 01:16:08.400
have time to write it up, he nice guy, but he didn't have time to write it up.

01:16:08.400 --> 01:16:09.400
Isn't that amazing?

01:16:09.400 --> 01:16:10.400
Isn't that astonishing?

01:16:10.400 --> 01:16:12.520
The most astonishing thing I've seen.

01:16:12.520 --> 01:16:14.800
And then of course there's death, death from COVID.

01:16:14.800 --> 01:16:21.200
So we can't tell from this day, because it's all caused mortality data, whether this thing

01:16:21.200 --> 01:16:25.000
actually makes you less likely to die from COVID, there might have been a benefit.

01:16:25.000 --> 01:16:29.720
But we have the US nursing home data and it shows that the COVID vaccine had no difference

01:16:29.720 --> 01:16:36.680
at all in terms of your, if you've got COVID, your risk of dying from COVID.

01:16:36.680 --> 01:16:42.160
Now the orange line here is basically the death curve and I've magnified it.

01:16:42.160 --> 01:16:44.000
It's on the right-hand axis.

01:16:44.000 --> 01:16:48.680
So you can see how it is relative to the infections and actually see how it changes.

01:16:48.680 --> 01:16:55.200
So in the first, the left-hand part of the graph, the pre-Omicron, you're seeing that

01:16:55.200 --> 01:17:03.080
the orange lines are like above the infection line and the scales are the same throughout

01:17:03.080 --> 01:17:05.000
the whole thing.

01:17:05.000 --> 01:17:15.240
But as soon as Omicron hits, look, the orange lines are like a tenth or 20% of the infection

01:17:15.240 --> 01:17:20.560
line, which means that your risk of dying went way down.

01:17:20.560 --> 01:17:23.520
But it wasn't the vaccine that caused it.

01:17:23.520 --> 01:17:25.760
It was Omicron.

01:17:25.760 --> 01:17:28.800
It was the variant that caused the change.

01:17:28.800 --> 01:17:33.320
And you can see this also with an odds ratio calculation.

01:17:33.320 --> 01:17:40.920
So you hit the vaccine rollout and that orange line is supposed to go down and it goes up.

01:17:40.920 --> 01:17:44.680
And people don't like to talk about this data because it goes the wrong way.

01:17:44.680 --> 01:17:48.880
And as an example, Apple Valley Village Healthcare Center, they saw seven times higher COVID

01:17:48.880 --> 01:17:52.000
death rates after the COVID vaccine was rolled out.

01:17:52.000 --> 01:17:54.320
It was supposed to go the other way.

01:17:54.320 --> 01:17:58.660
And of course, nobody who currently works at Apple Valley, village would talk to me on

01:17:58.660 --> 01:18:00.360
the record about this.

01:18:00.800 --> 01:18:05.160
It'll cause mortality, gold standard, time series, the cohort analysis, this is what

01:18:05.160 --> 01:18:06.160
we did.

01:18:06.160 --> 01:18:13.000
We looked at, we put people in buckets, they got, so this is an example of the record

01:18:13.000 --> 01:18:19.960
here and we put them in buckets of man weeks from the time of the dose and so forth.

01:18:19.960 --> 01:18:25.080
This is not too complicated, but long, I don't have enough time to explain it here.

01:18:25.080 --> 01:18:28.960
But basically, they're four independent variables and one dependent variable, you analyze it

01:18:28.960 --> 01:18:29.960
and you look at it.

01:18:29.960 --> 01:18:31.320
This is what the graph should look like.

01:18:31.320 --> 01:18:33.800
They should look pretty much straight lines.

01:18:33.800 --> 01:18:38.920
And of course, morbidity goes, your mortality rate goes up with age.

01:18:38.920 --> 01:18:42.640
But they basically should be straight lines because these are mortality rates, so these

01:18:42.640 --> 01:18:48.440
are in deaths per 100,000 person years, should be straight line.

01:18:48.440 --> 01:18:55.760
Okay, so record level data, of course, it's simply the year birth, the date of death,

01:18:55.760 --> 01:18:58.560
if they're dead in the vaccination history, that's what record level data is, it looks

01:18:58.560 --> 01:19:02.720
like this, very simple and it's the key and we can instantly tell.

01:19:02.720 --> 01:19:03.720
This is why it's never.

01:19:03.720 --> 01:19:04.720
No wonder he has so many slides.

01:19:04.720 --> 01:19:06.720
They have like two words on me just going through the-

01:19:06.720 --> 01:19:07.720
We see the only explanation.

01:19:07.720 --> 01:19:08.720
What the hell?

01:19:08.720 --> 01:19:09.720
What is this?

01:19:09.720 --> 01:19:13.680
It seems they're killing people and at this point, we're rearranging deck chairs and

01:19:13.680 --> 01:19:19.040
they're like, well, did it kill, you know, like 1.3 people per thousand or 1.2 people?

01:19:19.040 --> 01:19:20.040
Who cares?

01:19:20.040 --> 01:19:21.120
It killed too many people.

01:19:21.120 --> 01:19:23.080
And was there a benefit?

01:19:23.080 --> 01:19:24.080
No, there was no benefit.

01:19:24.080 --> 01:19:25.080
No way.

01:19:25.080 --> 01:19:26.080
No way.

01:19:26.080 --> 01:19:27.080
No way.

01:19:28.080 --> 01:19:29.080
Oh, that's awesome.

01:19:29.080 --> 01:19:37.080
So, and I'm, of course, pushing for a disclosure of this information, this record level data.

01:19:37.080 --> 01:19:41.080
And I offered all this stuff to the FDA, CDC, Pfizer, Moderna, and the California Department

01:19:41.080 --> 01:19:42.800
of Public Health.

01:19:42.800 --> 01:19:44.320
And none of them wanted to see the record level data.

01:19:44.320 --> 01:19:45.320
It's fantastic.

01:19:45.320 --> 01:19:52.200
Any qualified epidemiologists to see it, nobody wanted to see the data either and the reason

01:19:52.200 --> 01:19:56.100
is I wanted to show the data to them so they could say, oh, you, you know, you goofed

01:19:56.100 --> 01:20:00.660
your assigner or whatever and I was trying to be responsible before I went public with

01:20:00.660 --> 01:20:01.660
this.

01:20:01.660 --> 01:20:03.940
So, I tried for weeks to get anybody to look at the data.

01:20:03.940 --> 01:20:08.340
Nobody wanted to take the time to explain it to me or my colleagues, how we got it all

01:20:08.340 --> 01:20:09.340
wrong.

01:20:09.340 --> 01:20:10.340
They all refused my offer.

01:20:10.340 --> 01:20:13.900
They said the vaccines are safe and they don't want the record level data that they

01:20:13.900 --> 01:20:14.900
had never seen before.

01:20:14.900 --> 01:20:16.140
They don't want to see it.

01:20:16.140 --> 01:20:19.660
It's kind of like the treasure of Sierra Madre.

01:20:19.660 --> 01:20:24.340
You all remember this scene from the treasure of Sierra Madre's when he says, when this

01:20:24.340 --> 01:20:32.020
guy says, we are the Federalists, you know, the CDC, you know, and the guys at the,

01:20:32.020 --> 01:20:36.420
why is he making all these dumb jokes asking for, you know, do you have any badges?

01:20:36.420 --> 01:20:40.300
He said, and so the guy says, data, we ain't got no data.

01:20:40.300 --> 01:20:41.300
We don't need no data.

01:20:41.300 --> 01:20:47.820
And now he's also doing a bad accent, like some kind of paraphrasing, it was actually

01:20:47.820 --> 01:20:48.820
bad.

01:20:48.820 --> 01:20:49.820
That is really awful.

01:20:49.820 --> 01:20:50.820
But I changed the word to data.

01:20:50.820 --> 01:20:58.180
Essentially, we're the feds and we don't need to show you any stinkin' data.

01:20:58.180 --> 01:21:04.700
And this kind of sums it up here in terms of our trusted authorities.

01:21:04.700 --> 01:21:06.820
You don't have to trust me even in any research.

01:21:06.820 --> 01:21:08.180
We now have that record level data.

01:21:08.180 --> 01:21:10.340
And if you think you got it wrong, let me know.

01:21:10.340 --> 01:21:12.300
You can download the data here.

01:21:12.300 --> 01:21:15.020
You use the wasabi export and download it.

01:21:15.020 --> 01:21:18.260
You can also use our clone.

01:21:18.260 --> 01:21:25.220
And these are the access codes to be able to download it yourself and you can verify.

01:21:25.220 --> 01:21:26.220
What?

01:21:26.220 --> 01:21:31.820
It's in the slide, like it's posted, it's posted.

01:21:31.820 --> 01:21:35.060
Overcoming objections, you can't prove the vaccines caused this, you didn't control

01:21:35.060 --> 01:21:36.060
for confounders.

01:21:36.060 --> 01:21:40.500
There are no confounders that can possibly explain this.

01:21:40.500 --> 01:21:41.500
Where they hide the bodies.

01:21:41.500 --> 01:21:44.500
You know, people say, oh, you know, there's no access where they hide the bodies.

01:21:44.580 --> 01:21:47.100
Oh, they hit the bodies in plain sight.

01:21:47.100 --> 01:21:50.580
New Zealand records make us increase in registered deaths in 100 years.

01:21:50.580 --> 01:21:56.980
And the UK access deaths in 2022 among worst in 50 years.

01:21:56.980 --> 01:22:00.420
That's where they hit the bodies, folks, in plain sight.

01:22:00.420 --> 01:22:01.700
Okay.

01:22:01.700 --> 01:22:03.580
And then you saw the New Zealand thing.

01:22:03.580 --> 01:22:07.020
The US had 600K access in 2021 alone.

01:22:07.020 --> 01:22:11.860
It's normally 2.8 million people who die, 3.4 million who died in 2021.

01:22:11.940 --> 01:22:17.980
And it's a good bet that most of these deaths were vaccine related.

01:22:17.980 --> 01:22:19.580
John Badwin, who's who's here.

01:22:19.580 --> 01:22:23.940
Oh, John, Baldwin is there, too.

01:22:23.940 --> 01:22:24.940
My gosh.

01:22:24.940 --> 01:22:25.940
Right on.

01:22:25.940 --> 01:22:26.940
Whoa.

01:22:26.940 --> 01:22:27.940
It's a journal.

01:22:27.940 --> 01:22:29.940
John didn't have access to vaccine.

01:22:29.940 --> 01:22:30.940
John Baldwin has the data.

01:22:30.940 --> 01:22:33.300
All he got was mortality data.

01:22:33.300 --> 01:22:35.580
He got the death records in the audience.

01:22:35.580 --> 01:22:41.340
And he found that 4,000 excess deaths were caused by the vaccines, 4,000 deaths caused

01:22:41.340 --> 01:22:42.340
by the vaccines.

01:22:42.340 --> 01:22:47.020
And he didn't even have the vaccination data to do that.

01:22:47.020 --> 01:22:49.060
That is heroic work.

01:22:49.060 --> 01:22:59.700
Now, if the vaccines were safe, he should have found fewer than 15 deaths.

01:22:59.700 --> 01:23:05.580
He found 266 times higher than the safe expectations.

01:23:05.580 --> 01:23:06.740
And here is the URL.

01:23:06.740 --> 01:23:07.980
You should go check out his article.

01:23:07.980 --> 01:23:09.780
He spent a lot of work on those slides.

01:23:09.780 --> 01:23:10.780
And he deserves your support.

01:23:10.780 --> 01:23:14.300
And he also has a link to the book on that page, right?

01:23:14.300 --> 01:23:15.300
Yeah.

01:23:15.300 --> 01:23:16.300
Real.

01:23:16.300 --> 01:23:17.300
Real.

01:23:17.300 --> 01:23:18.300
Oh, there was Kevin.

01:23:18.300 --> 01:23:19.300
I saw Kevin.

01:23:19.300 --> 01:23:20.300
The book is TheRealCDC.com.

01:23:20.300 --> 01:23:21.300
It will be maybe three weeks.

01:23:21.300 --> 01:23:22.300
There's Kevin right there.

01:23:22.300 --> 01:23:23.300
See him?

01:23:23.300 --> 01:23:24.300
That's Kevin.

01:23:24.300 --> 01:23:25.300
That's Kevin.

01:23:25.300 --> 01:23:26.300
We're supposed to get the website up and there's Kevin.

01:23:26.300 --> 01:23:27.300
There's Kevin.

01:23:27.300 --> 01:23:35.300
Hopefully it's running, if not, try it tomorrow.

01:23:35.300 --> 01:23:36.300
Thanks, dude.

01:23:37.300 --> 01:23:40.340
And that's John selling his book.

01:23:40.340 --> 01:23:41.340
Nice.

01:23:41.340 --> 01:23:42.980
I like that.

01:23:42.980 --> 01:23:44.220
And John is also doing something.

01:23:44.220 --> 01:23:47.060
Can I talk about what you're doing with the CDC?

01:23:47.060 --> 01:23:49.180
The legal process and all that?

01:23:49.180 --> 01:23:50.180
Yeah.

01:23:50.180 --> 01:23:51.180
Okay.

01:23:51.180 --> 01:23:54.620
So he's also sending a little memo to the CDC saying, guys, I'm putting you on legal

01:23:54.620 --> 01:23:58.060
notice that this is happening and these deaths are happening.

01:23:58.060 --> 01:24:02.220
Yeah, John Bodewin, we like John Bodewin, we like that guy.

01:24:02.220 --> 01:24:03.220
We like him.

01:24:03.220 --> 01:24:04.220
John Bodewin.

01:24:04.220 --> 01:24:05.220
We like him.

01:24:05.220 --> 01:24:09.100
The real text that he doesn't say is, and if you ignore this and do nothing, you're

01:24:09.100 --> 01:24:12.100
going to jail.

01:24:12.100 --> 01:24:20.340
Here are the 20 most vaccinated countries.

01:24:20.340 --> 01:24:23.100
Here's what happened to Texas when we're telling all these countries, I'm not going

01:24:23.100 --> 01:24:25.340
to show you all of them, but you get the idea.

01:24:25.340 --> 01:24:27.220
This is the VAERS data.

01:24:27.220 --> 01:24:28.220
Do you see a problem?

01:24:28.220 --> 01:24:29.780
This is when the vaccines rolled out.

01:24:29.780 --> 01:24:31.620
Do you see a problem?

01:24:31.620 --> 01:24:34.340
Nobody who works at the CDC can see a problem with this.

01:24:34.340 --> 01:24:35.340
I don't think this is normal.

01:24:35.340 --> 01:24:36.340
This is normal.

01:24:36.340 --> 01:24:37.340
Right?

01:24:37.340 --> 01:24:38.340
We're now we're talking about...

01:24:38.340 --> 01:24:39.340
Can you believe that?

01:24:39.340 --> 01:24:40.340
I mean, these people are brain dead.

01:24:40.340 --> 01:24:41.340
It's still nice to listen.

01:24:41.340 --> 01:24:43.340
You know, they hire blind people.

01:24:43.340 --> 01:24:45.340
I mean, he can try and lay it out.

01:24:45.340 --> 01:24:46.340
He's almost done.

01:24:46.340 --> 01:24:47.340
He said he had 200 and some slides.

01:24:47.340 --> 01:24:50.340
I was kidding about that, but it seems like it.

01:24:50.340 --> 01:24:51.340
Okay.

01:24:51.340 --> 01:24:52.340
Western Australia.

01:24:52.340 --> 01:24:57.220
These are essentially the VAERS report and the VAERS reporting rate in Australia, the

01:24:57.220 --> 01:24:59.060
equivalent of VAERS.

01:24:59.060 --> 01:25:01.940
Medical examiners, willful blindness.

01:25:01.940 --> 01:25:04.220
This is by Peter McCulloch, MD.

01:25:04.220 --> 01:25:06.780
We talked about him before.

01:25:06.780 --> 01:25:14.620
He found that 73.9%, you know, essentially 74% of the deaths following vaccination were

01:25:14.620 --> 01:25:19.500
due to the COVID vaccine, but all of these were labeled as, you know, just normal deaths

01:25:19.500 --> 01:25:23.420
by the medical examiners.

01:25:23.420 --> 01:25:31.940
100,000 people, sorry, 10,000 died suddenly events and pull in full public view since

01:25:31.940 --> 01:25:36.220
the vaccine program started virtually all are in the vaccinated.

01:25:36.220 --> 01:25:39.660
This is unprecedented.

01:25:39.660 --> 01:25:49.020
In Ed Dao's book, Cause Unknown, they have only found one person who was unvaccinated

01:25:49.020 --> 01:25:53.980
in the 500 people who died unexpectedly.

01:25:53.980 --> 01:25:54.980
What about that?

01:25:54.980 --> 01:25:59.020
Do you not understand or the births that never happened?

01:25:59.020 --> 01:26:02.660
This is a tweet from someone that and this resonated with a lot of people.

01:26:02.660 --> 01:26:09.420
It got 128,000 views and the person wrote, I do not know one person who has had a clear

01:26:09.420 --> 01:26:15.940
and normal pregnancy since they took the COVID vaccines, either a miscarriage, still birth

01:26:15.940 --> 01:26:16.940
or early.

01:26:16.940 --> 01:26:18.540
We can talk to Dr. Thorpe.

01:26:18.540 --> 01:26:29.620
I mean, to even have anybody say that is a disaster and to have this resonate with

01:26:29.620 --> 01:26:33.740
the public where they say, yeah, yeah, me too, me too, me too, me too, you can go and

01:26:33.740 --> 01:26:37.420
reference this tweet and you will see how many me twos there are.

01:26:37.420 --> 01:26:38.980
There are a lot of them.

01:26:38.980 --> 01:26:45.220
And of course, this, I love this one clearly baffling, right, right, that Ed Dao wrote

01:26:45.220 --> 01:26:52.100
at the top clearly baffling U.S. infant mortality rate rises for the first time in

01:26:52.100 --> 01:26:57.660
more than 20 years clearly baffling, can't figure it out.

01:26:57.660 --> 01:27:04.420
But people are now seeing the evidence for themselves, people are, this person wrote,

01:27:04.420 --> 01:27:09.900
he is doubly vexed, he's, you know, he believed in it and he says, I have never heard about

01:27:09.900 --> 01:27:14.500
so many working adults sick in my life.

01:27:14.500 --> 01:27:21.140
Okay, next objection, if this is really true, why aren't any of the MIT professors speaking

01:27:21.140 --> 01:27:22.140
out, right?

01:27:22.140 --> 01:27:26.180
Because surely MIT professors are smart and they know everything that was in the slide

01:27:26.180 --> 01:27:28.620
deck and they would be speaking out.

01:27:28.620 --> 01:27:34.060
Well, some are, MIT professor at Zaflavi.

01:27:34.060 --> 01:27:39.900
I've played him on stream before.

01:27:39.900 --> 01:27:43.180
Others choose to remain silent.

01:27:43.180 --> 01:27:49.980
And so is that how science is supposed to work is, is, is doctor, is professor Robert

01:27:49.980 --> 01:27:50.980
Langer, the new role model?

01:27:50.980 --> 01:27:51.980
No, he's not a relative.

01:27:51.980 --> 01:27:56.660
He's like, is one of his best friends and is, is security in the United States of America

01:27:56.660 --> 01:28:03.180
that duck, you duck and run for cover when you are challenged?

01:28:03.180 --> 01:28:04.740
Is that the new standard?

01:28:04.740 --> 01:28:12.180
Is MIT Institute professor Langer's running from this data?

01:28:12.180 --> 01:28:19.380
Is that the model that we want to have for MIT students for this is how you deal with

01:28:19.380 --> 01:28:21.180
adverse data?

01:28:21.180 --> 01:28:22.860
This is how you deal with truth.

01:28:22.860 --> 01:28:24.620
You run from it.

01:28:24.620 --> 01:28:32.700
You make sure that you don't show up and talk about it and defend it.

01:28:32.700 --> 01:28:40.060
You know, so how many people want to see a real debate on this data?

01:28:40.060 --> 01:28:49.540
You know, or do you prefer that we censor one side of it so that you only hear from one

01:28:49.540 --> 01:28:54.060
side, like when you turn on CNN and you only hear one side of the story.

01:28:54.060 --> 01:28:59.860
You know, it's, they, these people say, oh, yeah, we want the debate to happen, but it

01:28:59.860 --> 01:29:01.900
shouldn't have in this scientific literature.

01:29:01.900 --> 01:29:04.900
I hate that because it takes years.

01:29:04.900 --> 01:29:10.700
Like autism, for example, 20 years, for 20 years, it's been an ongoing question.

01:29:10.700 --> 01:29:11.700
Is vaccines cause autism?

01:29:11.700 --> 01:29:14.500
I look at the data, and of course it does.

01:29:14.500 --> 01:29:15.500
There's no question.

01:29:15.500 --> 01:29:19.740
I would bet my life on it that vaccines cause autism.

01:29:19.740 --> 01:29:20.740
Wow.

01:29:20.740 --> 01:29:21.740
Now, that's something-

01:29:21.740 --> 01:29:26.380
How does that work out that we resolve this in the scientific literature?

01:29:26.380 --> 01:29:27.380
Interesting.

01:29:27.380 --> 01:29:28.380
For 20 years, that's an ice-glove.

01:29:28.380 --> 01:29:29.380
That's an ice-glove.

01:29:29.380 --> 01:29:30.540
Yes, there's an effect.

01:29:30.540 --> 01:29:32.140
No, there's not.

01:29:32.140 --> 01:29:41.300
For 20 years, and that's the way these so-called scientists want to resolve ambiguity.

01:29:41.300 --> 01:29:46.860
They want to do it in the literature where it will take 20 years or maybe never.

01:29:46.860 --> 01:29:49.060
That's how they want this to be resolved.

01:29:49.060 --> 01:29:52.100
And we want it to happen now in a real-time dialogue.

01:29:52.100 --> 01:29:53.100
Okay.

01:29:53.100 --> 01:29:54.700
And then there's the objection.

01:29:54.700 --> 01:29:57.340
Well, if that's true, how come I don't know anyone who died from the vaccine?

01:29:57.340 --> 01:30:00.100
Well, you probably did, but you just didn't realize it.

01:30:00.100 --> 01:30:06.300
Like, if you're an MIT, I, uh, uh, Pologasi was a roommate.

01:30:06.300 --> 01:30:14.260
I mean, I, in B entry, I was in B entry, McGregor, in the class of 78, Paul was one

01:30:14.260 --> 01:30:18.580
of my roommates in B entry, and he died, and you know what?

01:30:18.580 --> 01:30:25.460
He died six months right after he got his first vaccine shot, exactly on schedule, exactly

01:30:25.460 --> 01:30:26.760
as predicted.

01:30:26.760 --> 01:30:31.260
He died in his own swimming pool, and they never made that public.

01:30:31.260 --> 01:30:33.700
People don't die in their own swimming pool.

01:30:33.700 --> 01:30:35.500
That doesn't happen.

01:30:35.500 --> 01:30:37.500
Pologasi was full of life.

01:30:37.500 --> 01:30:42.220
He didn't deserve what happened to him, but nobody wants to talk about it.

01:30:42.220 --> 01:30:46.980
There's another very young guy, 29, Peter Badu.

01:30:46.980 --> 01:30:50.180
I'm not sure if I'm pronouncing it correctly.

01:30:50.180 --> 01:30:53.380
He died unexpectedly.

01:30:53.380 --> 01:30:55.380
Nobody knows why.

01:30:55.380 --> 01:30:56.940
There's a survey that was done.

01:30:56.940 --> 01:31:01.300
Do you know someone, this is done by Rasmussen, when they're one of the finest polling companies

01:31:01.300 --> 01:31:05.460
in America, they're willing to ask the questions that nobody else wants to ask.

01:31:05.460 --> 01:31:10.740
You know, someone personally who died from side effects of the COVID-19 vaccine, 24%

01:31:10.740 --> 01:31:12.660
said yes.

01:31:12.660 --> 01:31:16.700
This is, they're asking Americans.

01:31:16.700 --> 01:31:19.460
This is not, they're not asking anti-boxers.

01:31:19.460 --> 01:31:24.260
They're asking Americans, 24% knew someone who died from the vaccine.

01:31:24.260 --> 01:31:26.940
That is unprecedented.

01:31:26.940 --> 01:31:29.900
Data transparency is key to reform.

01:31:29.900 --> 01:31:33.820
All we need is one place, a state or a country.

01:31:34.820 --> 01:31:39.660
Holy cow.

01:31:39.660 --> 01:31:41.860
What the heck is this?

01:31:41.860 --> 01:31:48.180
Or we cut it, or we cut it like the president who believes in data transparency, who fix

01:31:48.180 --> 01:31:49.180
the problems.

01:31:49.180 --> 01:31:50.180
That's a left turn.

01:31:50.180 --> 01:31:52.740
With the corruption of the CDC and FDA.

01:31:52.740 --> 01:31:59.340
We will instantly reduce SIDS, autism, autoimmune diseases, heart disease, food allergies,

01:31:59.340 --> 01:32:02.340
doctors, and more.

01:32:02.340 --> 01:32:06.420
As you know that if you look at overnight autism where the kid is normal one day and

01:32:06.420 --> 01:32:12.340
just becomes autistic instantly, in a 24-hour period, goes from normal to autistic, severely

01:32:12.340 --> 01:32:18.780
autistic, in 24 hours, there are no cases that happen two weeks prior to a scheduled

01:32:18.780 --> 01:32:20.460
vaccine visits.

01:32:20.460 --> 01:32:25.700
And I talked with pediatrician Doug Holsted, he had 44 cases happening two weeks after the

01:32:25.700 --> 01:32:26.700
scheduled vaccine.

01:32:26.700 --> 01:32:30.100
It's not related to the vaccine, explain that.

01:32:30.100 --> 01:32:31.860
His closing bid is impressive.

01:32:31.860 --> 01:32:35.220
He repeated everywhere.

01:32:35.220 --> 01:32:36.700
SIDS, same deal.

01:32:36.700 --> 01:32:41.220
Fifty percent happened within 40 hours of the vaccine.

01:32:41.220 --> 01:32:44.420
If that's not causal, I don't know what is.

01:32:44.420 --> 01:32:46.140
It's in the Omaha Police Department records.

01:32:46.140 --> 01:32:48.220
I tried to foyer the information.

01:32:48.220 --> 01:32:49.500
The guy said, no, it's not there.

01:32:49.500 --> 01:32:50.500
I said, yes it is.

01:32:50.500 --> 01:32:53.780
And let me have you talk to the detective who knows it's there.

01:32:53.780 --> 01:33:00.060
He talked to the detective who told me about this, and then he ghosted me.

01:33:00.060 --> 01:33:04.220
They did not comply with the foyer request because they know it's right and they know

01:33:04.220 --> 01:33:09.460
what it would expose the link between SIDS and autism, and they don't want anyone to

01:33:09.460 --> 01:33:17.140
know because if people knew they would be furious, so they keep it hidden and they don't

01:33:17.140 --> 01:33:19.380
comply with the foyer request.

01:33:19.380 --> 01:33:27.980
In 2009, Congress decided it was better if the NIH did not do a study comparing the fully

01:33:27.980 --> 01:33:33.060
unvaccinated with the fully vaccinated to look at health outcomes.

01:33:33.060 --> 01:33:40.620
Congress said the American people do not want to know if the unvaccinated are healthier

01:33:40.620 --> 01:33:46.020
or not healthier than the vaccinated, we'd prefer not to know that.

01:33:46.020 --> 01:33:51.700
And so, HR 3069 in 2009 was killed in committee.

01:33:51.700 --> 01:33:59.900
They basically don't want you to know either they are corrupt.

01:33:59.900 --> 01:34:00.900
Every...

01:34:00.900 --> 01:34:01.900
Wow!

01:34:01.900 --> 01:34:02.900
So...

01:34:02.900 --> 01:34:03.900
So...

01:34:03.900 --> 01:34:04.900
So...

01:34:04.900 --> 01:34:05.900
So...

01:34:05.900 --> 01:34:06.900
So...

01:34:06.900 --> 01:34:07.900
So...

01:34:07.900 --> 01:34:08.900
So...

01:34:08.900 --> 01:34:09.900
So...

01:34:09.900 --> 01:34:10.900
So...

01:34:10.900 --> 01:34:11.900
So...

01:34:11.900 --> 01:34:12.900
So...

01:34:12.900 --> 01:34:13.900
So...

01:34:13.900 --> 01:34:14.900
So...

01:34:14.900 --> 01:34:15.900
So...

01:34:15.900 --> 01:34:16.900
So...

01:34:16.900 --> 01:34:17.900
So...

01:34:17.900 --> 01:34:18.900
So...

01:34:18.900 --> 01:34:19.900
So...

01:34:19.900 --> 01:34:20.900
So...

01:34:20.900 --> 01:34:23.020
They've been the first 37 minutes then.

01:34:23.020 --> 01:34:24.700
And there's a great book!

01:34:24.700 --> 01:34:25.900
Newly-moss Republicans.

01:34:25.900 --> 01:34:28.980
A guy named Robert F Kennedy.

01:34:28.980 --> 01:34:33.720
And Brian Hooker that documents all this that everyone should get.

01:34:33.720 --> 01:34:39.000
There is also a large pediatric practice in the United States, they have been in business

01:34:39.000 --> 01:34:43.540
for over 25 years, they have no autism, they have no ADHD.

01:34:43.540 --> 01:34:47.540
The kids never get sick, the secret.

01:34:47.540 --> 01:34:49.020
They avoid all vaccines.

01:34:49.020 --> 01:34:54.020
They avoid the use of Tylenol if you have a fever and a few other things.

01:34:54.020 --> 01:34:57.020
Now, they have to be hidden.

01:34:57.020 --> 01:34:58.020
I can't even tell you who they are.

01:34:58.020 --> 01:35:03.020
I know who they are, but I can't tell you their names because they would lose their license

01:35:03.020 --> 01:35:09.020
to practice medicine for going against the medical consensus.

01:35:09.020 --> 01:35:17.020
Heart disease, it doesn't affect the fully unvaccinated.

01:35:18.020 --> 01:35:22.020
This is a comparison of heart disease for the fully unvaccinated.

01:35:22.020 --> 01:35:29.020
It's zero versus 48% of Americans on average.

01:35:29.020 --> 01:35:32.020
And I checked the references on this.

01:35:32.020 --> 01:35:38.020
Your cardiologist simply forgot to tell you this.

01:35:38.020 --> 01:35:39.020
Bottom line.

01:35:39.020 --> 01:35:43.020
This is the first time he's ever said that autism is caused by vaccines.

01:35:43.020 --> 01:35:46.020
The first time he said that all vaccines should be gone.

01:35:46.020 --> 01:35:51.020
It's the first time he's ever pushed CHD like this.

01:35:51.020 --> 01:35:54.020
So now the question is, how are they going to respond to this presentation?

01:35:54.020 --> 01:35:57.020
They'll probably ignore it.

01:35:57.020 --> 01:35:59.020
Be silenced.

01:35:59.020 --> 01:36:04.020
If they are forced to, they will say we do not respond to external analyses.

01:36:04.020 --> 01:36:06.020
Period, end of story.

01:36:06.020 --> 01:36:07.020
Thank you very much.

01:36:07.020 --> 01:36:11.020
In fact, checker says, you know, you're wrong.

01:36:12.020 --> 01:36:15.020
Or it isn't published in a peer reviewed journal.

01:36:15.020 --> 01:36:16.020
We don't have to respond to it.

01:36:16.020 --> 01:36:17.020
You can't make us look.

01:36:17.020 --> 01:36:21.020
You can't make us look.

01:36:21.020 --> 01:36:27.020
And if it does get published, what happens is the paper magically gets retracted.

01:36:27.020 --> 01:36:31.020
They'll find a reason to retract it.

01:36:31.020 --> 01:36:32.020
Guarantee.

01:36:32.020 --> 01:36:34.020
That's how science works nowadays.

01:36:34.020 --> 01:36:40.020
Or they'll say, hey, the CC loves to do this.

01:36:40.020 --> 01:36:41.020
Things are safe.

01:36:41.020 --> 01:36:43.620
We've given billions of doses and nobody has died.

01:36:43.620 --> 01:36:46.060
You can trust us.

01:36:46.060 --> 01:36:49.740
And the fine print says, even though we've never done the proper autopsy's on anything

01:36:49.740 --> 01:36:53.580
I expect this, and we never did the time series cohort analysis ourselves on the record level

01:36:53.580 --> 01:36:54.580
data.

01:36:54.580 --> 01:37:01.100
So we don't actually really know if the vaccines are safe or not to be perfectly honest.

01:37:01.100 --> 01:37:03.740
But they don't, that's in the fine print.

01:37:03.740 --> 01:37:06.100
The part in yellow is in the fine print.

01:37:06.100 --> 01:37:08.860
You just see the part in white.

01:37:08.860 --> 01:37:09.860
Got it?

01:37:09.860 --> 01:37:10.860
So fine plots.

01:37:10.860 --> 01:37:16.660
Hiding public health data always leads to worse health outcomes.

01:37:16.660 --> 01:37:19.100
So why are we doing it?

01:37:19.100 --> 01:37:20.380
Stop hiding the data.

01:37:20.380 --> 01:37:22.380
Set the data free.

01:37:22.380 --> 01:37:28.580
That, you know, if you've got one thing from this presentation, stop hiding the data.

01:37:28.580 --> 01:37:31.460
Set the data free.

01:37:31.460 --> 01:37:33.300
We want to see the data.

01:37:33.300 --> 01:37:36.020
We want to see it now.

01:37:36.020 --> 01:37:38.580
This slide deck is posted on my sub stack.

01:37:38.580 --> 01:37:51.300
And I want to thank you to all of the paid subscribers that have my sub stack.

01:37:51.300 --> 01:37:53.100
Because you enable me to do this work.

01:37:53.100 --> 01:37:56.860
Otherwise I have to get a real job doing something and not speak out about the vaccine.

01:37:56.860 --> 01:38:02.340
So what's that is the dumbest thing I've ever heard to the, I thought you invented the

01:38:02.340 --> 01:38:03.340
optical mouse.

01:38:03.420 --> 01:38:06.140
I thought you sold two companies for a billion dollars.

01:38:06.140 --> 01:38:07.140
You don't need a job.

01:38:07.140 --> 01:38:14.500
It enables me to fund my work on other people and I fund multiple people and organizations

01:38:14.500 --> 01:38:16.300
to get this work out.

01:38:16.300 --> 01:38:21.140
You can contact me on Twitter.

01:38:21.140 --> 01:38:24.940
I'm SD Kirsch on, sorry, it's X now.

01:38:24.940 --> 01:38:30.620
SD Kirsch on X and there's a, on the pinned tweet.

01:38:30.620 --> 01:38:31.620
It shows how to contact me.

01:38:31.620 --> 01:38:32.620
Darn it.

01:38:32.700 --> 01:38:35.220
And there's even a proposed community.

01:38:35.220 --> 01:38:37.700
He doesn't need anybody's money on that.

01:38:37.700 --> 01:38:41.580
I mean, they're really trying to like, Mr. Kirsch is a mister, no misinformation spreader

01:38:41.580 --> 01:38:47.900
and it doesn't say that, you know, and, or you can subscribe to my sub stack at Kirsch

01:38:47.900 --> 01:38:51.540
sub stack.com and that is the last slide.

01:38:51.540 --> 01:38:53.540
So thank you very much.

01:38:53.540 --> 01:39:01.620
Oh, yeah, yeah, right, standing ovation.

01:39:01.620 --> 01:39:16.660
Where's Robert Malone?

01:39:16.660 --> 01:39:18.780
I mean, it's nice that everybody's excited about it.

01:39:18.780 --> 01:39:23.540
I do really think it's great that people are waking up and seeing that there's been damage

01:39:23.540 --> 01:39:25.900
done.

01:39:25.900 --> 01:39:28.420
I think it's really important.

01:39:28.420 --> 01:39:36.020
But I also think that I also think, thank you so much.

01:39:36.020 --> 01:39:37.820
So we have time for questions, right?

01:39:37.820 --> 01:39:39.220
Oh, okay, we're going to wait.

01:39:39.220 --> 01:39:41.220
I'll do that.

01:39:41.220 --> 01:39:50.540
We have the room till nine, so, okay, oh, hey, that's Randall Bach.

01:39:50.540 --> 01:39:51.540
That's Randall Bach.

01:39:51.540 --> 01:39:52.540
It's so fun.

01:39:52.540 --> 01:39:53.540
So, so thank you.

01:39:53.540 --> 01:39:54.980
My name is Dr. Randy Bach.

01:39:54.980 --> 01:39:56.540
I'm the author of overturning.

01:39:56.540 --> 01:39:57.540
You had him on.

01:39:57.540 --> 01:39:58.540
Which was the last pandemic.

01:39:58.540 --> 01:39:59.540
The pandemic never was.

01:39:59.540 --> 01:40:02.420
So I'm a skeptic kind of guy.

01:40:02.420 --> 01:40:03.420
And you should look into that.

01:40:03.420 --> 01:40:07.940
It's very easy to find Amazon, so a lot of stuff that people think happened with Zika

01:40:07.940 --> 01:40:08.940
causing microcephaly.

01:40:08.940 --> 01:40:09.940
It was a big bruja.

01:40:09.940 --> 01:40:12.980
It was a big huge, biggest pandemic ever until this one came along.

01:40:12.980 --> 01:40:13.980
So it's been eclipsed.

01:40:13.980 --> 01:40:19.740
But anyway, so I'm not, I don't buy into the background data that gets forced on people.

01:40:19.740 --> 01:40:21.500
So I'm with you on that.

01:40:21.500 --> 01:40:26.020
The thing that I don't want to throw the baby out with the bath water, no pun intended.

01:40:26.020 --> 01:40:27.660
These have, have use.

01:40:27.660 --> 01:40:29.820
And there are useful vaccines over time.

01:40:29.820 --> 01:40:31.340
We don't really, I'm a medical doctor.

01:40:31.340 --> 01:40:34.700
I did primary care for 30 something years.

01:40:34.700 --> 01:40:37.460
And we don't really see German measles anymore.

01:40:37.460 --> 01:40:39.900
Rebella is an issue in pregnancy.

01:40:39.900 --> 01:40:40.900
There's Rebella syndrome.

01:40:40.900 --> 01:40:42.140
We don't really see Rebella syndrome anymore.

01:40:42.140 --> 01:40:45.340
So there are vaccines that work and have been useful.

01:40:45.340 --> 01:40:46.900
Smallpox we don't see anymore.

01:40:46.900 --> 01:40:48.940
Anyway, I want to get to the point.

01:40:48.940 --> 01:40:50.300
I understand there's going to be pushback.

01:40:50.300 --> 01:40:55.940
But, but my real point is that, that insofar as arguing at this point, I'm like 90, I

01:40:55.940 --> 01:40:58.620
some percentage agreement with you.

01:40:58.620 --> 01:41:02.220
I'd like to stipulate though that this was not a vaccine rollout.

01:41:02.220 --> 01:41:04.420
You know, a genuine vaccine is prior to an illness.

01:41:04.420 --> 01:41:05.820
This has always been the case.

01:41:05.820 --> 01:41:10.180
I think they made huge factual errors and stipulation errors for whatever reason.

01:41:10.180 --> 01:41:13.780
I think they, you know, it was kind of a forced experiment on people.

01:41:13.780 --> 01:41:17.740
They needn't have done the mRNA platform and they had an adenovirus, a flu shot.

01:41:17.740 --> 01:41:21.660
And there's a lot of other ways in which this was a complete bollock stuff thing.

01:41:21.660 --> 01:41:23.580
They gave it to people who had already had the illness.

01:41:23.580 --> 01:41:28.300
So the, the, the pushback I would give about this vaccine not being safe is that it was

01:41:28.300 --> 01:41:29.300
never done prior.

01:41:29.300 --> 01:41:31.020
It was not done the way vaccines were.

01:41:31.220 --> 01:41:36.300
Dr. Fauci himself 2005 or four, I was talking whatever, you know, severe flu was happening

01:41:36.300 --> 01:41:40.580
that year and he was asked, should people who've had this flu get this flu shot?

01:41:40.860 --> 01:41:44.180
And he was like, no, they should not get this because anybody.

01:41:46.740 --> 01:41:49.620
And anybody who has had the illness has perfect immunity.

01:41:49.620 --> 01:41:51.380
You've seen the entire virus.

01:41:51.820 --> 01:41:53.940
The vaccine is only one little tiny protein.

01:41:54.060 --> 01:41:54.980
You're only going to get anyway.

01:41:55.260 --> 01:41:58.140
So, so thank you, I'll be here all night.

01:41:58.540 --> 01:42:02.700
The last point, or I have a number of other points.

01:42:02.700 --> 01:42:05.740
I mean, that you bring out the fact that Omicron came out.

01:42:06.020 --> 01:42:10.300
But basically the problem with this vaccine, a lot of the problem is that there was no real benefit

01:42:10.300 --> 01:42:14.220
because it was not given a 29, the way a normal flu shot would have been.

01:42:14.700 --> 01:42:17.740
And it was given after the fact, and many people had already had the illness.

01:42:17.740 --> 01:42:22.460
So you're working on the problem of having antibody, antibody-dependent enhancement

01:42:22.660 --> 01:42:26.380
and a lot of secondary problems from this shot, which was no longer acting like a vaccine.

01:42:26.380 --> 01:42:27.980
Plus, the virus itself had mutated.

01:42:27.980 --> 01:42:29.020
I got one quick example.

01:42:29.260 --> 01:42:30.220
Wow.

01:42:30.220 --> 01:42:31.420
Come on, Randall.

01:42:31.740 --> 01:42:32.980
The Alec Baldwin, we've got to get him.

01:42:32.980 --> 01:42:35.620
We've got to get him back on the show.

01:42:35.620 --> 01:42:37.220
There's four Baldwin brothers.

01:42:37.540 --> 01:42:39.260
And let's say Alec is the most dangerous.

01:42:39.260 --> 01:42:42.060
He punches paparazzi, shoots people, and so forth.

01:42:42.460 --> 01:42:45.900
And so if he was the ancestral SARS-CoV-2,

01:42:46.140 --> 01:42:48.420
and let's stipulate that is quasi-dangerous.

01:42:48.420 --> 01:42:50.420
I don't think it was that danger from the diamond princess.

01:42:50.500 --> 01:42:54.300
It was never the problem that was, it was foisted upon us to be.

01:42:54.540 --> 01:42:58.620
So, but let's say he's the most dangerous of the Alec Baldwin brother, of the Baldwin brothers.

01:42:59.140 --> 01:43:02.260
And so the police, at your party, you're not going to let this son of a bitch get in.

01:43:02.500 --> 01:43:03.980
So they all get pictures of Alec Baldwin.

01:43:03.980 --> 01:43:05.220
That's the type of immunity.

01:43:05.220 --> 01:43:06.180
They're your immune system.

01:43:06.260 --> 01:43:07.540
They're not going to let Alec Baldwin.

01:43:07.740 --> 01:43:08.740
So they're pretty good.

01:43:09.060 --> 01:43:12.580
If you haven't prior to the fact before he shows up at your party, he's going to shoot somebody.

01:43:13.300 --> 01:43:16.460
But in this case, Alec Baldwin's already been put in jail.

01:43:16.460 --> 01:43:17.540
He's already all left the scene.

01:43:17.540 --> 01:43:20.260
SARS-CoV-2 has left the scene.

01:43:20.460 --> 01:43:24.140
And now you're left with the Daniel, Billy, Stephen, Baldwin's.

01:43:24.340 --> 01:43:27.300
And those are the alpha, beta, delta variants and so forth.

01:43:27.420 --> 01:43:32.860
And they are substantially less dangerous than, but they could still punch a paparazzi, I guess.

01:43:33.220 --> 01:43:37.980
You know, and so your guards, some will let some of the Baldwin brothers in.

01:43:37.980 --> 01:43:39.100
Some of them.

01:43:39.100 --> 01:43:42.260
So this was always an imperfect experiment as far as the vaccine go.

01:43:42.260 --> 01:43:44.820
And yeah, I'll get to the first part.

01:43:45.020 --> 01:43:49.620
So there's no real point in giving this after the fact because Baldwin's already left the scene.

01:43:49.700 --> 01:43:52.460
And at this point, when Omicron showed up, it's really Haley Baldwin.

01:43:52.660 --> 01:43:54.260
She's not a danger.

01:43:54.460 --> 01:43:57.340
OK, so I'm with you for the most part.

01:43:57.380 --> 01:44:01.900
I think this was only risk and there was no benefit because they gave it to the wrong age groups.

01:44:02.100 --> 01:44:06.420
If they had so the thing I would push back as far as vaccine efficacy, this vaccine.

01:44:08.340 --> 01:44:12.220
I think they should have just given it to people at risk from coronavirus, which was elderly.

01:44:12.340 --> 01:44:13.620
And that's that.

01:44:13.820 --> 01:44:16.500
But the end killed all the elderly there, especially.

01:44:16.700 --> 01:44:19.500
Can you tell me where I said I've looked at the record level.

01:44:19.700 --> 01:44:22.460
Oh, my gosh, the gold standard.

01:44:22.660 --> 01:44:24.260
There's nothing better than that.

01:44:24.460 --> 01:44:28.980
There is no more better gold standard evidence, ground zero level.

01:44:29.180 --> 01:44:31.340
Randy, you should give it to the elderly.

01:44:31.540 --> 01:44:32.860
Randy, stop.

01:44:33.060 --> 01:44:37.020
Again, I agree with most of what you say, because this was given anachronistically.

01:44:37.220 --> 01:44:40.620
This was given this vaccine was given after SARS-CoV-2 had left the scene.

01:44:40.780 --> 01:44:43.740
Look, you just said that you should give it to the elderly.

01:44:43.940 --> 01:44:47.820
I'm asking for your evidence data, the evidence.

01:44:48.020 --> 01:44:53.060
Oh, man, no one is that vaccines of this type, like the flu shot, would be beneficial.

01:44:53.260 --> 01:44:55.700
Is Randy going to play the bad guy here?

01:44:55.900 --> 01:44:58.500
No, no, let me explain what evidence is.

01:44:58.700 --> 01:45:00.700
Oh, no, Randy's playing the bad guy here.

01:45:00.900 --> 01:45:03.100
If you have record level data, I want to see it.

01:45:03.300 --> 01:45:06.060
If you don't have record level data,

01:45:06.260 --> 01:45:09.700
do you have record level data that shows that it's beneficial for the elderly?

01:45:09.860 --> 01:45:11.700
I stipulated that I'm 90 percent

01:45:11.900 --> 01:45:15.140
in agreeing with you because this is a flawed experiment on human beings.

01:45:15.340 --> 01:45:16.980
I'm with you most of the way.

01:45:17.180 --> 01:45:19.740
This is a horrible experiment that was done.

01:45:19.940 --> 01:45:21.340
What is happening here?

01:45:21.540 --> 01:45:25.180
OK, all right. Next question.

01:45:25.860 --> 01:45:28.540
Wow, that was really interesting.

01:45:28.740 --> 01:45:31.700
Randy Bach was in the house.

01:45:31.900 --> 01:45:33.660
Steve, first, thank you very much.

01:45:33.860 --> 01:45:35.820
Have you followed the

01:45:35.980 --> 01:45:40.460
speaking of Dr. David Martin to the European Parliament about the origins of COVID?

01:45:40.660 --> 01:45:42.780
The doctor, David Martin.

01:45:42.980 --> 01:45:44.740
You know, I had that queued up for tonight.

01:45:44.940 --> 01:45:46.500
I was going to watch tomorrow.

01:45:46.700 --> 01:45:50.500
The development of the COVID-19 with the US government, USAID.

01:45:50.700 --> 01:45:51.980
Do you follow Dr. David Martin?

01:45:52.180 --> 01:45:53.340
Yes.

01:45:55.540 --> 01:46:00.020
So, yes, and it is troubling to me

01:46:00.220 --> 01:46:05.460
that there is this long nucleotide sequence, which is in the Moderna patent,

01:46:05.540 --> 01:46:07.860
which is also in SARS-CoV-2.

01:46:08.060 --> 01:46:08.940
Oh, here we go.

01:46:09.140 --> 01:46:09.700
How did it get there?

01:46:09.900 --> 01:46:10.420
Oh, geez.

01:46:10.620 --> 01:46:12.060
Moderna was asked that question.

01:46:12.260 --> 01:46:13.700
This one is really coming up a lot lately.

01:46:13.900 --> 01:46:20.740
How did the sequence, this warm sequence in the patent get into SARS-CoV-2 virus?

01:46:20.940 --> 01:46:21.860
How did that happen?

01:46:22.060 --> 01:46:28.580
And I think the CEO said, let me check on that and I'll get back to you.

01:46:28.780 --> 01:46:30.740
We are still waiting.

01:46:30.940 --> 01:46:32.820
We're still waiting to find out the answer.

01:46:32.820 --> 01:46:38.740
There must be a reason for how this sequence found in the Moderna patent.

01:46:38.940 --> 01:46:41.460
God, it's way into the virus.

01:46:41.660 --> 01:46:43.020
We'll hear about that someday.

01:46:43.220 --> 01:46:47.540
I'm sure maybe in 75 years from now,

01:46:47.740 --> 01:46:50.060
which is the new gold standard that the FDA

01:46:50.260 --> 01:46:53.540
endorses for releasing safety data on a vaccine.

01:46:55.100 --> 01:46:57.100
John.

01:46:58.580 --> 01:47:00.180
Thanks, Steve.

01:47:00.380 --> 01:47:01.300
You talked about data.

01:47:01.340 --> 01:47:02.500
John Bowden.

01:47:02.700 --> 01:47:06.820
There is one state that has probably the most powerful public health data

01:47:07.020 --> 01:47:10.020
transparency bill that is a security guard.

01:47:10.220 --> 01:47:12.460
Jason Gerhard submitted it in New Hampshire.

01:47:12.660 --> 01:47:14.580
It's a security guard.

01:47:16.780 --> 01:47:19.380
Jason Gerhard gets a lot of applause.

01:47:19.580 --> 01:47:21.620
We have some new new Hampshire people here now.

01:47:21.820 --> 01:47:24.500
A few state reps.

01:47:24.700 --> 01:47:26.900
Emily Phillips is a co-sponsor of that bill.

01:47:27.100 --> 01:47:29.700
She's up in the crowd.

01:47:31.300 --> 01:47:41.300
And you may not know Jason Gerhard and Erie Pauls have brought me in to speak

01:47:41.500 --> 01:47:44.980
with the Attorney General of New Hampshire and the Criminal Justice Bureau

01:47:45.180 --> 01:47:47.140
Chief. It was supposed to be 30 minutes.

01:47:47.340 --> 01:47:48.020
It went 45.

01:47:48.220 --> 01:47:49.020
They were still engaged.

01:47:49.220 --> 01:47:51.660
You're in Jason, get up to leave and I'm like, what are you doing?

01:47:51.860 --> 01:47:52.580
We're still talking.

01:47:52.780 --> 01:47:57.580
So it ended with a with a walk out the door after I got him to laugh.

01:47:57.780 --> 01:47:59.500
I got the smiles to drop in their faces.

01:47:59.660 --> 01:48:04.380
My question to you is after you read it,

01:48:04.580 --> 01:48:07.980
I hope you will consider supporting it publicly because again,

01:48:08.180 --> 01:48:13.100
this is probably the greatest public health transparency bill around.

01:48:13.300 --> 01:48:14.780
So I hope you'll support that.

01:48:20.420 --> 01:48:22.180
I have been talking to the people in New Hampshire.

01:48:22.380 --> 01:48:25.460
I just, you know, they said that they want to keep a pretty low profile

01:48:25.660 --> 01:48:27.300
so they don't get torpedoed.

01:48:27.340 --> 01:48:30.740
But yeah, I'm totally, it's absolutely the right thing to do.

01:48:30.940 --> 01:48:36.740
And I'll say another thing about data transparency, which is there is a governor

01:48:36.940 --> 01:48:39.860
in the United States of America

01:48:40.060 --> 01:48:44.780
that I think within the next few months

01:48:44.980 --> 01:48:52.660
will make a bold move and make these record level data public in his state.

01:48:52.860 --> 01:48:54.900
I know who he is.

01:48:58.300 --> 01:48:59.820
I have like no voice.

01:49:00.020 --> 01:49:14.300
Isn't him just sent to ask Steve to discuss how any right in course data shows no evidence of spread.

01:49:14.500 --> 01:49:16.900
I just sent it to John B right now.

01:49:17.100 --> 01:49:21.980
I just call it a vaccine because that's how I could call it the shot.

01:49:22.180 --> 01:49:24.220
We will see where the jab.

01:49:24.420 --> 01:49:25.900
Go ahead, John Bowdo.

01:49:26.060 --> 01:49:27.700
I'm just trying to relate.

01:49:27.900 --> 01:49:32.020
I'm hoping that this I don't want to talk to us.

01:49:32.220 --> 01:49:33.620
I don't want to have an echo chamber.

01:49:33.820 --> 01:49:39.780
I want to I want to talk to people who believe that now he won't take any more questions from John Bowdo.

01:49:39.980 --> 01:49:45.500
And so I'm trying to to to communicate

01:49:45.700 --> 01:49:50.180
in a language that those people understand about what it is.

01:49:50.380 --> 01:49:52.700
So I'm trying to be.

01:49:52.900 --> 01:49:55.100
Come on, John, be give me an answer.

01:49:55.180 --> 01:49:58.380
How about just give me an answer?

01:49:58.580 --> 01:50:01.700
No. Oh, that's all. Yeah.

01:50:03.420 --> 01:50:05.820
He is. Yeah.

01:50:09.540 --> 01:50:14.700
Yeah, you know, I I'd love to ask him a few questions about that.

01:50:14.900 --> 01:50:18.860
But I kind of don't think they're going to let me ask any questions.

01:50:19.060 --> 01:50:21.260
That's kind of the way it works.

01:50:21.460 --> 01:50:24.100
Yeah, Steve, first I want to thank you for this talk today.

01:50:24.100 --> 01:50:28.700
And I just want to thank the MIT students for hosting this.

01:50:32.700 --> 01:50:35.980
So my name is my name is Steve Vitar.

01:50:36.180 --> 01:50:38.180
I'm an electrical and computer engineer.

01:50:38.380 --> 01:50:42.540
I've been teaching at Worcester Polytechnic Institute for over 22 years.

01:50:42.740 --> 01:50:46.380
My red pill moment was when I had a young man sitting in my office asking for

01:50:46.580 --> 01:50:52.620
special accommodations because he instantly had a heart attack to the Pfizer booster

01:50:52.780 --> 01:50:54.980
and then was being treated for myocarditis.

01:50:55.180 --> 01:50:58.420
So that was my red pill moment and that's when I did the deep dive in the same data

01:50:58.620 --> 01:51:03.020
that you showed the adverse event reporting system and I saw all the increases

01:51:03.220 --> 01:51:04.660
and reports on the COVID vaccine.

01:51:04.860 --> 01:51:09.420
I was wondering why isn't the CDC seeing the red flags if I can see them and I'm

01:51:09.620 --> 01:51:11.260
not even trained for that.

01:51:11.460 --> 01:51:14.980
By January of 2022, just a few months later,

01:51:15.180 --> 01:51:20.460
I had seven student deaths, which they attributed to suicide,

01:51:20.580 --> 01:51:22.940
except that I knew one of those young men.

01:51:23.140 --> 01:51:26.260
His name was Tyler Larson from Pelham, New Hampshire.

01:51:26.460 --> 01:51:28.180
He was on crew team.

01:51:28.380 --> 01:51:31.580
He's found dead in his dorm room and it wasn't a suicide.

01:51:31.780 --> 01:51:34.140
So I know those vaccines are harmful,

01:51:34.340 --> 01:51:35.580
especially to that cohort.

01:51:35.780 --> 01:51:39.900
I'm standing here today because I care about these young people and I know the

01:51:40.100 --> 01:51:41.060
vaccine is harmful to them.

01:51:41.260 --> 01:51:44.860
I've been trying to get the word out on my campus.

01:51:45.860 --> 01:51:51.460
And so I applaud the work that you're doing.

01:51:51.660 --> 01:51:57.260
The guy at the Blackboard actually hosts a lot of the VROS and VSRF.

01:51:57.460 --> 01:51:59.660
I guess my question is first team.

01:51:59.860 --> 01:52:03.780
I do believe this has been deliberate attempt by our government and maybe the

01:52:03.980 --> 01:52:06.260
governments of the world to harm people.

01:52:06.460 --> 01:52:07.860
So and it's dangerous.

01:52:08.060 --> 01:52:11.460
We're living in a time where I just cannot believe what our government is doing.

01:52:11.620 --> 01:52:15.860
So what do you think is the most important thing that we should be doing?

01:52:16.060 --> 01:52:18.060
Oh, no, thank you.

01:52:18.260 --> 01:52:19.260
Thank you.

01:52:19.460 --> 01:52:20.460
Yeah.

01:52:26.260 --> 01:52:30.660
You know, I think the thing that's going to shift this is not my giving talks.

01:52:30.860 --> 01:52:31.660
It's people.

01:52:31.860 --> 01:52:36.060
It's when people like Paul Offit wake up in the morning and say,

01:52:36.260 --> 01:52:40.660
what side of history do I want to be on today?

01:52:40.660 --> 01:52:46.860
And it's going to take people like Paul Offit switching from supporting the

01:52:47.060 --> 01:52:53.060
vaccines and saying the vaccines are safe to switching sides and joining us on

01:52:53.260 --> 01:53:00.260
the right side of history and start advocating for truth and transparency.

01:53:00.460 --> 01:53:01.860
And that's when it will shift.

01:53:02.060 --> 01:53:09.860
It will shift when people who people respect and trust switch sides and the

01:53:09.860 --> 01:53:14.860
more people that do that, the faster this will change.

01:53:15.060 --> 01:53:22.060
And I have reached out to Paul Offit to say, Paul, I have record level data.

01:53:22.260 --> 01:53:27.660
Do you want to see the record level data and do you want the tools I used to

01:53:27.860 --> 01:53:31.460
analyze it because you can find the same thing.

01:53:31.660 --> 01:53:36.460
And his response was, I don't want to talk to him.

01:53:36.460 --> 01:53:40.260
And that is the problem.

01:53:40.460 --> 01:53:44.860
None of these people who can make a difference

01:53:45.060 --> 01:53:46.860
want to look at the data.

01:53:47.060 --> 01:53:49.860
They do not want to see the truth.

01:53:50.060 --> 01:53:53.660
And it is going to take a lot of people.

01:53:53.860 --> 01:53:59.060
It's going to take epidemiologists who are not afraid of losing their job

01:53:59.260 --> 01:54:02.660
and destroying their career.

01:54:02.860 --> 01:54:07.460
We're not afraid to do that in order to speak the truth.

01:54:15.260 --> 01:54:15.860
Hi, Steve.

01:54:16.060 --> 01:54:26.060
And I hope that this presentation that I made today will encourage people to say

01:54:26.060 --> 01:54:31.860
John Baldwin is enough not going to take it anymore.

01:54:32.060 --> 01:54:38.260
And I'm going to start speaking out because, you know,

01:54:38.460 --> 01:54:46.260
I asked these AI chatbots, you know, chat GPT, I asked, hey, have there been any

01:54:46.460 --> 01:54:53.460
people who are who become anti-vaxxers that switched over to being pro vaccine

01:54:53.460 --> 01:54:57.460
and the chatbot says, yes, there are plenty of them, like Dell Bigtree.

01:54:57.660 --> 01:55:02.060
And, you know, they've listed people who are like, man, these guys are like

01:55:02.260 --> 01:55:03.860
totally in our camp here.

01:55:04.060 --> 01:55:08.460
And so chat GPT is completely blue-pilled.

01:55:08.660 --> 01:55:10.660
I mean, it's unbelievable.

01:55:10.860 --> 01:55:13.860
Like, I'm not making this stuff up.

01:55:14.060 --> 01:55:22.060
I mean, I posted it on Twitter that the response, but it's going to take people

01:55:22.060 --> 01:55:28.060
to a lot of people to flip sides or pull off it could do it single-handedly.

01:55:28.260 --> 01:55:35.060
If pull off it, single-handedly got red-pilled and he chose to look at the data

01:55:35.260 --> 01:55:37.860
and he realized that, yeah, the jig is up.

01:55:38.060 --> 01:55:42.260
Look, you know, the longer these people refuse to look at the data,

01:55:42.460 --> 01:55:46.860
the more they are discrediting their community.

01:55:47.060 --> 01:55:50.060
The more they discredit the medical community, the medical community now has

01:55:50.060 --> 01:55:54.060
an opportunity to look at this data and analyze it themselves

01:55:54.260 --> 01:55:57.060
and verify everything I said today.

01:55:57.260 --> 01:56:03.060
The longer they avoid doing this, the deeper the hole they will dig themselves

01:56:03.260 --> 01:56:10.060
into and the first rule of holes is when you find yourself in one, stop digging.

01:56:14.060 --> 01:56:16.060
So, hi, Steve.

01:56:16.260 --> 01:56:18.260
I'm over here, right here.

01:56:18.260 --> 01:56:19.260
Go.

01:56:19.460 --> 01:56:24.260
Hi. So, I'm actually Team Reality CT, if anyone knows on Twitter.

01:56:24.460 --> 01:56:26.260
Hello.

01:56:26.460 --> 01:56:31.260
Yeah. So, I am running the National Stop College Mandates.

01:56:31.460 --> 01:56:35.260
This is stopcollegemandates.org.

01:56:35.460 --> 01:56:40.260
I do want to just give credit to Adam and Spencer. I have to tell you.

01:56:40.260 --> 01:56:51.260
We have been at this for two and a half years and I'm telling you, it's single digits

01:56:51.460 --> 01:56:58.260
how many college kids will get involved in this and it's painful and it really needs to happen at that level.

01:56:58.460 --> 01:57:06.260
When we interviewed and did the rally at Yale, we had Naomi Wolf there

01:57:06.260 --> 01:57:13.260
and every single student that we talked to privately, they knew of a child or a peer, I should say,

01:57:13.460 --> 01:57:20.260
that had menstrual issues, strokes, they had, you know, embolisms, it was incredible

01:57:20.460 --> 01:57:25.260
and they still got the booster. They would not buck the system.

01:57:25.460 --> 01:57:31.260
It was absolutely incredible. So, I do want to thank you. I absolutely want to talk to you after this.

01:57:31.260 --> 01:57:38.260
But Steve, I'm really curious about how do we wake up that generation because that's our biggest challenge.

01:57:38.460 --> 01:57:45.260
I think the kids in college, high school, they need to just stand up and say no more.

01:57:45.460 --> 01:57:52.260
There's 74 colleges that are still mandating in our country. This is absolutely an abomination.

01:57:52.460 --> 01:57:58.460
I mean, it's criminal at this point. We need to build a case for criminal and actually sue.

01:57:58.460 --> 01:58:02.460
So, I'm hoping some people will go to our website and sign our petition.

01:58:02.660 --> 01:58:09.460
There's also a student declaration. You should know about this. It's student declaration 2023.org.

01:58:09.660 --> 01:58:14.460
Okay. If you can sign that, that would be wonderful. I appreciate your time and everything you're doing.

01:58:14.660 --> 01:58:27.460
Thank you very much. Thank you for everything you're doing to get rid of the mandates on campus.

01:58:27.460 --> 01:58:36.460
It is going to be a long battle. I think the only way that the needle shifts is for, again, the people like Paul Offit or Peter Hotez.

01:58:36.660 --> 01:58:43.460
And the needle shifts when kids take one year off the university. Come on. This is a joke. This pisses me off.

01:58:43.660 --> 01:58:51.460
I told everybody not to go back to school in 21. Just stay home for a year for these universities.

01:58:51.460 --> 01:58:57.460
It's ridiculous. How much longer do we would have had parents stand up at this time and they could have stood up and just said,

01:58:57.660 --> 01:59:02.460
I'm keeping my kid home for a year and he's going to get a job. It would have been all over.

01:59:02.660 --> 01:59:06.460
This would have been a non-sequitur.

01:59:06.660 --> 01:59:13.460
And they would have been out of the Wake University. It's that simple because this is not sustainable what they're doing.

01:59:13.460 --> 01:59:23.460
They're living on short time views right now. And the longer they delay, the worse they look and the more they discredit their profession.

01:59:23.660 --> 01:59:37.460
Hi. I wanted to point out that the CDC is regionalizing health boards in the state of Massachusetts

01:59:37.460 --> 01:59:45.460
so that they can actually circumvent local boards of health that are elected and appointed officials of a town.

01:59:45.660 --> 01:59:55.460
So it's done with a grant. And the grant is like, oh, well, we'll give you this money and you can do a regionalization.

01:59:55.460 --> 02:00:03.460
The town like Halifax is being grouped with and in big cities and not even the towns that are like Halifax.

02:00:03.660 --> 02:00:14.460
And it's all to have the CDC rules. So when you ask why this is occurring, it's because under the cover of grants such as the Federal Cares Act,

02:00:14.660 --> 02:00:23.460
which allows the lowering of quorums at town meetings, and it's all done because we don't go to town meeting.

02:00:23.460 --> 02:00:28.460
I don't know how much this thing is worth, but that's an expensive rig right there.

02:00:28.660 --> 02:00:33.460
That's probably more expensive than all of the equipment that I have around me.

02:00:33.660 --> 02:00:39.460
It's Mike's from Craigslist. And that's not from Craigslist.

02:00:39.660 --> 02:00:47.460
Why do your taxes go up? Because you didn't go to town meeting and vote. So when you say they are doing this,

02:00:47.460 --> 02:00:52.460
no, you are. Get up and go vote at your town meeting.

02:00:53.460 --> 02:00:54.460
Thank you.

02:01:02.460 --> 02:01:04.460
Yeah, that's an expensive camera rig there.

02:01:05.460 --> 02:01:11.460
So as a graduate of this institution, I greatly respect the reviewed literature.

02:01:11.460 --> 02:01:15.460
And you mentioned the Cleveland Clinic study, which is fantastic.

02:01:15.660 --> 02:01:20.460
And then you probably know the one in vaccine from the FOIA data from Pfizer.

02:01:21.460 --> 02:01:27.460
Is there any effort to take your data and have it make its way through the peer reviewed literature?

02:01:28.460 --> 02:01:36.460
Yes, so we're writing up my results in a paper. So I'm a co-author on that.

02:01:36.660 --> 02:01:39.460
It's being written by someone who works for me.

02:01:39.460 --> 02:01:45.460
So I don't have to worry that it's not going to happen. And it's been reviewed by an editor of the peer review journal

02:01:45.660 --> 02:01:51.460
and he wants to publish it. And so we're just fine tuning it right now.

02:01:52.460 --> 02:01:53.460
Interesting.

02:01:53.660 --> 02:01:58.460
So they won't be able to say, well, we'll look at it when it's in the peer reviewed literature.

02:01:58.660 --> 02:02:03.460
Because that time is coming real soon now.

02:02:03.660 --> 02:02:05.460
And there's no way they're going to be able to stop it either.

02:02:05.460 --> 02:02:07.460
I wonder where I was going to go.

02:02:07.660 --> 02:02:13.460
Hi, Steve. I really appreciate this, of course. Great to see it all in one place and put together as you've done.

02:02:14.460 --> 02:02:18.460
I'm curious whether you have any other targets for your debate people.

02:02:21.460 --> 02:02:23.460
What do you mean by targets?

02:02:24.460 --> 02:02:28.460
You want to challenge to a debate and you offer you the million dollars or whatever.

02:02:29.460 --> 02:02:31.460
Is there anyone left that you're looking at?

02:02:32.460 --> 02:02:39.460
You know, I wish because I'm kind of looking, I could always use an extra million or more.

02:02:40.460 --> 02:02:47.460
So I'm looking to bet people, I offered a million dollars to anybody in the world who would take my million dollars.

02:02:48.460 --> 02:02:51.460
And the bet is I bet the vaccines killed more people than they saved.

02:02:52.460 --> 02:02:54.460
Now you know why I made the bet.

02:02:55.460 --> 02:02:59.460
And it's a neutral panel of judges that decides that there's only one guy in the world

02:03:00.460 --> 02:03:04.460
who accepted it and he said, I'm only going to do it for half a million.

02:03:05.460 --> 02:03:07.460
But if I like the judges, I'll go higher.

02:03:09.460 --> 02:03:13.460
So there is one guy from Israel who accepted my million dollar bet.

02:03:14.460 --> 02:03:20.460
So the interesting thing is that they're willing to bet your life that they are correct,

02:03:20.460 --> 02:03:24.460
but they're not willing to bet their money that they are correct.

02:03:25.460 --> 02:03:26.460
Isn't that interesting?

02:03:27.460 --> 02:03:29.460
I don't know, I don't know, you know, it's like big hat, no cattle, right?

02:03:30.460 --> 02:03:36.460
They talk big and they're willing to risk your life over it, but they're not willing to risk their money.

02:03:37.460 --> 02:03:40.460
Now Moderna won't risk their money and Pfizer won't risk their money.

02:03:40.460 --> 02:03:43.460
They're not standing behind their product.

02:03:44.460 --> 02:03:50.460
Why if the vaccines are safe and effective, why wouldn't Pfizer or Moderna want to challenge me

02:03:51.460 --> 02:03:54.460
and win and prove that their vaccine is safe?

02:03:55.460 --> 02:03:57.460
Because they know it's not.

02:03:58.460 --> 02:04:02.460
I'd like to suggest, Steve, I'd like to suggest a pivot.

02:04:04.460 --> 02:04:16.460
Could you consider perhaps offering that same kind of money to anyone who would be a whistleblower?

02:04:17.460 --> 02:04:20.460
Because what we need is the real, you've got the-

02:04:21.460 --> 02:04:22.460
That's a beautiful point.

02:04:22.460 --> 02:04:23.460
What we need is the intent.

02:04:24.460 --> 02:04:25.460
That's the beautiful point.

02:04:26.460 --> 02:04:28.460
Oh, wow, that's an interesting one.

02:04:29.460 --> 02:04:31.460
I don't think he expected anybody to call them out like this.

02:04:32.460 --> 02:04:35.460
A whistleblower would need support if they were to come forward.

02:04:36.460 --> 02:04:37.460
What do you think, Steve?

02:04:37.460 --> 02:04:38.460
Put your money where your mouth is, Steve?

02:04:39.460 --> 02:04:44.460
Perhaps, and the money would be towards them, their legal fees.

02:04:45.460 --> 02:04:46.460
What's up, Steve?

02:04:46.460 --> 02:04:48.460
Yeah, so I'm totally willing to do that.

02:04:49.460 --> 02:04:51.460
You can use the Contact Me link.

02:04:52.460 --> 02:04:56.460
There is already a checkbox for whistleblowers, and you just name your price.

02:04:59.460 --> 02:05:00.460
Wow.

02:05:02.460 --> 02:05:03.460
This is very-

02:05:03.460 --> 02:05:05.460
CDC would want to do the same thing, right?

02:05:06.460 --> 02:05:07.460
This is very, very interesting.

02:05:08.460 --> 02:05:10.460
They want to find out intent for whistleblowers.

02:05:11.460 --> 02:05:17.460
Isn't there a CDC program where you can show that they fudge the data

02:05:17.460 --> 02:05:18.460
or the fabric?

02:05:19.460 --> 02:05:21.460
Like, where is the CDC program?

02:05:22.460 --> 02:05:25.460
Surely there must be a CDC and FDA program for that.

02:05:26.460 --> 02:05:27.460
Isn't there?

02:05:28.460 --> 02:05:29.460
No way.

02:05:30.460 --> 02:05:31.460
What does that tell you?

02:05:34.460 --> 02:05:35.460
Yeah.

02:05:36.460 --> 02:05:37.460
Why is he filming from behind Steve?

02:05:38.460 --> 02:05:40.460
I guess he gets the audience or something, I don't know.

02:05:41.460 --> 02:05:43.460
It's hard to be a whistleblower nowadays, right?

02:05:44.460 --> 02:05:47.460
You know, there are whistleblower rejection laws, but they don't necessarily work the way they were intended.

02:05:48.460 --> 02:05:50.460
That's because you first have to whistleblower internally.

02:05:51.460 --> 02:05:53.460
And by the way, this whistleblower in New Zealand?

02:05:54.460 --> 02:05:55.460
Sorry.

02:05:55.460 --> 02:06:00.460
The whistleblower in New Zealand, he made an enormous risk.

02:06:02.460 --> 02:06:03.460
No, it's a he.

02:06:04.460 --> 02:06:07.460
He made an enormous risk in disclosing this data.

02:06:08.460 --> 02:06:10.460
He could go to jail, he could be killed.

02:06:11.460 --> 02:06:13.460
There are lots of things that could happen to this guy.

02:06:14.460 --> 02:06:15.460
This guy is a hero.

02:06:16.460 --> 02:06:19.460
And, you know, please give a round of applause for him.

02:06:22.460 --> 02:06:28.460
Because it's people like that that are willing to sacrifice everything to get the truth out

02:06:29.460 --> 02:06:33.460
because they are so disgusted with what is happening

02:06:34.460 --> 02:06:36.460
and how many people have been killed by their government

02:06:37.460 --> 02:06:41.460
that they are willing to sacrifice their life, their future

02:06:42.460 --> 02:06:44.460
in order to get the word out.

02:06:45.460 --> 02:06:48.460
And this whistleblower has my undying gratitude

02:06:49.460 --> 02:06:52.460
and I will absolutely help this whistleblower get another job.

02:06:53.460 --> 02:06:55.460
It's probably not going to be keeping his for very long

02:06:56.460 --> 02:07:02.460
and do anything I can to help him because he has done an incredible service to humanity

02:07:03.460 --> 02:07:05.460
in making this data public.

02:07:07.460 --> 02:07:10.460
So one question, one comment.

02:07:11.460 --> 02:07:16.460
The question is how can the CDC, the FDA, ignore the open veers data?

02:07:17.460 --> 02:07:18.460
It is so compelling.

02:07:19.460 --> 02:07:20.460
That's the question.

02:07:20.460 --> 02:07:22.460
And for those people that believe in vaccines,

02:07:23.460 --> 02:07:26.460
if you want the true story, there's a book called Turtles All the Way Down.

02:07:27.460 --> 02:07:28.460
If you read that book.

02:07:29.460 --> 02:07:30.460
Come on, really.

02:07:31.460 --> 02:07:35.460
You read that book, even polio, you'll question if any vaccine

02:07:35.460 --> 02:07:38.460
that's ever been developed is effective in safe.

02:07:41.460 --> 02:07:43.460
And I totally agree with that.

02:07:44.460 --> 02:07:48.460
There's no evidence that any of these vaccines are safe and effective.

02:07:49.460 --> 02:07:52.460
None of them are tested against placebo for safety

02:07:53.460 --> 02:07:55.460
and as far as effectiveness, the way we find out.

02:07:56.460 --> 02:07:59.460
I will remind you that he did plug Brian's book.

02:08:00.460 --> 02:08:01.460
He did plug Brian's book.

02:08:02.460 --> 02:08:05.460
And so he didn't plug his Turtles All the Way Down book.

02:08:06.460 --> 02:08:07.460
He plugged Brian's book during the talk.

02:08:08.460 --> 02:08:12.460
So even if that guy mentioned it now, Steve, Steve did well.

02:08:13.460 --> 02:08:14.460
How often that?

02:08:15.460 --> 02:08:19.460
I mean, the data presentation was awful and the way that he presented it was awful.

02:08:20.460 --> 02:08:22.460
But the end of the talk, the summary was really strong.

02:08:23.460 --> 02:08:28.460
They had the data and Medicare could be exposed to never, for any vaccine,

02:08:29.460 --> 02:08:31.460
they are never, ever disclosing that information.

02:08:32.460 --> 02:08:33.460
There must be a reason.

02:08:34.460 --> 02:08:35.460
I can't figure it out.

02:08:36.460 --> 02:08:39.460
And as far as the bears, like how can they ignore the bears data, it's easy.

02:08:40.460 --> 02:08:41.460
They just ignore it.

02:08:42.460 --> 02:08:43.460
That's the way it works.

02:08:44.460 --> 02:08:45.460
We're the Federales.

02:08:46.460 --> 02:08:47.460
We don't need no data.

02:08:48.460 --> 02:08:49.460
We don't have no data.

02:08:50.460 --> 02:08:55.460
They actually did look at their data and they found 770 different safety signals.

02:08:56.460 --> 02:08:59.460
That means this adverse event is a huge problem.

02:09:00.460 --> 02:09:03.460
This class of adverse events is a huge problem.

02:09:04.460 --> 02:09:06.460
770 different classes of adverse events.

02:09:08.460 --> 02:09:10.460
That is like the houses on fire.

02:09:11.460 --> 02:09:12.460
What did they do?

02:09:12.460 --> 02:09:13.460
They did nothing.

02:09:14.460 --> 02:09:18.460
The only reason that we know about the 770 safety signals that were triggered.

02:09:19.460 --> 02:09:20.460
And I'm not talking about 770 people.

02:09:21.460 --> 02:09:25.460
I'm talking about pulmonary embolism is one of the safety signals.

02:09:26.460 --> 02:09:28.460
I'm talking about cancer is one of the safety signals.

02:09:29.460 --> 02:09:30.460
That's what I'm talking about.

02:09:31.460 --> 02:09:32.460
I said safety signals 770.

02:09:33.460 --> 02:09:35.460
The buildings on fire, what did they do?

02:09:36.460 --> 02:09:37.460
They did nothing.

02:09:37.460 --> 02:09:43.460
The only way we found out about that is because somebody did a freedom of information act request on the CDC.

02:09:44.460 --> 02:09:49.460
And they discovered that the CDC had the alarm bells are going off throughout the building.

02:09:50.460 --> 02:09:55.460
770 fires throughout the building of the CDC and the CDC did nothing.

02:09:56.460 --> 02:09:57.460
It did absolutely nothing.

02:09:57.460 --> 02:09:58.460
It's in the public record.

02:09:59.460 --> 02:10:00.460
It's a freedom of information act request.

02:10:01.460 --> 02:10:02.460
Anybody can do that.

02:10:03.460 --> 02:10:04.460
They do nothing.

02:10:05.460 --> 02:10:06.460
They are corrupt.

02:10:06.460 --> 02:10:07.460
There is no question about it.

02:10:08.460 --> 02:10:10.460
That organization needs a big overhaul.

02:10:14.460 --> 02:10:16.460
Yes, I don't think so.

02:10:17.460 --> 02:10:18.460
Hi.

02:10:19.460 --> 02:10:20.460
Thank you so much.

02:10:20.460 --> 02:10:24.460
And thank you to over there as well, the young students.

02:10:28.460 --> 02:10:31.460
I want to bring up the word whistleblower.

02:10:33.460 --> 02:10:43.460
It really blows my mind that if everyone stood up, there is no whistleblower.

02:10:44.460 --> 02:10:45.460
It's that everyone stands up.

02:10:46.460 --> 02:10:56.460
And I see it in communities that I'm in and everyone's talking about, you know, the information about the job and how bad it is, but they don't speak up.

02:10:57.460 --> 02:10:58.460
Who's that?

02:10:58.460 --> 02:10:59.460
Is that Jill?

02:10:59.460 --> 02:11:01.460
It's kind of like it's a good day for dying.

02:11:02.460 --> 02:11:03.460
It's like speak up.

02:11:04.460 --> 02:11:07.460
Because if everyone spoke up, then the tables would turn.

02:11:08.460 --> 02:11:16.460
And whether it's about money or not money, it's about if everyone that knows the truth.

02:11:18.460 --> 02:11:23.460
Professors, whomever, if they all stood up, the table would turn.

02:11:24.460 --> 02:11:25.460
Do you know what I mean?

02:11:25.460 --> 02:11:26.460
It's like so simple.

02:11:28.460 --> 02:11:31.460
Yeah, if everybody got together, follow the doctors at UCSF.

02:11:32.460 --> 02:11:36.460
And by the way, none of them, I think none of the doctors at UCSF are getting any more jobs.

02:11:37.460 --> 02:11:38.460
They all know that it kills people.

02:11:39.460 --> 02:11:40.460
They're not going to get themselves.

02:11:41.460 --> 02:11:42.460
But that's what I mean.

02:11:43.460 --> 02:11:49.460
Everyone has their own level of fear, whether it's about money or ridicule or whatever.

02:11:50.460 --> 02:12:00.460
But if the majority of people that knew the truth stood up, they wouldn't lose their jobs because the majority spoke.

02:12:01.460 --> 02:12:12.460
The other little glimmer of light that I would like to also share is people that I know who have constantly gotten the job and the boosters.

02:12:13.460 --> 02:12:14.460
They keep on getting COVID.

02:12:15.460 --> 02:12:20.460
And they're lightening up realizing that they're not going to do that anymore.

02:12:21.460 --> 02:12:23.460
So slowly but surely light is shining.

02:12:24.460 --> 02:12:25.460
But just rise up.

02:12:26.460 --> 02:12:27.460
Come on.

02:12:28.460 --> 02:12:33.460
So it's funny to say that, right? Because we've been saying that for four years, not a few months.

02:12:34.460 --> 02:12:36.460
We've been saying that since 2020.

02:12:37.460 --> 02:12:38.460
Just afraid. I mean, it's 2020.

02:12:39.460 --> 02:12:49.460
This guy was looking for EUA contracts and looking for finding ways to make money off of the pandemic by patenting combination of drugs that were already FDA approved.

02:12:50.460 --> 02:12:52.460
Working closely with Robert Malone during that whole year.

02:12:53.460 --> 02:13:03.460
So it's really ridiculous to say that this guy's a hero when there were people who were speaking up in 2020 and 2021 while he was still trying to cash in.

02:13:04.460 --> 02:13:06.460
Come on, ladies and gentlemen, let's be honest.

02:13:07.460 --> 02:13:17.460
He did very well in this talk, but he should have been given credit to the people who were doing very well the years before he did and the years before the pandemic before I was even aware of this stuff.

02:13:18.460 --> 02:13:21.460
All this stuff public because I think that's that's more important. There was some some commentary.

02:13:22.460 --> 02:13:23.460
Commander.

02:13:23.460 --> 02:13:26.460
And I think the audience needs to reflect on the fact that peer view is what got us here.

02:13:27.460 --> 02:13:28.460
Okay. We saw a surge of fear.

02:13:29.460 --> 02:13:30.460
Oh, he's going to go against peer review.

02:13:31.460 --> 02:13:32.460
Now he's going to go.

02:13:33.460 --> 02:13:34.460
He's going to do his new journal.

02:13:34.460 --> 02:13:37.460
And if that is the cathedral, we're going to run all of our data through.

02:13:37.460 --> 02:13:38.460
We're going to end up in the same place.

02:13:39.460 --> 02:13:40.460
Yeah.

02:13:40.460 --> 02:13:41.460
So I think it's really important.

02:13:41.460 --> 02:13:42.460
New publishing model.

02:13:42.460 --> 02:13:44.460
What do we care about from peer review?

02:13:44.460 --> 02:13:45.460
What we care about is reproduction.

02:13:45.460 --> 02:13:48.460
We don't care about a bunch of our friends giving us gold stars in our papers.

02:13:49.460 --> 02:13:50.460
All right.

02:13:50.460 --> 02:13:51.460
Does it reproduce?

02:13:52.460 --> 02:13:55.460
If your data can be put public and others can reproduce it, that's all we care about.

02:13:56.460 --> 02:14:03.460
I hope people in this audience at MIT, if you're in the Bitcoin club here, you understand this concept that distributed consensus is all that matters.

02:14:03.460 --> 02:14:06.460
If your enemies can reproduce your work, it's real.

02:14:07.460 --> 02:14:11.460
You don't need any any GM telling you it's real because they're probably paid off.

02:14:12.460 --> 02:14:13.460
I used to work in that industry.

02:14:13.460 --> 02:14:19.460
We used to pay even 2008, 10 to $20,000 per page for an ad in science and nature.

02:14:19.460 --> 02:14:20.460
And we bought them every month.

02:14:21.460 --> 02:14:22.460
All right.

02:14:22.460 --> 02:14:27.460
So if you think the actual like Fox is paid off, nature and science are far more paid off.

02:14:28.460 --> 02:14:29.460
Okay.

02:14:29.460 --> 02:14:31.460
They are going to support whatever the pharmaceutical narrative is.

02:14:31.460 --> 02:14:36.460
And if you think you're going to get a fair hearing in that scenario, you're sadly mistaken.

02:14:37.460 --> 02:14:38.460
Okay.

02:14:38.460 --> 02:14:39.460
So put it public.

02:14:39.460 --> 02:14:41.460
Let others interrogate it.

02:14:41.460 --> 02:14:47.460
And I think peer views very important, but it's no longer trustworthy in the hands that we've trusted it with in the past.

02:14:50.460 --> 02:14:52.460
I'll give you a clap for that one.

02:14:52.460 --> 02:14:53.460
That's definitely right.

02:14:53.460 --> 02:14:55.460
But let's also just talk about reduction in science in general.

02:14:55.460 --> 02:14:57.460
I spent some time here on campus.

02:15:02.460 --> 02:15:03.460
So nice.

02:15:03.460 --> 02:15:04.460
All these MIT kids.

02:15:05.460 --> 02:15:13.460
So, so Kevin is actually working on a new project to do exactly what he was talking about.

02:15:13.460 --> 02:15:23.460
And by the way, Kevin McCurnan, for those who don't know him, he came to fame recently because of this little issue of DNA contamination.

02:15:24.460 --> 02:15:27.460
Or as we like to call it adulteration.

02:15:28.460 --> 02:15:41.460
So he found this stuff, including the SV40 promoter sequence in the Pfizer vaccine that is not supposed to be there.

02:15:41.460 --> 02:15:45.460
And not only that, we had a nice little chat on Zoom.

02:15:45.460 --> 02:15:52.460
And I asked a question about, well, shouldn't like, how did this happen?

02:15:52.460 --> 02:15:58.460
And Kevin was saying, well, you know, as soon as you enter the sequence into the, what's the name of that program?

02:15:58.460 --> 02:15:59.460
Yeah.

02:15:59.460 --> 02:16:08.460
As soon as you enter the sequence into snap gene, snap gene says, hey, SV40 promoter sequence.

02:16:08.460 --> 02:16:21.460
And so somebody had to do the active work to erase that sequence from the diagram before it was given to the FDA.

02:16:22.460 --> 02:16:26.460
In other words, they knew there was a problem.

02:16:26.460 --> 02:16:27.460
And I don't think we have.

02:16:27.460 --> 02:16:28.460
We haven't really held it from the FDA.

02:16:28.460 --> 02:16:30.460
I don't think we have the data from the FDA.

02:16:30.460 --> 02:16:35.460
I think we have the EUA submission, the submission rather to the EU, not the FDA.

02:16:35.460 --> 02:16:43.460
It's like I'm waiting for the FDA to give Pfizer the FULAS award.

02:16:43.460 --> 02:16:50.460
You know, for fooling them on the SV40 promoter sequence because the FDA, in any reasonable society,

02:16:50.460 --> 02:16:53.460
the FDA should be livid about this.

02:16:53.460 --> 02:16:55.460
They should call for an investigation.

02:16:55.460 --> 02:16:58.460
And somebody should go to jail for this.

02:16:58.460 --> 02:17:04.460
And what we have is an FDA that says, we're not going to investigate it.

02:17:04.460 --> 02:17:06.460
We don't need to investigate it.

02:17:06.460 --> 02:17:09.460
We don't, you know, we don't have to do anything.

02:17:09.460 --> 02:17:13.460
Safe and effective.

02:17:13.460 --> 02:17:16.460
And that's the way it goes.

02:17:16.460 --> 02:17:24.460
So when Kevin finds this huge safety problem, there is no money from the FDA to fund whether

02:17:24.460 --> 02:17:31.460
or not this thing can permanently rearrange or change your DNA.

02:17:31.460 --> 02:17:35.460
And so the FDA can say, well, there's no evidence that it causes harm.

02:17:35.460 --> 02:17:43.460
Of course, there's no evidence because you decided not to fund the research to uncover the evidence.

02:17:44.460 --> 02:17:47.460
That is how it works.

02:17:47.460 --> 02:17:49.460
That's how the game is played.

02:17:49.460 --> 02:17:51.460
Yeah, John.

02:17:51.460 --> 02:17:53.460
Thank you.

02:17:53.460 --> 02:17:56.460
You guys might know that I'm more on the criminal track.

02:17:56.460 --> 02:17:57.460
Here we go.

02:17:57.460 --> 02:17:58.460
John asked the question.

02:17:58.460 --> 02:17:59.460
Liability.

02:17:59.460 --> 02:18:00.460
Everybody knows about the prep act.

02:18:00.460 --> 02:18:01.460
The Wolfland misconduct.

02:18:01.460 --> 02:18:02.460
He's not going to ask the question.

02:18:02.460 --> 02:18:07.460
What Kevin found is extremely instrumental in proving the case in the court of law because strict

02:18:07.460 --> 02:18:10.460
liability standard is not going to ask any factor in defect.

02:18:10.460 --> 02:18:12.460
Jessica sent him the question too.

02:18:12.460 --> 02:18:16.460
For design defect, you'd have to prove a reasonable alternative design.

02:18:16.460 --> 02:18:20.460
It's a long drawn out process of negligence within court.

02:18:20.460 --> 02:18:26.460
But what Kevin did was unlock the Wolfland misconduct exception of the prep act.

02:18:26.460 --> 02:18:32.460
And then as cases move forward, the manufacturing defect, you don't have to prove a reasonable alternative

02:18:32.460 --> 02:18:33.460
design.

02:18:33.460 --> 02:18:37.460
You only have to prove that the product that was shipped did not meet the safety spec.

02:18:37.460 --> 02:18:39.460
That the manufacturer put out.

02:18:39.460 --> 02:18:41.460
They came up with a spec.

02:18:41.460 --> 02:18:43.460
They're not meeting their own spec.

02:18:43.460 --> 02:18:48.460
So product escapes into the stream of commerce, all standard terms that I'm using.

02:18:48.460 --> 02:18:53.460
So it's just really important that people understand that the game is on.

02:18:53.460 --> 02:18:55.460
And we go after them.

02:18:55.460 --> 02:18:56.460
And then we get.

02:18:56.460 --> 02:18:59.460
And then we tears the corporate bail.

02:18:59.460 --> 02:19:02.460
We go to the executives, take all the money back.

02:19:02.460 --> 02:19:03.460
They knew it should have known.

02:19:03.460 --> 02:19:05.460
Nobody should have made money on these products.

02:19:05.460 --> 02:19:06.460
I go to the gym.

02:19:06.460 --> 02:19:08.460
I see the guys from Moderna.

02:19:08.460 --> 02:19:09.460
They're around here.

02:19:09.460 --> 02:19:12.460
So Norwood's their big manufacturing facility.

02:19:12.460 --> 02:19:15.460
And hey, they're up in parties every Friday.

02:19:15.460 --> 02:19:16.460
The wine and beer are flowing.

02:19:16.460 --> 02:19:18.460
They get free gourmet lunches all the time.

02:19:18.460 --> 02:19:20.460
They live in high on the hog as they kill people.

02:19:20.460 --> 02:19:23.460
So thanks Kevin and thank you Steve for doing this.

02:19:23.460 --> 02:19:28.460
Wow.

02:19:28.460 --> 02:19:30.460
So no question about spread.

02:19:30.460 --> 02:19:33.460
No question about them faking anything.

02:19:33.460 --> 02:19:36.460
Exacerbating the damage.

02:19:36.460 --> 02:19:38.460
We'll take one last question.

02:19:38.460 --> 02:19:39.460
All right.

02:19:39.460 --> 02:19:40.460
Go ahead.

02:19:40.460 --> 02:19:41.460
Wow.

02:19:41.460 --> 02:19:42.460
Wow.

02:19:42.460 --> 02:19:45.460
Thank you very much.

02:19:45.460 --> 02:19:47.460
So this I believe.

02:19:47.460 --> 02:19:50.460
COVID is the tip of the iceberg.

02:19:50.460 --> 02:19:56.460
If we do the data analysis of every vaccine that's ever been given.

02:19:56.460 --> 02:19:57.460
Yes.

02:19:57.460 --> 02:19:58.460
From the beginning.

02:19:58.460 --> 02:19:59.460
We're doing it.

02:19:59.460 --> 02:20:00.460
The death rate.

02:20:00.460 --> 02:20:01.460
The destruction.

02:20:01.460 --> 02:20:03.460
The loss of family wealth.

02:20:03.460 --> 02:20:04.460
We're doing it.

02:20:04.460 --> 02:20:07.460
The loss of livelihoods of people.

02:20:07.460 --> 02:20:08.460
We're doing it.

02:20:08.460 --> 02:20:09.460
Everywhere I go.

02:20:09.460 --> 02:20:12.460
I see more and more and more dysfunctional.

02:20:12.460 --> 02:20:13.460
I'm sorry.

02:20:13.460 --> 02:20:14.460
I don't use that word.

02:20:14.460 --> 02:20:15.460
People.

02:20:15.460 --> 02:20:17.460
I teach in a high school.

02:20:17.460 --> 02:20:20.460
My kids are sick.

02:20:20.460 --> 02:20:22.460
My kids cannot think.

02:20:22.460 --> 02:20:26.460
They are all getting their boosters because they want to play their sports.

02:20:26.460 --> 02:20:27.460
Oh my God.

02:20:27.460 --> 02:20:30.460
If we do the real analysis, which I think your wonderful team could do.

02:20:30.460 --> 02:20:37.460
We will see that this is truly a takedown of the American people and the world people

02:20:37.460 --> 02:20:43.460
by, I believe, a super elite that Professor Quigley refused to name.

02:20:43.460 --> 02:20:44.460
I recognize this guy.

02:20:44.460 --> 02:20:46.460
Because he didn't want to be killed.

02:20:46.460 --> 02:20:48.460
I think I've met him before.

02:20:48.460 --> 02:20:49.460
Who is that?

02:20:49.460 --> 02:20:50.460
Let's look.

02:20:50.460 --> 02:20:51.460
Let's reveal it.

02:20:51.460 --> 02:20:53.460
Let's accept it.

02:20:53.460 --> 02:20:56.460
We are at war.

02:20:57.460 --> 02:21:00.460
I like that she said that.

02:21:00.460 --> 02:21:02.460
I don't think Steve will agree.

02:21:02.460 --> 02:21:03.460
Maybe he will.

02:21:03.460 --> 02:21:06.460
Steve's not going to say anything.

02:21:06.460 --> 02:21:08.460
Well, I hope we can give another big round of applause.

02:21:08.460 --> 02:21:09.460
He's not going to say anything.

02:21:09.460 --> 02:21:10.460
I see.

02:21:10.460 --> 02:21:12.460
He didn't have any response to that.

02:21:12.460 --> 02:21:13.460
Wow.

02:21:13.460 --> 02:21:15.460
That's impressive.

02:21:15.460 --> 02:21:16.460
Okay.

02:21:16.460 --> 02:21:17.460
So.

02:21:17.460 --> 02:21:18.460
That's the show.

02:21:18.460 --> 02:21:25.460
I don't think there's going to be anything.

02:21:25.460 --> 02:21:33.460
I don't think there's going to be anything after that, but I'll let it play.

02:21:33.460 --> 02:21:41.460
I would really like, maybe I'll just listen to the close here.

02:21:41.460 --> 02:21:45.460
Thank you very much for coming over and talking about this.

02:21:45.460 --> 02:21:51.460
Imagine the world, if all this data, if the world knows about these data, if the mainstream

02:21:51.460 --> 02:22:01.460
media, the medical and academia complex did not suppress this, all these data.

02:22:01.460 --> 02:22:02.460
It's a weird.

02:22:02.460 --> 02:22:07.460
On Monday, November 27th, we sent out one of the more popular tweets on Steve's account.

02:22:07.460 --> 02:22:12.460
It was actually the dorm spam in which we emailed 5,000 undergraduates at MIT.

02:22:12.460 --> 02:22:13.460
It's so weird.

02:22:13.460 --> 02:22:14.460
This guy is going to cross the Rubicon.

02:22:14.460 --> 02:22:15.460
Y'all should come.

02:22:15.460 --> 02:22:20.460
Well, I think in every sense of the word tonight, the Rubicon has been crossed.

02:22:20.460 --> 02:22:23.460
What in the world is going on here?

02:22:23.460 --> 02:22:26.460
What is this guy doing?

02:22:26.460 --> 02:22:27.460
He's performing.

02:22:27.460 --> 02:22:32.460
Because tonight we have done something that has not been pierced in a long time.

02:22:32.460 --> 02:22:38.460
We know that the people know what's going on in their skeptical, but the academic and the

02:22:38.460 --> 02:22:41.460
government bubbles remain as resolute as ever.

02:22:41.460 --> 02:22:43.460
The glass could not be pierced.

02:22:43.460 --> 02:22:54.460
Well, as the MSOI tonight, we have just ruptured their glass bubble and we will not be denied.

02:22:54.460 --> 02:22:59.460
Interesting, interesting, interesting.

02:22:59.460 --> 02:23:06.460
248 years ago, 1775, there was the shot heard around the world, which you're all familiar

02:23:06.460 --> 02:23:07.460
with because you're from the region.

02:23:07.460 --> 02:23:10.460
It was 30 miles west of here in Concord, Massachusetts.

02:23:10.460 --> 02:23:15.460
And tonight, I think, in two ways, we have fired the shot heard around the world.

02:23:15.460 --> 02:23:16.460
Oh, man.

02:23:16.460 --> 02:23:17.460
I mean, come on.

02:23:17.460 --> 02:23:18.460
Are you serious?

02:23:18.460 --> 02:23:22.460
We are suggesting the record-level data from New Zealand showing Israel as well and many

02:23:22.460 --> 02:23:23.460
other sources.

02:23:23.460 --> 02:23:25.460
We now have the vindication.

02:23:25.460 --> 02:23:31.460
And in the second way, we as the MSOI, we will continue to rise and we will never back down

02:23:31.460 --> 02:23:33.460
as we have in our previous events.

02:23:33.460 --> 02:23:37.460
Let's keep this going.

02:23:38.460 --> 02:23:45.460
Okay, I think we're going to need your support.

02:23:45.460 --> 02:23:48.460
And I hope that this can be the beginning of a big community.

02:23:48.460 --> 02:23:50.460
No one knows what's going on on the horizon.

02:23:50.460 --> 02:24:06.460
I think I'm just going to leave it here now and slip back over to my notes and to my slide deck that I will end with.

02:24:06.460 --> 02:24:16.460
I find it interesting that Kevin McCurnan was in the audience.

02:24:16.460 --> 02:24:20.460
I find it interesting that John Bodwin was there.

02:24:20.460 --> 02:24:24.460
They both live in Boston, so it's not a crazy thing that they would be there.

02:24:24.460 --> 02:24:27.460
I don't have John Bodwin on my diagram here.

02:24:27.460 --> 02:24:29.460
I should have him here.

02:24:29.460 --> 02:24:31.460
I've got Kevin McCurnan over here.

02:24:31.460 --> 02:24:32.460
He's blocked me on Twitter.

02:24:32.460 --> 02:24:34.460
He doesn't like to discuss things with me anymore.

02:24:34.460 --> 02:24:37.460
He thinks I'm kind of an idiot with regard to molecular biology.

02:24:37.460 --> 02:24:38.460
But that's okay.

02:24:38.460 --> 02:24:42.460
He's the guy who found the DNA and we're very excited about that.

02:24:42.460 --> 02:24:50.460
They are also preserving this narrative of a novel virus and that a million people were killed by it and many more were saved from it.

02:24:50.460 --> 02:24:52.460
And it probably came from getting a function.

02:24:52.460 --> 02:24:57.460
And in fact, you heard nothing about this tonight.

02:24:57.460 --> 02:24:59.460
Nothing about the novel virus.

02:24:59.460 --> 02:25:01.460
Nothing about there being no spread.

02:25:01.460 --> 02:25:12.460
Despite the fact that Steve Kirsch mentioned a number of times Denny Rancor's data and even one time attributed to Robert Malone agreeing with Denny Rancor's data.

02:25:12.460 --> 02:25:14.460
And he did.

02:25:14.460 --> 02:25:25.460
Robert Malone promoted the fact that Denny Rancor's data showed that there might be 17 million people dead from the shot, but ignored, very specifically ignored.

02:25:25.460 --> 02:25:34.460
What I think is the much larger elephant in the room that there's no evidence of spread of a particularly dangerous pathogen in any of the data.

02:25:34.460 --> 02:25:36.460
And I suspect that Steve knows that.

02:25:36.460 --> 02:25:43.460
I suspect that everybody that's promoting Denny Rancor as they are knows exactly what they're doing.

02:25:43.460 --> 02:25:57.460
They're leaving out that very important observation that the virus and deaths did not cross borders of counties in America.

02:25:57.460 --> 02:26:07.460
And so what you saw here was nothing more than a preservation of this narrative that there was a novel virus, millions of people died, millions more were saved, gain a function is real and the virus could come again.

02:26:07.460 --> 02:26:13.460
They preserved that 100%.

02:26:13.460 --> 02:26:24.460
And at the same time, didn't give any credit to the hundreds of thousands of people in the United States who have been on the right side of this issue from before the pandemic.

02:26:24.460 --> 02:26:36.460
And I hope you can see that I try to make that point as often as possible. Being a member of Children's Health Defense is actually a very humbling experience because I don't have any skin in the game.

02:26:36.460 --> 02:26:42.460
I haven't lost a child to autism.

02:26:43.460 --> 02:26:53.460
I haven't, I don't have particularly damaged kids, although, you know, the more I think about it, the more I think some of them might be a little damaged, but maybe that's just my parenting.

02:26:53.460 --> 02:27:08.460
And all jokes aside, I am blessed to have been completely ignorant to this nonsense for my whole life. And I think actually Steve Kirsch was too.

02:27:08.460 --> 02:27:20.460
And so I get a little frustrated and I hope it's okay if I get a little frustrated with you here. I get a little frustrated that this guy has no humility at all, not one drop in any of his boots.

02:27:20.460 --> 02:27:33.460
Not any humility at all for the women and the men who have been fighting for this issue for decade. If not two.

02:27:34.460 --> 02:27:43.460
And it's really important to say that. It really is important to say that because he didn't and they seem to never do it.

02:27:43.460 --> 02:28:00.460
And I hope you will get tired of me humbling myself to these people who have had skin in the game for much longer who have been facing this, this rejection, this crazy label for two decades where I just, I just got it and I'm getting paid.

02:28:00.460 --> 02:28:05.460
So you know, like, how can I even consider myself doing anything?

02:28:05.460 --> 02:28:17.460
How can Steve ask for money for his VRSF and for his sub stack so he can do this work?

02:28:17.460 --> 02:28:25.460
Really? You don't want to raise money for the damaged people? You don't want to raise money for the parents of damaged kids?

02:28:25.460 --> 02:28:33.460
You don't want to raise money to bring awareness to all of the damaged soldiers? You want to raise money for your sub stack?

02:28:33.460 --> 02:28:42.460
You want to raise money for VSRF so you can do what?

02:28:42.460 --> 02:28:51.460
On the day that Bobby announced his candidacy in Boston, Bobby had a $250 a plate dinner.

02:28:51.460 --> 02:29:01.460
And on the exact same night in the exact same town at the exact same time, Steve Kirsch had a $2,500 a plate dinner.

02:29:01.460 --> 02:29:18.460
And yes, he was at the Boston announcement of Bobby's candidacy. So he raised money for his organization on the same night in the same location as Bobby did.

02:29:18.460 --> 02:29:25.460
And so yeah, I do kind of see it as a little strange. I'm not necessarily convinced that he's just pro Bobby.

02:29:26.460 --> 02:29:33.460
There might be people in the world who are pro Bobby having Steve on his team.

02:29:33.460 --> 02:29:40.460
Pro Steve being close to Bobby. Maybe there are people who are happy about that idea.

02:29:40.460 --> 02:29:45.460
And for not reasons that are obvious to us now.

02:29:46.460 --> 02:29:57.460
Steve did very well. He plugged Brian's book, which was really great because when he did his roast the other night and Brian's movie was played.

02:29:57.460 --> 02:30:07.460
And Brian gave a really nice classy roast type video for Steve's three hour fundraiser.

02:30:07.460 --> 02:30:19.460
And after the video was played, there was no thanks Brian. There was no Brian Hooker's great. There was no Brian Hooker's a hero. There was no CHD is awesome. There was just talk about Steve and himself.

02:30:19.460 --> 02:30:35.460
And so I was very impressed that he he plugged Brian's book and plugged it as the tome of all of the papers that the document the dangers of vaccine in the peer peer reviewed literature.

02:30:35.460 --> 02:30:46.460
So kudos for him saying that kudos for him saying that autism is caused by vaccines kudos for him saying these things that needed to be said absent freakin lutely.

02:30:46.460 --> 02:30:49.460
But big strike.

02:30:49.460 --> 02:30:55.460
Big strike against him for not plugging Denny Rancor's data honestly.

02:30:55.460 --> 02:31:10.460
A big strike against him for plugging Denny Rancor's data exactly as Robert Malone did and ignoring the part that matters the most which is there's no evidence of a spreading pathogen.

02:31:10.460 --> 02:31:14.460
And that means that he preserved the faith.

02:31:14.460 --> 02:31:18.460
He walked right around this elephant.

02:31:18.460 --> 02:31:22.460
And so this chess game is continuing.

02:31:22.460 --> 02:31:31.460
And it's annoying because the chess game doesn't seem to end. They keep playing games with these numbers. We had the numbers already in America. You see.

02:31:31.460 --> 02:31:38.460
We have the numbers already in New York City with Jessica. You see, we have the numbers already with John Bodewin. You see.

02:31:38.460 --> 02:31:46.460
But Steve Kirsch never went in front of MIT with John Bodewin's data. He never was going to go in front of it. He didn't use Jessica.

02:31:46.460 --> 02:31:51.460
He didn't really use Denny Rancor's data at all.

02:31:51.460 --> 02:32:00.460
He mentioned Denny Rancor and he mentioned Denny Rancor's number, but he didn't show the data. And if he had showed the data, then it would have been hard to avoid.

02:32:00.460 --> 02:32:11.460
There was no spread because all kinds of other things are correlated with the mass casualty events that were called COVID in 2020 and 2021.

02:32:11.460 --> 02:32:14.460
So that's pretty spectacular.

02:32:14.460 --> 02:32:18.460
And you see what happened here. We saw exactly this.

02:32:18.460 --> 02:32:33.460
An elaborate pulling and pushing on the train. I'm afraid that this is nothing more than that. If we see the data from New Zealand, you're going to see signals that they're going to turn around and say show that lockdown worked.

02:32:34.460 --> 02:32:44.460
And that these extra deaths from the vaccine are just because COVID came to New Zealand two years later because our lockdown was so effective.

02:32:44.460 --> 02:32:48.460
Because there was no cases of COVID on those graphs.

02:32:48.460 --> 02:33:00.460
There were no cases of COVID. There were no diagnostics of COVID. We don't see any waves on there. So we don't know if the waves of death after vaccination occurred with the onset of COVID in New Zealand.

02:33:00.460 --> 02:33:07.460
And we don't know if onset of COVID means onset of protocols.

02:33:07.460 --> 02:33:16.460
We don't know if it means that they stopped using antibiotics on pneumonia on some of those people because it was COVID.

02:33:16.460 --> 02:33:29.460
But what's going to happen is not what we think's going to happen. This train is being faked. This this act is being faked. And I'm afraid that what we just watched is an elaborate trap.

02:33:29.460 --> 02:33:41.460
Draped in very fine clothes, right? I mean, when you say that autism is caused by the vaccines, when you say that the vaccine schedule needs to be, you know, we need the data.

02:33:41.460 --> 02:33:44.460
It feels really good.

02:33:44.460 --> 02:33:50.460
But what was the first 40 minutes was really not what the last 20 minutes was.

02:33:51.460 --> 02:34:03.460
The last 40 minutes seemed like a very elaborate show to distract the mother of all data releases was Denny Rancor's paper.

02:34:03.460 --> 02:34:08.460
The mother of all revelations was Denny Rancor's paper that shows there's no spread.

02:34:08.460 --> 02:34:20.460
The mother of all revelations was was Jonathan Engler and and Jessica Hawkins analysis of or I don't know if she helped with that with the Italian data.

02:34:20.460 --> 02:34:26.460
And it's Jessica Hawkins stuff in in New York City. It's the Panda paper.

02:34:26.460 --> 02:34:32.460
These are the mother of all revelations, not the New Zealand data.

02:34:32.460 --> 02:34:41.460
And if he really had data leaked from the CDC, why didn't we see it? Why didn't he spend more time on it? Why weren't we analyzing it? Why isn't that?

02:34:41.460 --> 02:34:46.460
Wasn't that front and center? Why didn't he use it in the summary?

02:34:46.460 --> 02:34:55.460
Why were there 246 slides but no summary slides with like data summaries and bullet points?

02:34:55.460 --> 02:35:10.460
246 slides with a few words on each slide is not impressive. It's annoying, especially when you go through it really fast with no pointer, no teaching, nothing.

02:35:10.460 --> 02:35:21.460
I'm afraid that was a elaborate train pulling thing that did not question the zoonosis, did not question the batcave virus, did not break the illusion of consensus,

02:35:21.460 --> 02:35:37.460
didn't even talk about lockdowns and EUAs because of course Steve Kirsch was involved intimately with getting EUAs for combinations repurposed drugs all through 20 and 21 with Robert Malone as his primary contact.

02:35:37.460 --> 02:35:50.460
As was explained when they finally came out and decided that they might be kind of need to speak up about something, although they're not really sure what they wanted to speak up about in June of 21.

02:35:50.460 --> 02:35:56.460
But Steve used to say, very sucked.

02:35:56.460 --> 02:36:08.460
This faith in a novel virus is safe with Steve Kirsch that you can assure the faith in a novel virus is safe with Kevin McCurnan. I can assure you that.

02:36:08.460 --> 02:36:15.460
And because this faith is a lie and a spectacular commitment to lie is what is necessary.

02:36:15.460 --> 02:36:23.460
You have to be committed, otherwise you're not going to rise up through this social media landscape. You're not going to be artificially promoted by the algorithms.

02:36:23.460 --> 02:36:32.460
You're not going to be permitted to succeed unless you stay away from questioning the faith.

02:36:33.460 --> 02:36:44.460
And so Denny Rancor is very selectively elevated but used to respect rather than question the faith. That's exactly what you saw happen here.

02:36:44.460 --> 02:36:54.460
And why did you see it happen? Because we are at a time point where they need to invert our sovereignty to permissions and they're doing it as fast as they can.

02:36:54.460 --> 02:37:07.460
They're trying to eliminate the control group and like it or not, a lot of these people are on that team. They're on the team of the ruling class because they want to be part of the ruling class.

02:37:07.460 --> 02:37:16.460
And so if they're given any nuggets about what's going to happen, any nuggets about what the plan is, any nuggets about what's coming to be in the future.

02:37:17.460 --> 02:37:24.460
If it's rearranging publishing Kevin's in the right position, if it's mandating sequencing Kevin's in the right position.

02:37:30.460 --> 02:37:45.460
And if it's if it's we need a new person to come out against the Vax and play a new role that, you know, Bobby Kennedy can't play or we need somebody to come out and make sure that there's mistakes made and that.

02:37:46.460 --> 02:37:56.460
Bobby Kennedy is really associated with an ego maniac. Maybe that's what the plan is. I don't know.

02:37:56.460 --> 02:38:12.460
I'm not necessarily, you know, I can't because I work for CHD. I'm not definitely not endorsing anyone here. And I certainly have mapped it out quite right on the the people map that all of these people are still protecting the faith.

02:38:12.460 --> 02:38:20.460
And that concludes Bobby Kennedy. Bobby Kennedy is still protecting the faith in the novel virus. I don't have that book anywhere now. I don't know where it went. Oh, it's up there.

02:38:20.460 --> 02:38:27.460
But the point is, is that everybody needs to learn this. When the book comes out next week, we're going to start teaching it.

02:38:27.460 --> 02:38:34.460
So thanks for joining me. This has been fun. They are trying to eliminate the control group by any means necessary.

02:38:34.460 --> 02:38:43.460
This has been, you know, biological where we know that intramuscular injection by any combinations of substances with the intent of augmenting the immune system is dumb.

02:38:43.460 --> 02:38:48.460
Transspection and healthy humans is criminally negligent and viruses are not patterned integrity tomorrow.

02:38:48.460 --> 02:38:57.460
I'm going to watch a video with Alex, Zach, and David, our Martin, our David Martin.

02:38:57.460 --> 02:39:14.460
Because I want to finally understand whether David Martin believes there are viruses or not because I know Alex Zach would not let him get away with an hour-long conversation where the guy who didn't believe in viruses, I can't believe Alex Zach would waste an hour of his time talking to somebody like that.

02:39:14.460 --> 02:39:23.460
So I'm really excited to hear that. I've got a very special shout out to Mary Holland, which I was super excited about.

02:39:23.460 --> 02:39:29.460
I've never seen before where she came and went to bat for me, which is just crazy. I love it.

02:39:29.460 --> 02:39:39.460
It was a really interesting question and answer session. So we'll watch that clip and then we'll watch a video with David Martin and Alex Zach tomorrow.

02:39:39.460 --> 02:39:44.460
Thanks for joining me. This has been Gigal, biological. It is a presentation of the independent drive lab.

02:39:44.460 --> 02:40:03.460
I don't know who else is a member, but you can find us when we are struggling to figure out the truth and correcting ourselves when we make mistakes and promoting other people that have the message that we want to get out here and hear something about who is promoting.

02:40:03.460 --> 02:40:06.460
I'm not worried about being given any credit.

02:40:06.460 --> 02:40:19.460
Promote Jessica Hawkins. Promote Mark Coolack. Promote Nick Hudson. Promote Jonathan Engler. Promote Heart. Promote CHD. Promote Broken Science Initiative.

02:40:19.460 --> 02:40:24.460
And if you can, support me at Google and biological.com. Thank you very much, guys.