1 0:00:00 --> 0:00:06 Okay everybody, so anyway, so for those of you who say the USA was the leading nation in the 2 0:00:06 --> 0:00:11 world swimming and aquatic championships called FINA for you French speakers means 3 0:00:12 --> 0:00:20 Fédération Internationale Natation Aquatic, anyway whatever it is. 4 0:00:22 --> 0:00:26 Anyone need to make any announcements while I'm downloading Elvis? 5 0:00:30 --> 0:00:40 Welcome to new, welcome to Virgil visitors. We'll be getting underway in a moment. I'm your moderator 6 0:00:40 --> 0:00:47 wearing my red passion jacket in Melbourne Australia where it's now the 4th of July. 7 0:00:49 --> 0:00:56 We've got people from all over the world, 58 so far Albert, well done. 8 0:00:57 --> 0:01:03 There are some good news on cases but I'll wait for Stephen to come back on that in Australia 9 0:01:04 --> 0:01:12 and elsewhere. David Martin's case is coming up on the 6th of July. We'll talk about that, very 10 0:01:12 --> 0:01:23 important and his interview by Greg Hunter is well worth watching and then revisiting his 11 0:01:24 --> 0:01:32 presentation to us on the 28th of February but he is so strong in talking about the corruption, 12 0:01:32 --> 0:01:41 the criminality of Big Pharma, it's a delight to watch it only goes for 63 minutes. Has anybody 13 0:01:42 --> 0:01:52 else watched that interview? Well worth watching. I can put up onto the screen. 14 0:01:57 --> 0:02:03 My wife and I watched it and it was very depressing. I watched it too, I always watch 15 0:02:04 --> 0:02:13 David Barton's interviews. Excellent, yeah well that's interesting that Mark said it was 16 0:02:13 --> 0:02:18 depressing. I thought it was inspiring because what he said was what I believe to be the case 17 0:02:18 --> 0:02:23 and what many of us here believe that we will win. The only question is how long it's going to take. 18 0:02:24 --> 0:02:29 No I mean depressing when he said that 700 million people will die. 19 0:02:30 --> 0:02:36 Oh yes, yes I suppose I've come to the view that those who refuse to wake up will die. 20 0:02:38 --> 0:02:49 I suppose that's that is certainly my opinion is slightly different. My opinion is these people 21 0:02:49 --> 0:02:57 have been you know propaganda has taken place and they've been sucked into it and what we know 22 0:02:57 --> 0:03:03 people right we've got family members, we've got friends, they just don't believe anything that 23 0:03:03 --> 0:03:15 you say right. It's a closed book, you can't open it. Looking beyond the tragedy of family, friends, 24 0:03:15 --> 0:03:20 neighbors and so on not believing what you say and finding out later that you're right as the 25 0:03:21 --> 0:03:28 deaths accelerate we are facing the greatest infrastructure collapse humanity has ever seen 26 0:03:28 --> 0:03:34 making the Black Death look trivial because only half of Europe died during the Black Death 27 0:03:34 --> 0:03:41 and it took quite a number of years. This is going to be a rapid almost instantaneous 28 0:03:41 --> 0:03:49 collapse of in some places 70 to 95 percent of the population. What happens to infrastructure, 29 0:03:49 --> 0:03:54 what happens to food, what happens to transportation, what happens to the grid, 30 0:03:54 --> 0:04:02 what happens to water, what happens to any aspect of our lives that we depend on other people and 31 0:04:02 --> 0:04:10 systems for and if we who are alert and unjabbed do not start planning for this now we are going 32 0:04:10 --> 0:04:16 to be swept away just like everybody else in the cataclysmic collapse of society and the few 33 0:04:16 --> 0:04:24 remaining will beg the so-called elite to protect them and suddenly Shazam you've got 2030 34 0:04:26 --> 0:04:32 all laid out. Yes come come live in the transit villages you small remaining remnant we'll feed 35 0:04:32 --> 0:04:38 you we'll protect you you'll have your 16 square meters to live in you'll have nothing and you'll 36 0:04:38 --> 0:04:44 be happy. This is all I mean we are not thinking about this we bemoan and I bemoan with us 37 0:04:45 --> 0:04:49 we bemoan the fact that nobody's paying attention to us but what are we doing to plan for the 38 0:04:49 --> 0:04:56 inevitable these people unless we do the research now and find out how to truly detoxify not just 39 0:04:56 --> 0:05:03 give glutathione but get rid of the nanobots get rid of the the god knows what all unless we're 40 0:05:03 --> 0:05:10 doing this research and working on it as a kind of manhattan project deal we're participating in 41 0:05:10 --> 0:05:18 our own destruction. So I remember in the last week well said I've watched a lot of reference to 42 0:05:18 --> 0:05:24 your not early to your video there's a link to your video Albert by the way I haven't got a 43 0:05:24 --> 0:05:31 subsequent email from you. No no no that's okay just download the link that I gave you 44 0:05:31 --> 0:05:38 for the download that's okay if you have that link. Yeah I've got it all ready to go. Okay okay 45 0:05:38 --> 0:05:47 no that's good we're good. So Rima just just you predicted this some how long ago is that video 46 0:05:47 --> 0:05:52 that you put up some 10 years ago? Well that was 2009 with Jesse Ventura a lot of people have seen 47 0:05:52 --> 0:06:00 that but I closed my practice in 2004 my medical practice with my husband because I knew I read 48 0:06:00 --> 0:06:04 their documents it's not hard it's all laid out. 49 0:06:08 --> 0:06:18 Yes yes so there is a question of a question in the chat Rima of you know just give us a couple 50 0:06:18 --> 0:06:27 of minutes on your perspective on that preparation and by the way and by the way you know and by and 51 0:06:27 --> 0:06:33 by the way you know the I've said to this group and there are always a varying population that's 52 0:06:33 --> 0:06:40 what's exciting about the group we never know who's up but we've had an organic farm since 1976 46 53 0:06:40 --> 0:06:45 years of no chemicals on a property 100 kilometers from where I live in the heart of Melbourne. 54 0:06:45 --> 0:06:55 So and the and the value of and the value of good tree change properties in Australia is going 55 0:06:55 --> 0:07:02 through the roof people are moving out of the cities into regional and rural areas so what's 56 0:07:02 --> 0:07:09 your view on what you know for this what people should be doing to prepare as you say? Yes well 57 0:07:10 --> 0:07:16 first of all anybody who isn't growing their own food using indoor or outdoor square foot gardening 58 0:07:16 --> 0:07:24 or larger plots of land or whatever is setting themselves up for some serious problems but 59 0:07:24 --> 0:07:29 beyond that you need water for food most of us can't provide our own water if we live in cities 60 0:07:29 --> 0:07:38 and so on infrastructure is critical to our lives so in my mind the research that we need and you 61 0:07:38 --> 0:07:45 know I've been I've been beating this drum publicly since the beginning actually since before the covid 62 0:07:45 --> 0:07:55 idiocy and and I'm not getting any any response first of all we need to use laboratory facilities 63 0:07:55 --> 0:08:03 to find out what's in people we need to be doing chromatography and spectrophotometry and so on 64 0:08:03 --> 0:08:08 and other fairly simple laboratory evaluations to find out what people are transfecting other people 65 0:08:08 --> 0:08:16 with we know that it's at least the spike protein is it also micro mRNA are people shedding or 66 0:08:16 --> 0:08:27 transfecting others both the jabbed and the unjabbed with the equivalent of smart smart dust in spit 67 0:08:27 --> 0:08:32 I don't know it's entirely possible second of all we need to find out what's in people 68 0:08:33 --> 0:08:40 not what's in the vials we we have some ideas what's in some of the vials but what exactly is 69 0:08:40 --> 0:08:46 in people now that these self-assembling nanobots have been introduced into people 70 0:08:47 --> 0:08:53 what does it look like in the in the blood and the tissues of people a year past their first 71 0:08:55 --> 0:09:00 shot two years past their first shot and then we have to find out what frequencies these nanobots 72 0:09:01 --> 0:09:10 remember just one moment when you say we do you mean not not government bodies us well I don't 73 0:09:10 --> 0:09:15 think this I know of no government body that's looking out for my welfare do you know one for 74 0:09:15 --> 0:09:21 you I'm just asking when you say we all of these resources here we are we are a group of people who 75 0:09:21 --> 0:09:30 are concerned about this we are alert we are alive we are communicating yep that's we excellent 76 0:09:30 --> 0:09:35 good just to get clear you know it's not we're not waiting on on government I'm not looking for a 77 0:09:35 --> 0:09:43 government grant right maybe I could get some money from DARPA no um all right excellent remote 78 0:09:44 --> 0:09:50 yeah that is well that is well said and that is why we're here and so many of us who have been 79 0:09:50 --> 0:09:55 here and Stephen well done for setting up Stephen that's the reason why Stephen set this group up 80 0:09:55 --> 0:10:01 and so but there is I reject the view that no one's doing anything there's a vast amount of 81 0:10:01 --> 0:10:05 stuff happening because not one of us knows everything that is happening you're absolutely 82 0:10:05 --> 0:10:11 correct let me put it this way I am not aware of the results of the kind of research that I have been 83 0:10:12 --> 0:10:19 begging people who have laboratories or who have facilities to I mean look there are pathologists 84 0:10:19 --> 0:10:27 who are examining tissues who are there are people who are beating these drums too I'm 85 0:10:27 --> 0:10:34 certainly not the only one nor are you but what I'm saying is that and and thank you for correcting 86 0:10:34 --> 0:10:44 my passionate mis-speech I am not aware of coordinated communication about these questions 87 0:10:44 --> 0:10:52 do we know other than speculative um uh speculative notions that are being shared 88 0:10:53 --> 0:11:01 how to truly detox people from what's been injected into them I'm I'm not aware I mean I'm 89 0:11:01 --> 0:11:07 I'm a nutritional physician and and Rima we're going to get to Albert you've laid that out 90 0:11:07 --> 0:11:13 beautifully and thank you for that okay we're going to go back and look at Rima's video and I 91 0:11:13 --> 0:11:19 would urge you remember to send it you know the thoughts that any of us have about what would be 92 0:11:19 --> 0:11:24 of value to this group people send emails to Stephen please do that and say hey this is what we 93 0:11:25 --> 0:11:31 Charles I just yeah I just like to say something to Rima um Rima I agree with you that far too 94 0:11:31 --> 0:11:38 many people are having a summer off thinking that life is back to normal it's not back to normal 95 0:11:38 --> 0:11:43 we need to sort this out and hold these people to account and tell them we're coming for them 96 0:11:44 --> 0:11:48 and frighten them put them on the defensive as well because we are coming for them 97 0:11:50 --> 0:11:56 let me let me do this I will put my contact information in here and again forgive me for 98 0:11:56 --> 0:12:04 speaking over passionately I'm watching the world dissolve um I get passionate about it you know 99 0:12:04 --> 0:12:11 this is a massive fucking disaster I mean we all need to fix it but there are lots of people on 100 0:12:11 --> 0:12:16 here who feel the same way yes that's why I'm here that's why you're here right even the people on 101 0:12:16 --> 0:12:23 here can't bear to think of the collapse of the human species so they put it away for a week or 102 0:12:23 --> 0:12:28 two weeks you know let me have my holiday and then I'll do it and then the time comes to do something 103 0:12:28 --> 0:12:33 and they don't do anything okay folks anybody who's done that I don't believe you've done that 104 0:12:33 --> 0:12:39 or you wouldn't be here on a Sunday but in case in case Stephen is right Stefan is right put on 105 0:12:39 --> 0:12:47 your big girl and big boy panties get over it and let's find the solutions together excellent and 106 0:12:47 --> 0:12:54 Anna Anna Mihalcea we've talked about you doing a spiritual presentation we've had Jesse Romero 107 0:12:55 --> 0:13:01 and Anna we need to have that conversation here at one point soon as well um Stephen can you make me 108 0:13:01 --> 0:13:07 a co-host please we'll get into thank you Rima and we'll get into Albert um yeah I'm trying to 109 0:13:07 --> 0:13:14 do it now um so I am signed in now uh you'll be pleased to hear but I don't seem to have all the 110 0:13:14 --> 0:13:26 options when I click on more it should say click on your name and hopefully okay no I've got limited 111 0:13:26 --> 0:13:36 it says spotlight for everyone rename and pin gosh so I've got two extra sorry did you change 112 0:13:36 --> 0:13:43 yeah I've got the password yeah I'm signed in so I don't know what's happened yeah that's very 113 0:13:43 --> 0:13:52 strange yes it says that you're the host does it yeah yes it does how's that then well so I've got 114 0:13:53 --> 0:13:59 so I just go on to my name and I've got mute and more to the right of that and when I click 115 0:14:00 --> 0:14:03 what happens if you click on my name and click more 116 0:14:08 --> 0:14:11 oh I get the full range then good well make me a co-host 117 0:14:13 --> 0:14:21 yes ah maybe yes I was clicking on my name yes um yes I made you the co-host now 118 0:14:21 --> 0:14:30 brilliant well done well done well done okay um Albert so everybody remember love your passion 119 0:14:30 --> 0:14:36 that's why we're here we are passionate that's why I'm wearing red because never never never 120 0:14:37 --> 0:14:45 explain about your passion and there are many big with many solutions here and you know it could be 121 0:14:45 --> 0:14:49 we have a day of so we have a day of solutions but there are people who'd love to hear you speak 122 0:14:49 --> 0:14:56 I know I want to hear you speak on the spiritual on the spiritual battle I've had other information 123 0:14:56 --> 0:15:02 from other sources that we don't have to do this alone Rima so it's a much bigger game than just 124 0:15:02 --> 0:15:10 us human Charles we can't do it alone not one of us there is no way that we can do it alone 125 0:15:10 --> 0:15:18 these collaborations I mean thank god the Chinese government gives us zoom yes the the collaboration 126 0:15:18 --> 0:15:24 system is absolutely essential because otherwise we have no hope but with each other and with our 127 0:15:24 --> 0:15:32 resources and our um uh many kinds of talents then we do have hope but only if we recognize 128 0:15:32 --> 0:15:37 the problem I mean it's like a doctor you have to diagnose the underlying cause this is not how 129 0:15:37 --> 0:15:42 allopathic medicine is done but that's how it should be diagnose the underlying cause and you 130 0:15:42 --> 0:15:46 treat the underlying cause and the symptoms simultaneously well we have to do that 131 0:15:47 --> 0:15:53 and I will have and and we have spiritual forces available to us as well yes anyway 132 0:15:53 --> 0:15:59 I'll run the good news is I think we've after all these meetings we've got a very good idea 133 0:15:59 --> 0:16:08 a good few of us of what's going on and um and what each party's that's affecting the whole thing 134 0:16:09 --> 0:16:19 you know the economics and history um yeah many aspects which I maybe police um military um 135 0:16:20 --> 0:16:24 constitutions we historians we've got historians on the group philosophers 136 0:16:24 --> 0:16:29 psychologists so and we're trying to get the message we're trying to work on the messaging 137 0:16:29 --> 0:16:34 because I don't know what you think but we can talk um if you email me then we can get we can 138 0:16:34 --> 0:16:43 start talking um Stephen Steve is it Stephen or Stefan uh Stephen with a ph Stephen Stephen 139 0:16:43 --> 0:16:55 with a ph.frost as in Jack Frost at BTinternet.com okay I'm gonna put that in the chat okay very good 140 0:16:55 --> 0:17:03 or maybe you you put your you put your um email in the chat I don't want to presume no it's okay 141 0:17:03 --> 0:17:08 I don't mind well everybody's got my email address I think everyone's got Stephen's email 142 0:17:08 --> 0:17:16 someone please put in the chat. Do I? I don't think so. Yes. Oh well no I don't think so but I've put my 143 0:17:16 --> 0:17:25 contact information there. Excellent. Rema do you get an invitation every week twice a week sorry? 144 0:17:25 --> 0:17:33 No. Oh I thought I saw your email address okay if you could send me your email address then that 145 0:17:33 --> 0:17:41 would be great. I will do that. Right. Okay is that your dog Stephen? Yeah that finished sorry about 146 0:17:41 --> 0:17:51 that. Nice all right well everybody we have two and a half hours for our meeting as usual so it's 147 0:17:51 --> 0:17:59 been most enlightening in any event and we've got logistics fixed and we've got Albert ready to go 148 0:17:59 --> 0:18:06 as I say welcome to newcomers and Albert can you give us a bit of background on you and I'll show 149 0:18:06 --> 0:18:14 you I'll get you excited Albert this is what I'm gonna get Albert excited about. Thank you wow thank 150 0:18:14 --> 0:18:19 you thank you can you uh I don't know if you could uh well you can leave it like that or you could 151 0:18:19 --> 0:18:26 put it into oh there you go you put it into presentation mode yeah um yeah so my name 152 0:18:26 --> 0:18:36 my name is Albert Benavides I go by welcome to the eagle and I'm the uh I consider myself one of the 153 0:18:36 --> 0:18:44 best uh VAERS auditors in the world I've been following the VAERS uh database since uh day one 154 0:18:44 --> 0:18:54 of the rollout and uh so with that that's okay who's um it's hard to see all the people on him 155 0:18:54 --> 0:19:02 okay keep going keep going Albert uh yeah um so I called my uh presentation the VAERS uncomfortable 156 0:19:02 --> 0:19:11 treats and with that I just want to say that I think that our American VAERS is the the best um 157 0:19:12 --> 0:19:19 uh adverse event system in the world but it can be better um I I think that they've hijacked it 158 0:19:20 --> 0:19:28 and the people entrusted to take care of the VAERS system are doing anything but um you know but 159 0:19:28 --> 0:19:38 maintaining the system so with that um I want to say my key points are that um uh you know tens of 160 0:19:38 --> 0:19:48 thousands of um reports are are under classified or undercoded meaning that there is some severe 161 0:19:48 --> 0:19:55 adverse events that are basically classified as office visits or none of the above that they have 162 0:19:55 --> 0:20:03 not been um uh correctly uh the boxes haven't been correctly checked off and so they're they're just 163 0:20:03 --> 0:20:11 flushing out as none of the above um also that um with the uh you know with the hot lots and the 164 0:20:11 --> 0:20:20 toxic lots and which I truly believe and agree that there are toxic toxic lots but even with that 165 0:20:20 --> 0:20:30 there are thousands of misclassified uh lot numbers meaning that there is um uh the manufacturer and 166 0:20:30 --> 0:20:37 the lot number are are discombobulated there's thousands of them that tell tell us that there's 167 0:20:37 --> 0:20:43 the manufacturer is Pfizer but they give us the Moderna lot or they or they say the manufacturer 168 0:20:43 --> 0:20:50 is Moderna but they give us the Pfizer lot so if we clean those up I think we would get a better 169 0:20:50 --> 0:20:58 idea an even more crystal clear um visualization or idea of what's real you know what's talked 170 0:20:58 --> 0:21:06 toxic and what's possibly saline or a placebo something like that um so the other thing too is 171 0:21:06 --> 0:21:13 that I want to bring to people's attention if they don't you know know this is that only initial 172 0:21:13 --> 0:21:22 reports are made public even though they continue to capture uh to capture data internally you know 173 0:21:22 --> 0:21:31 follow-up data be it um be it autopsy reports so they say or uh just whatever follow-up data at the 174 0:21:31 --> 0:21:38 at the at the two-month mark at the six-month mark at the at the one-year mark uh they they follow up 175 0:21:38 --> 0:21:45 with these reports but only the initial reports are made public so it actually begs the question 176 0:21:46 --> 0:21:55 of how many people could could now since be dead out of the 1.3 million reports that are in there 177 0:21:55 --> 0:22:03 now I wonder how many people are now since dead uh lastly the last point was that bears I believe 178 0:22:03 --> 0:22:13 that bears throttles reports that these reports do not come to us or are made public organically 179 0:22:13 --> 0:22:21 that that bears themselves actually keep the report they receive the report or I I should say 180 0:22:21 --> 0:22:28 they receive the claim and they have up to four to six weeks to rigorously authenticate the report 181 0:22:28 --> 0:22:35 to make sure that it's not a duplicate to make sure that it's not a false report and with that 182 0:22:35 --> 0:22:43 very comfortable four to six weeks they still hold on to the claim in their position uh possession 183 0:22:43 --> 0:22:50 before they even publish it uh you know to the public uh to the point that now there's um there's 184 0:22:50 --> 0:22:56 quite a few reports that are actually been in their possession for over a year before they actually 185 0:22:56 --> 0:23:07 make it public so uh if you can go to the next slide so here is um an example a side-by-side 186 0:23:07 --> 0:23:14 view of med alerts on the left and the actual CDC wonder system on the right this is brand new data 187 0:23:14 --> 0:23:21 this is live right now this is what it looks like right now and you can see on the right side 188 0:23:22 --> 0:23:32 on the wonder system down at the very bottom it says uh the none of the above 65 nearly 65 percent 189 0:23:32 --> 0:23:40 of the entire database is none of the above so what that is saying that in all of these reports 190 0:23:40 --> 0:23:47 if none of the boxes are checked off that it was not life-threatening or permanent disability or 191 0:23:47 --> 0:23:53 hospitalization if none of those boxes were checked off it would it would simply get default 192 0:23:53 --> 0:24:02 and go into the none of the above not serious now to the casual observer uh it would say wow look 193 0:24:02 --> 0:24:08 how safe and effective this is sure there's 29 000 deaths but half of them come from outside the 194 0:24:08 --> 0:24:16 united states and uh you know 65 percent look like they're not serious none of the above and that's 195 0:24:16 --> 0:24:22 what i want to bring to your attention that this is not true that there are thousands hundreds of 196 0:24:22 --> 0:24:34 thousands of very serious um adverse effects that are basically um in the none of the above 197 0:24:34 --> 0:24:40 so i'm going to show you it's by an evil intelligent design that that that number 198 0:24:40 --> 0:24:46 climbs to be 65 percent of uh none of the above okay uh next slide please 199 0:24:48 --> 0:24:53 okay so here's a good example uh now this is the same the same thing but now by uh 200 0:24:54 --> 0:25:01 the same summary but now by ages you could see that unknown age down at the bottom wow 201 0:25:01 --> 0:25:08 i think that says 26 i can't see it on the bottom of my screen but it's about 28.25 202 0:25:09 --> 0:25:18 there you go 28 wow that's incredible nearly a nearly a third uh you know fourth to a third 203 0:25:18 --> 0:25:27 that are unknown ages wow i mean think of this when they have four to six weeks to to rigorously 204 0:25:27 --> 0:25:34 vet and authenticate a report that came into them don't you think they could have at least like 205 0:25:35 --> 0:25:41 call back and say uh uh hey you know how old was this how old was this patient right they they 206 0:25:41 --> 0:25:47 don't they basically don't do any any they don't offer us that help you know very rare 207 0:25:47 --> 0:25:52 that they call back and say hey you know how old is this person but anyways there you go next next 208 0:25:52 --> 0:26:03 slide okay so so i can tell by this none of the above and this is this is a really curious way of 209 0:26:03 --> 0:26:12 of um of uh spooling this report but on the top there i'm saying that show me everything where 210 0:26:12 --> 0:26:17 none of the boxes are checked so i actually have to go and say that it's not life-threatening that 211 0:26:17 --> 0:26:23 the patient did not die there was no er no office visit no nothing and then i get the results in 212 0:26:23 --> 0:26:31 there it's telling me wow 850 000 reports of that 1.3 are basically saying not serious none of the 213 0:26:32 --> 0:26:47 above the next slide and so um you know this slide a hand here that uh you know this is an example 214 0:26:47 --> 0:26:54 basically of how how there's even deaths in none of the above not serious there's actually deaths 215 0:26:54 --> 0:27:01 there's actually 60 deaths here where i chose the symptom now now only a child would do so do some 216 0:27:01 --> 0:27:07 kind of a a query like this it's kind of weird and obscure but i asked for all the symptoms 217 0:27:07 --> 0:27:14 that had this kind of death brain death cardiac death clinical death all the different flavors 218 0:27:14 --> 0:27:22 of death but i chose that the patient did not die but had those symptoms and lo and behold i got 219 0:27:23 --> 0:27:32 60 deaths 60 people that that um you know follow this their symptom says death but the the box 220 0:27:32 --> 0:27:38 isn't checked off so i do have some sample ids there in red five of them that you can actually 221 0:27:38 --> 0:27:42 go and read for yourself but i'll show you a few here can you go to the next slide please 222 0:27:43 --> 0:27:50 so here's a good example there's just another one for you to ponder here these are all kids 223 0:27:51 --> 0:27:59 with with an age keep in mind there's 26 or 28 percent of the entire um the in the entire 224 0:27:59 --> 0:28:04 database that doesn't have an age that we don't know what the age is but when we do know the age 225 0:28:04 --> 0:28:12 what the age is but when we do know the age here we have uh 2500 kids with chest pain 226 0:28:13 --> 0:28:21 and i just you know draw your eye to the bottom there where it says not serious and recovered 227 0:28:21 --> 0:28:29 um you know i i question that i say you know recovered for now or that any any child that 228 0:28:29 --> 0:28:37 has it gets a vaccine and has chest pain as we know now about this this jab you say wow that 229 0:28:37 --> 0:28:43 that's probably i want to consider that kind of serious at least you know not to say that it's 230 0:28:43 --> 0:28:51 not serious but um so there's you know that that's kind of uh revealing there 2500 okay next slide 231 0:28:51 --> 0:29:02 please okay this is interesting there are 404 cases where the age is unknown 232 0:29:04 --> 0:29:12 so there's no age populated there in the age field but yet their symptom is drug administered 233 0:29:12 --> 0:29:22 to inappropriate age so that inappropriate age is you're not too old to get the to get the vax 234 0:29:22 --> 0:29:29 to get the to get the jab you're only too young to get the jab so there you go that's these are 235 0:29:29 --> 0:29:37 underage kids at the time of their at the time of their shot they they weren't of of appropriate 236 0:29:37 --> 0:29:45 age and there you have eight deaths um you know uh eight permanent disabilities these are the ones 237 0:29:45 --> 0:29:51 you know these are hiding because they don't come out when people you know the casual observer 238 0:29:51 --> 0:30:01 spools a report in med alerts or open bears or uh the cdc wonder system and say uh you know show 239 0:30:01 --> 0:30:07 show me all the deaths for all the kids because when you choose the age these ones aren't going 240 0:30:07 --> 0:30:13 to pop up because they're in unknown age so you know tack on eight more deaths for kids 241 0:30:14 --> 0:30:18 they're hiding right here there's there's here's 404 of them okay next slide 242 0:30:22 --> 0:30:28 okay here here's an example of how this inappropriate age what what the report may look 243 0:30:28 --> 0:30:34 like and this is how the report looks like in med alerts but it says that um you know inappropriate 244 0:30:34 --> 0:30:43 age is technically an administration error and the fine print in the various system is to say 245 0:30:43 --> 0:30:53 all administration errors like storage um leaky syringes syringes that break uh temperature 246 0:30:53 --> 0:30:58 excursions expirations all of those are considered administration errors and need to be 247 0:30:59 --> 0:31:09 filed into bears like religiously asap so that's why that inappropriate age it's like even a mistake 248 0:31:10 --> 0:31:17 in this bizarro world of uh you know how they how this is set up even when they make a mistake 249 0:31:17 --> 0:31:22 and jab somebody underage it's almost like they get credit for it because it flushes out in the 250 0:31:22 --> 0:31:34 not serious none of the above so uh you know and they deliberately by design they leave the age 251 0:31:34 --> 0:31:40 field blank much much of the time i don't want to say all of the time but it seems like wow every 252 0:31:40 --> 0:31:46 time they do this inappropriate age a majority of the time they do not populate the actual age so 253 0:31:46 --> 0:31:53 it falls into the none of the above or the unknown age and you can't you know you can't see it okay 254 0:31:53 --> 0:32:02 next slide okay so we're i'm just going to flash this really fast i know it's small but at least 255 0:32:02 --> 0:32:08 it's on the screen and we can go back on replay mode and you could read read these things but 256 0:32:08 --> 0:32:13 all of these are basically inappropriate age but they don't have the you know the the age populated 257 0:32:13 --> 0:32:21 and even that one on the left is a bundle the lower left four patients four kids on one report 258 0:32:21 --> 0:32:30 that's a whole nother that's a whole nother uh uh topic of bundling multiple patients on one report 259 0:32:31 --> 0:32:33 uh okay next uh slide 260 0:32:36 --> 0:32:43 okay again so all of these are inappropriate ages when you look close you're like i'm like 261 0:32:43 --> 0:32:50 wow these are horrible side effects and they're coming out as as um not serious none of the above 262 0:32:50 --> 0:32:59 because none of the event level boxes are checked off and when they're not checked off they instantly 263 0:32:59 --> 0:33:08 go to uh no they default to no and then therefore default to none of the above not serious so there's 264 0:33:08 --> 0:33:12 four of them right there uh next page uh next slide 265 0:33:15 --> 0:33:23 you know here's one you know i i just wanted to give it its own slide a child a baby paralyzed 266 0:33:23 --> 0:33:30 okay this one notice the age it doesn't tell me anything the clue was inappropriate age and the 267 0:33:30 --> 0:33:37 symptom and then it doesn't say this is a six month old this is a you know whatever it just says baby 268 0:33:37 --> 0:33:43 paralyzed and there's a lot of reports that i have to pull out that i have to actually read and it 269 0:33:43 --> 0:33:52 says an adolescent a neonate a teenager and it's like oh man how do i classify i can't put the the 270 0:33:52 --> 0:33:59 number in the age field so in my own dashboard i'll at least populate it like that say adolescent 271 0:33:59 --> 0:34:08 teenager child so at least i'll know that it was a child report okay next page 272 0:34:12 --> 0:34:19 so here's another example these are these are uh you know the inquisitive child in me right here 273 0:34:19 --> 0:34:25 so these are reports uh starting with the one on the left and i think that's my um that's my 274 0:34:25 --> 0:34:32 embolism my embolisms so i asked for um all of the symptoms that had this embolism 275 0:34:33 --> 0:34:40 but then i said okay now but but but they did not have they were not serious none of the above so 276 0:34:40 --> 0:34:44 they they weren't life-threatening they didn't have an office visit didn't have emergency so 277 0:34:44 --> 0:34:50 that's where i show the second bottom half of the of the query where it's not any of these fields 278 0:34:51 --> 0:34:57 and um you know there you go you know however many it says that are you know 279 0:34:57 --> 0:35:04 embolized and they're not serious none of the above on the right hand side uh it's all the 280 0:35:05 --> 0:35:12 strokes so the same thing i'm asked 211 strokes that they're saying are not serious none of the 281 0:35:12 --> 0:35:22 above so now you can now you're starting to see how this how the 65 percent of the not serious 282 0:35:22 --> 0:35:29 none of the above get to be that get to climb to that height because there's so many um serious 283 0:35:29 --> 0:35:34 events in the none of the above now this is you know i'm gonna pause here for a second and say 284 0:35:34 --> 0:35:43 this is where people like myself jessica rose team enigma you know we kind of have to get together 285 0:35:44 --> 0:35:50 standardize maybe say hey you know what okay let's on our on our own personal database let's 286 0:35:50 --> 0:35:56 reclassify some of these into their more appropriate buckets like life-threatening 287 0:35:56 --> 0:36:04 or hospital when it says that they're in the hospital um you know reclassify these 288 0:36:04 --> 0:36:12 appropriately and then do our analysis with our toxic lots and our you know all of our other 289 0:36:12 --> 0:36:18 analysis because really if you're not doing that you're shaving you're shaving all of that all of 290 0:36:18 --> 0:36:24 this stuff away when you're doing it and saying oh look how you know not toxic this lot is are you 291 0:36:24 --> 0:36:30 sure are you sure because there's a there's a ton in the none of the above that we we simply always 292 0:36:30 --> 0:36:33 uh shave away okay next slide please 293 0:36:36 --> 0:36:44 okay so uh infarctions the same thing infarctions that are not serious none of the above i mean come 294 0:36:44 --> 0:36:49 on that's that's a lot i can't it's kind of small i can't see how many how many cases there were but 295 0:36:49 --> 0:36:56 um there you go that's quite a bit of infarctions that are basically classified as not serious wow 296 0:36:56 --> 0:36:58 okay next slide please 297 0:37:01 --> 0:37:12 okay so um again um you know i've identified and reclassified currently over 20,000 reports that 298 0:37:12 --> 0:37:19 in my dashboard i said i could do whatever i want this is my dashboard i reclassify these 20,000 299 0:37:19 --> 0:37:25 none of the above and i put them in their more appropriate um event level as in life-threatening 300 0:37:25 --> 0:37:32 or hospitalization you know i can't i i don't put anything in permanent disability because i 301 0:37:32 --> 0:37:37 can't tell that it was a permanent disability so basically all of these 20,000 i'm basically 302 0:37:37 --> 0:37:42 that i've that i've already reclassified and i have to do a whole bunch more this is like a weekly 303 0:37:42 --> 0:37:49 thing um uh but i've reclassified them into hospitalization or life-threatening or uh 304 0:37:49 --> 0:37:53 urgency room uh okay next uh next slide 305 0:37:58 --> 0:38:08 uh okay no no wrong way that's okay here we go where am i okay this is a good one i watched 306 0:38:08 --> 0:38:17 i saw this just a sec okay all righty so this is my are you dead or are you not dead this is a part 307 0:38:17 --> 0:38:23 of my uh you know the misclassifications where they tell us that there's they're dead in the 308 0:38:23 --> 0:38:32 symptoms and maybe even in the write-up but not in the box the box and the box is everything i mean 309 0:38:32 --> 0:38:39 unfortunately the box is everything of what you check off in the box um but here's here's an 310 0:38:39 --> 0:38:45 example of people that are basically dead but the box wasn't checked off okay next slide 311 0:38:47 --> 0:38:55 these are more uh deads or not deads now there's a some philosophical debates in in some of these 312 0:38:55 --> 0:39:02 reports because it'll say uh one of them says a brain death but he's not dead so yeah i get it 313 0:39:02 --> 0:39:08 but this this begs the question like is the person dead now after a year later or are they still 314 0:39:09 --> 0:39:17 in a coma and brain death right so that that just tells you that these only the initial report is 315 0:39:17 --> 0:39:23 made public these are now these reports are now petrified in time they're not going to change um 316 0:39:25 --> 0:39:27 uh okay next page 317 0:39:30 --> 0:39:38 so here's the old game um of cardiac arrest so at the time when i spooled this there was 318 0:39:38 --> 0:39:51 2800 cardiac arrests which produced 1900 deaths that leaves us with about almost another thousand 319 0:39:52 --> 0:40:00 cardiac arrests where the person did not die and you know me as a as a hmo claims auditor where 320 0:40:00 --> 0:40:09 i am deciding whether to pay to reimburse the er doc or the the admitting physician at stanford 321 0:40:09 --> 0:40:18 hospital or to um or to deny reimbursement you know i read i read these um these cardiac arrests 322 0:40:18 --> 0:40:25 and then i'm wondering hey wait a minute did this person ever um breathe again did he have a did he 323 0:40:25 --> 0:40:32 ever did he get resuscitated and uh the one on the right is an example where i would question it 324 0:40:32 --> 0:40:38 of course as an hmo claims auditor unfortunately and i hated i hated that job was that i was trying 325 0:40:38 --> 0:40:46 to look for some reason not to pay as opposed to uh you know trying to to pay appropriately i was 326 0:40:46 --> 0:40:53 trying to i was jaded i i was trained to be jaded i you know they i mean they they wanted to know 327 0:40:53 --> 0:41:00 how much did i actually deny and not pay stanford hospital you know things like that but anyways 328 0:41:00 --> 0:41:10 okay uh next next page so here's an example of a good uh cardiac arrest with resuscitation 329 0:41:12 --> 0:41:18 and um you know a lot of times these critical care docs you know the last thing they tell me is 330 0:41:18 --> 0:41:25 cardiac arrest that's like the very last two words in the whole soap note or the whole encounter data 331 0:41:25 --> 0:41:33 or their op op report and it's like um you know is this person dead did this person you know or or 332 0:41:33 --> 0:41:37 what because you don't you know according to this report you never told me that the person was 333 0:41:37 --> 0:41:44 resuscitated so there's an example of a good resuscitation so what i'm saying basically to 334 0:41:44 --> 0:41:50 wrap it up is that there's a ton of uh there's probably at least two or three hundred i could 335 0:41:50 --> 0:41:56 safely say of cardiac arrest that i would seriously question they're probably dead but 336 0:41:57 --> 0:42:02 but you know the way it's written up and who the heck knows by the way they you know they they uh 337 0:42:02 --> 0:42:10 the box for did they die yes or no just checked off no i would seriously question if i had a crystal 338 0:42:10 --> 0:42:14 ball and everybody was playing fair i could find another couple hundred deaths from just the people 339 0:42:14 --> 0:42:26 in cardiac arrest okay next uh next slide okay so here is the official blurb right here and they 340 0:42:26 --> 0:42:33 call it a reporting issue the cdc wonder system where it says basically the follow-up reports do 341 0:42:33 --> 0:42:42 not appear you know even though even though the system continues to to receive uh updates 342 0:42:44 --> 0:42:51 you know only the follow-up reports uh you know the follow-up reports do not appear and they call 343 0:42:51 --> 0:42:58 that a reporting issue it's under reporting issues that's that's an arbitrary that's an arbitrary 344 0:42:58 --> 0:43:05 issue that's one that they created um you know and my i put in there so i wonder how many people 345 0:43:06 --> 0:43:11 who filed initial and severe reports are now since dead okay next page please 346 0:43:14 --> 0:43:25 so here is the blurb that tells us that you know only initial reports is a new thing since 2011 347 0:43:26 --> 0:43:38 because before 2011 they did they did update theirs with follow-up reports so you know it's 348 0:43:38 --> 0:43:44 arbitrary that they tell us well only only initial reports are now are now made public 349 0:43:45 --> 0:43:53 that that's the biggest loophole right there that that they have that you know maybe maybe 350 0:43:53 --> 0:43:58 Fauci gives uh gives the ceos of uh of the university hospitals a little private memo 351 0:43:58 --> 0:44:04 and he says hey if you absolutely must file a mares report make sure the person has a heartbeat 352 0:44:04 --> 0:44:11 because um you know uh only the initial report's going to be made public and and the public won't 353 0:44:11 --> 0:44:16 know about it when they die the following day okay so next next slide 354 0:44:16 --> 0:44:24 okay so here is um administration errors there's a hundred and twenty eight thousand admin errors 355 0:44:24 --> 0:44:31 at the time that i pulled this report and just to take a gander at all the different types of errors 356 0:44:32 --> 0:44:41 you know it they the inappropriate age is hiding and they stuff it under the administration errors 357 0:44:41 --> 0:44:50 which has uh right there 89,000 uh reports about third on the list right there but you get an idea 358 0:44:50 --> 0:44:59 of where they're all at and and this this technically should be should be the the you know the um 359 0:45:00 --> 0:45:08 the none of the above not serious okay 128,000 sure not not the uh 12,000 360 0:45:09 --> 0:45:18 12,000 inappropriate ages uh you know are hiding in this 128,000 administration errors um but yeah 361 0:45:18 --> 0:45:28 not not 800,000 reports you know not uh 65 of our entire bears database as none of the above 362 0:45:28 --> 0:45:38 something is wrong um so okay next next slide 363 0:45:41 --> 0:45:47 um uh charles can you read the the top for me i can't see that part 364 0:45:50 --> 0:45:56 i can read it um so it says to reiterate only initial reports are made public 365 0:45:57 --> 0:46:05 this report has been expecting deaths for over a year yeah there you go so if you look closely 366 0:46:05 --> 0:46:11 you will see um down you know at the top when it was entered when the person was vaccinated 367 0:46:11 --> 0:46:17 when his symptoms started and then down at the bottom you know you see the write-up and it says 368 0:46:17 --> 0:46:24 expect death well if you go today and look at this report it still says expect death you know so the 369 0:46:24 --> 0:46:31 person's been expecting to die for over a year now well this person probably most likely is dead 370 0:46:31 --> 0:46:38 now but you know this we'll never know and the report will never be updated so you've written 371 0:46:38 --> 0:46:43 there to finish albert you've said so it will forever be classified as life-threatening 372 0:46:44 --> 0:46:49 yeah because this is this is what that's the highest level that it currently is at 373 0:46:50 --> 0:46:59 um so it'll it'll be there forever as as a life-threatening it won't it won't change to either 374 0:46:59 --> 0:47:05 oh he recovered he got better now he's alive or he's dead you know just stays there petrified like 375 0:47:05 --> 0:47:11 that okay so next next slide please 376 0:47:11 --> 0:47:19 okay here's moving on don't forget to look everywhere for covid-19 adverse events this 377 0:47:19 --> 0:47:25 is like where's waldo so i'm looking at everything and you'd notice these are all 378 0:47:25 --> 0:47:32 covid-19 adverse events but for whatever reasons they're classified as some other vaccine type 379 0:47:32 --> 0:47:38 be it a pneumovax or an hpb or a flu vaccine um you know i won't go i won't go into i'll let you 380 0:47:38 --> 0:47:45 read these later but looking closely at them if you could see it's pretty small print these were 381 0:47:45 --> 0:47:54 all covid vaccines and uh some of them i could see like dengue fever being a a musta uh 382 0:47:54 --> 0:48:03 and uh some of them i could see like dengue fever being a a musta uh an off stake because on the 383 0:48:03 --> 0:48:13 pick list i'll uh an adverse event report dengue sits right below covid-19 because and that's why 384 0:48:13 --> 0:48:19 there's like a bunch of dengue fevers that are that are vaccine adverse events that are not dengue's 385 0:48:19 --> 0:48:26 they're they're actually covid-19 and um i get the clue either in the write-up or in the lot 386 0:48:26 --> 0:48:33 number the lot number tells me uh uh that that's a clue that it's a moderna or a Pfizer something 387 0:48:33 --> 0:48:43 like that okay next slide there's thousands by the way like this that are classified incorrectly as 388 0:48:43 --> 0:48:51 a flu vaccine or a hpb or and this is the stuff we got to fix you know and then and then analyze 389 0:48:51 --> 0:48:56 then do the you know the craig part of coopers and the team enigmas and the welcome the eagles and 390 0:48:56 --> 0:49:12 the jessica roses uh analysis um but anyways uh next slide okay so these are good examples 391 0:49:12 --> 0:49:17 of clues where you know i'll take you you know they're they're classified as something else like 392 0:49:17 --> 0:49:27 a dengue fever but on the bottom uh right there's a dengue fever uh and it gave me that it gave me 393 0:49:27 --> 0:49:35 this this lot number right here um the ew and i say here this is a common but excellent example 394 0:49:35 --> 0:49:45 this is a Pfizer lot this is just one lot number ew whatever 0185 and you can see that about a 395 0:49:45 --> 0:49:53 quarter of the way down there manufacturer uh what is it the type of the manufacturer and then the 396 0:49:53 --> 0:50:00 lot number and i'm pointing to um reports it's really small i can't really i can't see it too 397 0:50:00 --> 0:50:06 well but here's an example where all of these are are actually misclassified and this is common so 398 0:50:06 --> 0:50:12 this is one lot number and there's probably when you add up all of the records that are misclassified 399 0:50:12 --> 0:50:21 that's about that's about 10 10 reports that's about 10 10 reports so multiply 10 reports times 400 0:50:21 --> 0:50:29 uh you know 20 000 unique lot numbers and that's about how how much how much um you know 401 0:50:29 --> 0:50:36 over how much um not oversight how much needs to be fixed i mean they're telling us 402 0:50:36 --> 0:50:43 you know it's filed under dengue Ebola meningiococcal and then unknown vax type that's another big one we 403 0:50:43 --> 0:50:50 got to look at the unknown vax type and the unknown manufacturer that's another big one i read reports 404 0:50:50 --> 0:50:55 and people say like oh well i know it was covid i know it was a covid job but i don't know which 405 0:50:55 --> 0:51:02 if it was moderna or if it was uh pfizer and so they they you know rightfully they choose unknown 406 0:51:02 --> 0:51:07 manufacturer that's fair enough but hey you know the four to six weeks bet process they ought to 407 0:51:07 --> 0:51:13 go back in there and say hey you know figure out which which which uh manufacturer was it 408 0:51:13 --> 0:51:17 you know help a brother out okay next uh next slide 409 0:51:17 --> 0:51:24 okay so in my uh actual dashboard where i change where i actually make the appropriate change and 410 0:51:24 --> 0:51:32 i change a jansen to a moderna or a jansen to a pfizer or a moderna to a jansen you know there's 411 0:51:32 --> 0:51:39 my numbers and and i can tell i mean my dashboard is courtroom ready courtroom ready i can say hey 412 0:51:39 --> 0:51:44 what about this what about these ones what about these ones and i have still way more to go to 413 0:51:44 --> 0:51:52 these ones and i have still way more to go this is 5000 close to 5000 reports where i fixed the 414 0:51:52 --> 0:52:00 manufacturer lot mismatch i mean this moves the needle this moves the needle when you're going to 415 0:52:00 --> 0:52:07 analyze hot lots and toxic lots and and everything else you're gonna do you know if you're analyzing 416 0:52:07 --> 0:52:15 by age you're analyzing by dose you're i mean come on we have to do this first before we get 417 0:52:15 --> 0:52:21 to that next step even though you know the the data is ugly enough but i'm telling you it could be 418 0:52:21 --> 0:52:29 it's gonna look a lot worse a lot uglier if we could theoretically fix cleanse the data properly 419 0:52:29 --> 0:52:30 okay next page 420 0:52:32 --> 0:52:40 okay so this one let's take let's take a pause in one of my dashboards i'm looking at the unknown 421 0:52:40 --> 0:52:50 vax type and i see that there's i don't know how much that is 25 27 000 total reports of all 32 422 0:52:50 --> 0:52:58 years and down at the bottom half you could see by year every year we were getting what maybe a few 423 0:52:58 --> 0:53:06 hundred unknown vax types and then you see that it shot up right at you know i can't explain 2019 424 0:53:06 --> 0:53:19 but 2020 and 2021 that it's like how do you explain now i've i've found a ton of uh basically 425 0:53:19 --> 0:53:26 covet jabs in the unknown vax type because of the clue of the lot number or it straight up just 426 0:53:26 --> 0:53:32 tells me right in the write-up this was a covet jab but when it doesn't do either it doesn't give 427 0:53:32 --> 0:53:39 me any clue i think about this historical data and i say you know this is probably a covet jab 428 0:53:39 --> 0:53:49 because of this historical data i mean it and then 2020 you look at the 2020 most of that is because 429 0:53:49 --> 0:53:55 of the last half the last two weeks of december when you look at it by backstate because when the 430 0:53:55 --> 0:54:02 jab rolled out that's when all the unknowns started to spike so most of that stuff in unknown 431 0:54:02 --> 0:54:10 is is covet stuff so that's another thing all of us superstar grizzled analytics type we have to 432 0:54:10 --> 0:54:16 factor in this unknown stuff here too this unknown vax type okay uh next slide please 433 0:54:17 --> 0:54:26 okay here's a good example i just did a i just did a search for unknown vax type and that has the 434 0:54:26 --> 0:54:32 word in the write-up in the symptom write-up area the word moderna i mean that's an easy one and look 435 0:54:32 --> 0:54:40 at how many i find in unknown vax type so there's 24 deaths right there 41 permanent disabilities in 436 0:54:41 --> 0:54:51 the word moderna just like this report on the left hand side so you know i mean this is low 437 0:54:51 --> 0:54:56 hanging fruit people let's type let's tack on another 24 deaths to the to the to the back to 438 0:54:56 --> 0:55:03 the death count you know this is where i say this is where i say you know i always uh poke the bear 439 0:55:03 --> 0:55:10 with uh with steve kirsch with his factor 41 i hope he's listening but i keep poking the bear 440 0:55:10 --> 0:55:15 and i say nope nope nope it's higher it's got to be higher it's higher you know and i could pull 441 0:55:15 --> 0:55:23 out a thousand deaths conservatively extra deaths and tack it on to whatever the domestic death count 442 0:55:23 --> 0:55:30 is and say okay now do your factor 41 how much does it increase um you know and i'm you know i'm 443 0:55:30 --> 0:55:39 getting numbers like uh 700 000 like a million i'm of i'm in that camp that believes there's over a 444 0:55:39 --> 0:55:46 million people easy that have died since you know with because of the jab and uh you know a lot of 445 0:55:46 --> 0:55:53 people the actual people that died and their relatives don't even make won't even make the 446 0:55:53 --> 0:55:59 connection and will not ever file a report i mean that's what the factor 41 the under reporting 447 0:55:59 --> 0:56:06 factor is all about the harvard pilgrim report is all about i believe the harvard pilgrim report is 448 0:56:06 --> 0:56:12 correct was right and now possibly even antiquated i'm going in i'm coming in hot 449 0:56:12 --> 0:56:18 and i'm gonna say i think it's over 100 you could you could you could multiply this by more than 100 450 0:56:20 --> 0:56:26 but conservatively i think yeah i could double i could double factor 41 and make it 81 just by 451 0:56:27 --> 0:56:34 the the reports that i have in in bears if we can classify things properly if we can say you know 452 0:56:35 --> 0:56:42 uh you know get those cardiac arrests the 550 deaths that they've deleted let's i haven't even 453 0:56:42 --> 0:56:49 talked about the mass deletions but the 560 deaths or so that they've that they've deleted 454 0:56:49 --> 0:56:55 and sure some of them are duplicates sure but other ones you cannot you're scratching your head 455 0:56:55 --> 0:57:00 you don't know why they uh deleted them there you know presumably because it's a duplicate or false 456 0:57:00 --> 0:57:05 report but still what i'm saying is that no no no that's bullshit there now they're just straight 457 0:57:05 --> 0:57:13 up scrubbing data just like the dod database right that what they publish is what they want 458 0:57:13 --> 0:57:20 the public to see i actually am going on record to say i don't even think they publish all legitimate 459 0:57:20 --> 0:57:26 reports that they that they receive and what you know this under reporting factor i say hey wait a 460 0:57:26 --> 0:57:33 minute wait a minute there's a distinction here are you talking about one percent of the reports 461 0:57:33 --> 0:57:39 that they receive or or that they publish one percent of you know what what they get 462 0:57:39 --> 0:57:46 they only publish one percent or that's two distinct things like you know i don't i just 463 0:57:46 --> 0:57:52 don't think that they report everything that they receive all legitimate reports okay uh next slide 464 0:57:52 --> 0:57:55 please 465 0:57:55 --> 0:58:01 okay so you know here i already showed you this one but just to show you again so at 466 0:58:01 --> 0:58:10 at this point in time there was almost 28 percent of the ages were unknown well on a weekly basis i 467 0:58:10 --> 0:58:17 go in and i find the age properly written in the write-up and then i go and populate it and i i 468 0:58:17 --> 0:58:27 have my database my dashboard down to eight percent so where they're telling us 357 000 469 0:58:27 --> 0:58:34 reports are unknown age i have i have mine down to about a hundred and five thousand reports 470 0:58:34 --> 0:58:41 or eight percent of the entire database so um that tells me there's something wrong in the 471 0:58:42 --> 0:58:48 in the in the data fields like what are you doing this on purpose 472 0:58:52 --> 0:58:53 purposely not the age 473 0:59:01 --> 0:59:08 you tell you of that's not work properly because it seems like all of the foreign 474 0:59:08 --> 0:59:10 places 475 0:59:16 --> 0:59:18 we're losing you a little bit albert 476 0:59:21 --> 0:59:23 they're not gonna be anyways i grass hex 477 0:59:27 --> 0:59:28 albert why are we losing you 478 0:59:33 --> 0:59:34 he'll come back 479 0:59:38 --> 0:59:42 just everyone look at while we're waiting for elbert to come back look at that chart at the 480 0:59:42 --> 0:59:44 bottom right corner 481 0:59:47 --> 0:59:48 yeah 482 0:59:51 --> 0:59:58 so this is on his dashboard available to lawyers in cases which is one of the resources this is a 483 0:59:58 --> 1:00:03 this is an important resource this dashboard elbert can be called as a witness happy to be a 484 1:00:03 --> 1:00:09 elbert can be called as a witness happy to be a witness in cases i'm there are you here charles 485 1:00:09 --> 1:00:16 you got me yeah we lost the voice sorry sorry sorry yeah i got it looks like i got disconnected 486 1:00:16 --> 1:00:23 there but uh yeah so uh okay so uh unknown yeah unknown ages i got my dashboard down to 487 1:00:23 --> 1:00:29 eight percent so fine you know i fine tune you know get better visibility in my dashboard 488 1:00:30 --> 1:00:32 okay next uh next slide please 489 1:00:35 --> 1:00:43 okay this is another shot from my dashboard this is a foreign data that we don't talk about and uh 490 1:00:43 --> 1:00:54 you know um it's just a visual i'm just look at the colors there um you know 15 000 deaths 491 1:00:54 --> 1:01:00 are coming from foreign that's more than our domestic but they only give us uh like 40 percent 492 1:01:00 --> 1:01:08 of the 40 percent less than 40 percent of all the reports are foreign but the stuff that they give 493 1:01:08 --> 1:01:16 us is like way more uh severe it makes sense because that's what it's supposed to be uh let's 494 1:01:16 --> 1:01:26 go to the next slide i'll point this out the blurb that says you know so it gives the definition 495 1:01:26 --> 1:01:33 here of what of what uh you know what you know every now and then you know goes when the 496 1:01:33 --> 1:01:40 manufacturer receives from their subsidiary you know and what's a subsidiary i think countries 497 1:01:40 --> 1:01:48 all countries governments are a subsidiary to big pharma now but uh you know serious and unexpected 498 1:01:49 --> 1:01:57 only serious and unexpected or both serious and unexpected okay but that begs the question why are 499 1:01:57 --> 1:02:02 you giving us all this other stuff all this low level office visits and none of the above if you're 500 1:02:02 --> 1:02:08 only supposed to be giving us serious and unexpected so all of that yellow stuff in there is like why are 501 1:02:08 --> 1:02:14 you giving us that if it's only supposed to be serious and unexpected i don't look albert looking 502 1:02:14 --> 1:02:24 at those figures can i just ask quickly yeah you see the uk 11 281 yes a 13 354 and the usa has a 503 1:02:24 --> 1:02:30 population of 330 million i think the uk is about 67 million yeah that figure for the 504 1:02:31 --> 1:02:38 us should be a lot higher than it is if you go on what's happening in in the uk right absolutely 505 1:02:38 --> 1:02:45 so i you know is disproportionately high too see with a population of 10 million yeah and you could 506 1:02:45 --> 1:02:55 see them on the graph yes sorry sorry to bother um so this is pre-salon um i was wondering if if you 507 1:02:55 --> 1:03:04 were able to have any visual on on the reports on service members for the u.s because um i have 508 1:03:04 --> 1:03:11 numerous reports where i cannot find them in the various system that i submitted that they've been 509 1:03:12 --> 1:03:20 deleted and i previously requested a report from them that showed there were over 9953 510 1:03:20 --> 1:03:30 service member reports of which like 119 died and 10 were considered serious um but 511 1:03:31 --> 1:03:36 i'm sorry i'm i'm pressed for time here but i do have i do have a general who 512 1:03:37 --> 1:03:43 um yeah i do have an opportunity to present to and and and i have to give him solid compelling 513 1:03:43 --> 1:03:50 absolutely absolutely if you reach out to me i haven't there there is a field in there that 514 1:03:50 --> 1:03:55 tells us if it's coming from military if it's coming from pharmacy if it's coming from private 515 1:03:56 --> 1:04:04 um and i have looked at it and um i can update my my dashboard to include to include that i actually 516 1:04:04 --> 1:04:10 do have actually do have that view i can give it i can show you where it's at but um and then 517 1:04:10 --> 1:04:17 i've record all the deleted reports so i can corroborate you know whether you're what you're 518 1:04:17 --> 1:04:27 seeing or what what the historical record in bears says um as as far as uh you know i i really 519 1:04:27 --> 1:04:34 haven't seen that many that many uh deaths being deleted um from the military of course this is 520 1:04:34 --> 1:04:42 bears not that dod that's a separate uh database but here in bears um it really it doesn't seem 521 1:04:42 --> 1:04:49 like we're getting that much at all from the military into the um civilian civilian bears 522 1:04:49 --> 1:04:56 so i don't know how that works um but but yeah i can uh i can definitely help you out 523 1:04:56 --> 1:05:01 to uh to see what you know to tell you what what's in there point you in the right direction so you 524 1:05:01 --> 1:05:08 get a good bird's eye view of exactly what what military reports are in are in bears 525 1:05:09 --> 1:05:15 and this is so this is so acceptable because and i think people need to understand out of all of 526 1:05:15 --> 1:05:22 the bears reports those put in on service members are so easily and readily verified 527 1:05:23 --> 1:05:30 um to be true or accurate um and even the military won't respond and give us their 528 1:05:30 --> 1:05:37 investigation into the reports that um that have been sent on our own service members so 529 1:05:37 --> 1:05:46 that's treason teresa right yeah no not what you're saying it is it that they're withholding 530 1:05:46 --> 1:05:53 evidence of crime concealment of crime in this case is treason when the military is practicing it 531 1:05:56 --> 1:06:02 yeah all right so teresa any other questions because you are tight for time and we all know 532 1:06:02 --> 1:06:03 the work that you've been doing 533 1:06:03 --> 1:06:11 um no i really uh all i would say is i i have gotten a um quote official response back on the 534 1:06:11 --> 1:06:19 dmed data and they've changed their response yet again um but they do concede even in this response 535 1:06:19 --> 1:06:29 at 30 increase in pulmonary embolisms esophageal cancer and ovarian dysfunction but um not 536 1:06:29 --> 1:06:38 but they but they want everyone to believe that uh vaccine has cured bianboré belz palsy 537 1:06:39 --> 1:06:48 and um pretty much everything else um so so uh i appreciate everyone's help and anything you guys 538 1:06:48 --> 1:06:54 can give me i do have people that are starting to open their eyes and um are willing to uh 539 1:06:54 --> 1:07:00 to look closer but it's it's getting them even more dad i don't know how much more you need beyond 540 1:07:00 --> 1:07:06 the dmed but um so the theirs is the last part that i can think of 541 1:07:07 --> 1:07:13 hey albert hey albert this yes do you mind if i say a few words 542 1:07:14 --> 1:07:17 uh jim will have the questions when albert finishes but 543 1:07:17 --> 1:07:20 do you mind if i say a few words 544 1:07:21 --> 1:07:26 jim will have the questions when albert finishes but uh the reason why teresa came in she has to 545 1:07:26 --> 1:07:30 leave early just put your hand up and we'd be delighted to have your question let's let's 546 1:07:31 --> 1:07:38 can i ask um can i ask teresa whether she could email me or or if she's happy to do put a email 547 1:07:38 --> 1:07:46 in the chat but maybe she isn't yeah i absolutely am and i have data that we've never disclosed to 548 1:07:46 --> 1:07:51 other people on the congenital malformations specifically the types of malformations that 549 1:07:52 --> 1:07:57 that we have that i'll be working with dr thorpe and other people to to bring that forward 550 1:07:59 --> 1:08:05 but absolutely thank you very much would you would you be able to speak to sometime teresa 551 1:08:06 --> 1:08:13 i would love to okay um if you email me we can fix that absolutely all right thank you 552 1:08:13 --> 1:08:20 very good thank you okay albert back to you okay back to share screen just a second 553 1:08:22 --> 1:08:29 yeah we're almost we're almost done anyway so so yeah and what i was what i was thinking about this 554 1:08:29 --> 1:08:39 um foreign uh data here that we um we kind of skip over is that i think that possibly 555 1:08:40 --> 1:08:49 if we took the foreign statistics and somehow applied the the ratios to the to the domestic to 556 1:08:49 --> 1:08:55 the american stuff you know i think that would be a truer representation of what's going on i 557 1:08:55 --> 1:09:02 somehow think that there's maybe less barriers that the foreign data has that they're more honest 558 1:09:02 --> 1:09:08 about it or something that they crosswalk it and obviously you know we have to know that we are not 559 1:09:08 --> 1:09:15 getting all of the permanent disability or all of the severe effects uh severe events from foreign 560 1:09:15 --> 1:09:22 by way of the definition that it that only only the unex the serious and unexpected is supposed 561 1:09:22 --> 1:09:28 to cut is supposed to come over not everything i actually wish everything would come over 562 1:09:28 --> 1:09:33 and then it would truly be like an international their system if every single thing came over but 563 1:09:33 --> 1:09:41 no albert when it comes to deaths the uk is hiding deaths i have evidence of that oh yeah yeah i mean 564 1:09:41 --> 1:09:46 everybody's hiding in death there and then they're in unison no i mean you're comparing with the u 565 1:09:47 --> 1:09:55 us now if you look at the populations and on that basis you know think what the figures should be 566 1:09:55 --> 1:10:02 in the uk and they've still got around about 2000 and it's been stuck at about 2000 for ages 567 1:10:02 --> 1:10:09 oh man there's got to be there's got to be a lot more than that but anyway so yeah there's my um 568 1:10:09 --> 1:10:14 foreign data that we never uh we you know we don't talk about foreign the bears foreign data but 569 1:10:14 --> 1:10:20 here's in my visual um you know and and a lot of props a lot of thank yous to team enigma and 570 1:10:20 --> 1:10:28 craig part of cooper who um who who i who i learned that there was the um the two digit country code 571 1:10:28 --> 1:10:34 was in one of the data fields and then it was like oh okay now you know that makes sense now 572 1:10:34 --> 1:10:38 i can gear up you know i can create this visual and we could see exactly which country 573 1:10:39 --> 1:10:45 all of these foreign reports are coming from so uh so my hat's off to uh team enigma craig 574 1:10:45 --> 1:10:55 part of cooper uh next slide please okay so this is uh i know that this is the same side-by-side 575 1:10:55 --> 1:11:04 view of my uh total deleted reports but i have total deleted reports for all vaccines for the 576 1:11:04 --> 1:11:09 since the beginning of time at least since the beginning of meta alerts which will take us back 577 1:11:09 --> 1:11:19 to 2007 so uh you know all i think we're up to like 30 000 total reports that have been deleted 578 1:11:19 --> 1:11:25 majority of them during this covet era and a majority of them are covet reports that 579 1:11:25 --> 1:11:32 they're being deleted but um you know this is this is a view that you can look at later 580 1:11:32 --> 1:11:34 okay next next slide 581 1:11:37 --> 1:11:45 now this one is interesting nobody ever talks about it except me so we know what's published 582 1:11:45 --> 1:11:53 and we know what's deleted that leaves the last the the third uh section of id numbers that have 583 1:11:53 --> 1:12:02 never been published so every i every bears id number should be like treated like a gold 584 1:12:02 --> 1:12:11 ticket we should know what which you know every single id number but yet there's about 25 000 585 1:12:11 --> 1:12:17 id numbers that are unaccounted for that basically have never been published you know it's one thing 586 1:12:17 --> 1:12:24 to to publish a report and then delete it okay fine in the rare cases that you go you know after 587 1:12:24 --> 1:12:31 your four to six weeks of rigorous authentication you still have a reason to say oh no we have to 588 1:12:31 --> 1:12:38 delete it because it's a duplicate or it's a false report okay fine so be it but what about the 25 589 1:12:38 --> 1:12:46 000 basically in yellow that are unpublished this is um you know like the two-year-old 590 1:12:47 --> 1:12:54 death in alaska the baby in alaska around last thanksgiving that they accidentally released to 591 1:12:54 --> 1:13:01 the public and then and then pulled it back and uh never published that report that was you know 592 1:13:01 --> 1:13:08 what happened to that one i mean how where are these unpublished id numbers and and you know are 593 1:13:08 --> 1:13:14 they occupied with with severe events that you you know that you said oh no no this must be 594 1:13:15 --> 1:13:22 but they're not i'm just saying that they're not because you know why give us a temporary id number 595 1:13:22 --> 1:13:28 when you submit your report for the very first time they give you a temporary id number i mean 596 1:13:29 --> 1:13:34 what's the what's the facade and the minutiae behind all of that if you're just going to 597 1:13:34 --> 1:13:41 arbitrarily not report id numbers at all so anyway that deep thought uh next next page 598 1:13:45 --> 1:13:53 so here's here's the example of this um this report that never was officially published 599 1:13:53 --> 1:14:00 this is that two-year-old uh who baby that died in alaska six hours after they were 600 1:14:00 --> 1:14:08 waxed in thanksgiving of 2021 i mean let alone what are you doing vaccine a two-year-old 601 1:14:08 --> 1:14:15 that's that's an appropriate age but died six hours later seemingly as a gruesome you know 602 1:14:15 --> 1:14:26 bleeding from the eyes ears nose mouth supposedly um but it was never published officially it was 603 1:14:26 --> 1:14:32 available in the download files for the first few minutes and then they figured out that they 604 1:14:32 --> 1:14:38 didn't um you know want to publish it and they pulled back all the files and then repopulated 605 1:14:38 --> 1:14:46 the files so anyways uh this is the one i'll explain this uh erin siri um i got we got the 606 1:14:46 --> 1:14:52 information to erin siri and erin siri filed a foya against they realized the value of this 607 1:14:54 --> 1:15:00 but uh anyways okay next slide albert what dose did they give that baby do you know that was a uh 608 1:15:00 --> 1:15:06 um i'm sorry i it had it back there does it say it in there um i think it was 609 1:15:07 --> 1:15:14 Pfizer i think it was no i don't mean that did they give it adult dose oh oh you know what it 610 1:15:14 --> 1:15:23 does not say but i would have to imagine i mean they didn't have child doses back then in in uh 611 1:15:23 --> 1:15:32 last year of i mean not officially right exactly the points i'm making yeah yeah yeah and also 612 1:15:32 --> 1:15:38 they've only got a third of the joes now haven't they yeah from what i believe but they haven't 613 1:15:38 --> 1:15:46 adjusted it for babies of six months old right idiots right right yeah there's so much going 614 1:15:46 --> 1:15:53 how do you realize that um well yeah i mean i was kind of like stuck on the fact that uh 615 1:15:53 --> 1:16:01 that uh not you know the yes this was a baby yes you know tragically but really this speaks more 616 1:16:01 --> 1:16:10 to the throttling and the how many how many how many um legitimate reports are you not making 617 1:16:10 --> 1:16:19 public yes but the point i'm making um albert is that these doses are very important because you 618 1:16:19 --> 1:16:25 can't be giving uh an adult dose to a child and you can't either be giving the same dose to a 619 1:16:25 --> 1:16:33 to a 16 year old or a near 16 year old as a six month old baby yeah yeah it's just nonsense so in 620 1:16:33 --> 1:16:39 other words it's not only concealment of crime it's incompetence as well medical incompetence 621 1:16:39 --> 1:16:46 right there and just to let you know dr frost there is a lot of administration error reports 622 1:16:46 --> 1:16:55 that point to that fact under dosing or overdosing and seemingly there's there's not an adverse event 623 1:16:55 --> 1:17:00 other than the fact that it was an administration error and we haven't even looked at the mixing and 624 1:17:00 --> 1:17:05 matching which is unprecedented as far as i know right and then now couple that with the fact that 625 1:17:05 --> 1:17:12 only initial reports are made public if anything happens to these to these kids or adults for the 626 1:17:12 --> 1:17:18 overdosing or the under dosing or when they say the syringe breaks well the syringe breaks 627 1:17:19 --> 1:17:25 or they did give them a um you know a dose the following day a temperature excursion expiring 628 1:17:25 --> 1:17:31 the dose still happened but the report is filed because it's an administration error 629 1:17:32 --> 1:17:39 but think about it if anything else happen develops a few weeks down the road you know i 630 1:17:39 --> 1:17:47 showed you 128 000 errors that are there because they're errors but if those errors develop into 631 1:17:47 --> 1:17:52 something else we're not going to know about it that's how they shield that's how they shield 632 1:17:52 --> 1:17:58 what's going on i mean part of the i mean part of how how they how that's going to happen 633 1:17:58 --> 1:18:03 if only initial reports are made public and not not any of the follow-up stuff 634 1:18:05 --> 1:18:08 so anyways okay if we can move on to the next slide 635 1:18:11 --> 1:18:20 so this was the this was the foya that uh that erin seary made based on my work that i caught 636 1:18:20 --> 1:18:27 lightning in a bottle that day and i caught that one report that is unique from all other deleted 637 1:18:27 --> 1:18:34 reports because this report basically was never officially deleted it was it was technically never 638 1:18:34 --> 1:18:41 published never published at all other than the first three minutes that it became available then 639 1:18:41 --> 1:18:48 they pulled it back and then they tell erin seary oh no we don't have any records that doesn't exist 640 1:18:48 --> 1:18:55 well basically just that they don't have any records uh which which is strange uh okay next 641 1:18:56 --> 1:18:57 next slide 642 1:19:01 --> 1:19:11 and this is the backstory that i'll post a link with bearsanalysis.info of how we caught it and 643 1:19:11 --> 1:19:19 what we did and you know how we gave it to reached out to erin seary and how they found the value 644 1:19:19 --> 1:19:24 of what we were telling them and how that transpired into a foya request okay next slide 645 1:19:25 --> 1:19:35 and same same thing more just kind of more talks about the specifics about this why this 646 1:19:36 --> 1:19:44 why this is so valuable this this one two-year-old death but anyways next next slide 647 1:19:48 --> 1:19:55 and this was uh you know this was just showing that we when we our email when we wrote uh sent 648 1:19:55 --> 1:20:00 to series uh series people um okay next slide 649 1:20:05 --> 1:20:13 and again this was another the second uh part of the foya uh next slide 650 1:20:16 --> 1:20:20 that's where we stored the data uh next next slide 651 1:20:20 --> 1:20:31 okay now this one uh uh international person reached out to me and they said that they their 652 1:20:31 --> 1:20:42 group filed a foya for the same id number exactly the same id number because they saw this the iCan 653 1:20:43 --> 1:20:52 erin seary foya but they foya differently and they retrieved this email from johnny sue and 654 1:20:52 --> 1:20:59 shima buruku or whatever his name is and uh on the on the right hand side you can see i highlighted 655 1:20:59 --> 1:21:05 the actual id number and in here they're gonna say that they're passing it around each other 656 1:21:05 --> 1:21:11 with the dates and they're saying that hey can you look at this we think it's a false claim 657 1:21:11 --> 1:21:18 a false report okay that's fine so be it but that's not the point the point is is that you 658 1:21:19 --> 1:21:26 they are acknowledging the id number and it was never even published that's what this is that's 659 1:21:26 --> 1:21:33 what this is gonna um shed light on like hey you just told erin seary that the id number did not 660 1:21:33 --> 1:21:39 exist but internally you're bouncing it around between each other trying to figure out if it's 661 1:21:39 --> 1:21:49 fake or or not fake or or whatever but but really the id number if it exists that that means it's 662 1:21:49 --> 1:21:58 published and you never published it so so okay what about the 25 000 other id numbers that you 663 1:21:58 --> 1:22:04 never published is there any other deaths that you said oh no no no this death is a false report 664 1:22:05 --> 1:22:11 you know we caught him we caught him right here saying two different things telling erin seary 665 1:22:11 --> 1:22:17 that the id number did not exist and then telling this other group you know here's the email thread 666 1:22:17 --> 1:22:28 of us talking about it so anyways i i reached out to erin seary to let them know that they you know 667 1:22:28 --> 1:22:33 that there was another uh entity an international group i let them know who the group was 668 1:22:33 --> 1:22:39 just erin's to say erin seary erin they you know they're they're lying to you because they told 669 1:22:39 --> 1:22:46 this other group that that number did exist and here's the heavily redacted 16 pages of what they 670 1:22:46 --> 1:22:50 what they bounced back and forth but anyways okay uh next page 671 1:22:53 --> 1:22:59 okay so we're at the end here uh my friends and family and supporters next page 672 1:22:59 --> 1:23:05 uh this is some reports i um some analysis i made for steve kirsch you go to next page 673 1:23:06 --> 1:23:11 there was a video we could skip the video it was a three minute video but 674 1:23:13 --> 1:23:21 uh next page and uh yeah i just flashed it on screen but i was doing this type of stuff for steve 675 1:23:21 --> 1:23:27 when he'd ask me to hey can you analyze this or can you analyze that and i was like okay 676 1:23:27 --> 1:23:30 can you analyze this or can you analyze that so next page 677 1:23:34 --> 1:23:42 uh some more reports next page i just want to get them on screen um so yeah i basically did 678 1:23:42 --> 1:23:54 you know all 6500 symptoms you know for two just one year one solid year 2021 of covet 19 versus 679 1:23:54 --> 1:24:04 uh in all 29 years of uh of uh the verze database okay next next page 680 1:24:09 --> 1:24:16 uh oh here's a here's a good one take a look closely so they they received the enter date 681 1:24:16 --> 1:24:26 means the received date uh enter date 2021 what is that march 30 of 2021 and they did not make it 682 1:24:26 --> 1:24:34 officially public where where it actually appeared to the public so one year later there's 683 1:24:34 --> 1:24:40 thousands of reports like this now you know so i say thanks early warning system all cynically like 684 1:24:40 --> 1:24:49 that but that there's a good that's proof right there proofs is in the pudding okay next page 685 1:24:53 --> 1:25:01 um the mass deletions this the one on the right the right side uh yeah we're up to now this is 686 1:25:01 --> 1:25:07 just the last 12 weeks where they've deleted i don't know what i can't see the number 14 000 687 1:25:07 --> 1:25:14 or something they're up over 20 000 for the whole the whole covet era they've deleted 20 000 over 688 1:25:14 --> 1:25:21 20 000 reports but you know more than 14 000 of these reports have just been in the last 12 weeks 689 1:25:22 --> 1:25:30 and then on the left side we'll go back you go back and study that on replay but um i'm showing 690 1:25:30 --> 1:25:41 that uh 30 percent of the entire database takes over 43 days to publish that's late that's after 691 1:25:41 --> 1:25:48 six weeks that's after the four to six weeks vetting period they still uh are you know 32 percent 692 1:25:48 --> 1:25:58 of the entire database is still delayed and then if you look at it closely uh 146 000 reports are 693 1:25:58 --> 1:26:09 over 60 days late and that's inside of that uh 260 000 reports so i i tell you i basically go by 694 1:26:09 --> 1:26:18 and i see well gee there's actually 40 reports that are actually 400 plus days late kind of like 695 1:26:18 --> 1:26:22 that death that i showed you that took them they were sitting on the death they had it in their 696 1:26:22 --> 1:26:28 possession for over a year and then they published it so that's what that's what that's all about you 697 1:26:28 --> 1:26:39 can look at that later okay uh next page and this is the most toxic lot in the entire bears universe 698 1:26:39 --> 1:26:47 that escapes every scatter plot every phd scatter plot known to man uh this is when we do experiments 699 1:26:48 --> 1:26:54 we basically punt them off to like puerto rico or the north marianas islands but this particular 700 1:26:54 --> 1:27:03 lot right here this moderna lot 032 h20a that only has a little over 200 adverse events 701 1:27:03 --> 1:27:13 well guess what it actually has of those uh 200 adverse events it has um 36 deaths wow so so that 702 1:27:13 --> 1:27:22 proportion is actually makes it the most toxic lot in the entire universe but when you look closely 703 1:27:22 --> 1:27:29 at those 36 deaths you see that 25 of them or 26 of them whatever that came from puerto rico 704 1:27:30 --> 1:27:37 it's like how can that be possible uh but anyways uh yeah next next page 705 1:27:37 --> 1:27:47 this was um you know uh this is when um the expose broke uh broke either craig part of cooper or uh 706 1:27:47 --> 1:27:53 sasha a lot of pova's uh analysis of the toxic lots and then and then right away we're talking 707 1:27:53 --> 1:28:00 about the red states and blue states and possibly toxic lots go into more red states hey but on the 708 1:28:00 --> 1:28:06 right side that's the money shot that's uh i'm gonna i'll call it team enigma's analysis 709 1:28:07 --> 1:28:14 and on the left left side that was my analysis right next to theirs and i concurred i did the same 710 1:28:14 --> 1:28:24 exact thing and you know almost in the right order um based on the population size of each state i 711 1:28:24 --> 1:28:28 pretty much came up with the same numbers you know with the color motif the blue states in the 712 1:28:29 --> 1:28:38 in the red states um but i i included our territories like puerto rico like mariana's 713 1:28:38 --> 1:28:48 islands and lo and behold when i did that i saw that wow north mariana's islands is is actually 714 1:28:48 --> 1:28:58 the most toxic number one um puerto rico came in at number 15 and it's like you know for for uh 715 1:28:58 --> 1:29:05 a territory that size uh you know they they shouldn't be that high up but it's because they 716 1:29:05 --> 1:29:14 got that toxic lot uh 032 h 20a has to do a lot with that one but anyways um so that's the 717 1:29:14 --> 1:29:21 do a lot with that one but anyways um that was my analysis and i concur yeah i think that they 718 1:29:21 --> 1:29:26 i think that they were sending toxic lots even to red states i mean it's just what the data 719 1:29:27 --> 1:29:31 bears out but anyways uh next next slide 720 1:29:35 --> 1:29:42 this um on the left side it was showing like where kentucky and tennessee were 721 1:29:42 --> 1:29:50 um i did say that was all all um all deaths and then i kind of put in there for uh domestic 722 1:29:50 --> 1:29:59 and territory that's the d and t i was giving you the number for um just domestics and then um on 723 1:29:59 --> 1:30:06 the left side it was a slightly older slightly older you could see tennessee tennessee is the 724 1:30:06 --> 1:30:13 number one state bar none just straight up deaths bigger more deaths in tennessee than new york 725 1:30:13 --> 1:30:19 california texas is like how does that happen how does that happen that tennessee with their size 726 1:30:20 --> 1:30:27 straight up has more deaths period than new york tennessee strange and then kentucky's right next 727 1:30:27 --> 1:30:35 door and they're way up there too really strange are those absolute sorry i can't see the number 728 1:30:35 --> 1:30:42 are those absolute numbers those are absolute absolute death numbers yeah absolute wow 729 1:30:44 --> 1:30:49 this makes no sense why kentucky and tennessee are that high up but what oh texas has got a large 730 1:30:49 --> 1:30:56 population isn't it yeah texas florida has a large california has a large population i mean a lot 731 1:30:56 --> 1:31:03 larger than kentucky and tennessee what's the population of kentucky and tennessee about 732 1:31:03 --> 1:31:10 a couple of million each or more uh i think a little bit more than that um but i i don't have 733 1:31:10 --> 1:31:15 it at the top of my head i have a quick i have a quick hyperlink on my dashboard and you look 734 1:31:15 --> 1:31:21 right at the population so we've been able to 35 35 million isn't it so you would expect tennessee 735 1:31:21 --> 1:31:31 and kentucky to be above yeah yeah okay so uh we can go to the next page i think we're almost done 736 1:31:31 --> 1:31:42 here uh this is just another another uh report this was my 50 demarcation um analysis where 737 1:31:42 --> 1:31:51 all the blue was just the uptake of doses not not patients not people just doses and then i took um 738 1:31:52 --> 1:32:01 the line at 50 like uh where where does the the jab date land where i could say 50 of the of the 739 1:32:02 --> 1:32:10 adverse events happened happened uh within these doses by backstate and the other 50 on the other 740 1:32:10 --> 1:32:20 side um next page i'll show you a better a better one makes sense to me so this one i realized when 741 1:32:20 --> 1:32:28 i did the same thing they actually are throttling deaths deaths more than they throttle all the 742 1:32:28 --> 1:32:36 other permanent disabilities um all the other events as an example at this moment in time 743 1:32:36 --> 1:32:46 when there was about um when there was 7600 deaths in the bottom right hand corner i can't see but 744 1:32:46 --> 1:32:57 that was around the time in late 2021 um and 7600 deaths i did a study and i figured out that half 745 1:32:57 --> 1:33:06 of them or 3800 deaths all had a vaccination date uh look at tennessee seven million population i saw 746 1:33:07 --> 1:33:17 a note come by tennessee had seven million people um but 3800 deaths at this time had a vaccine date 747 1:33:17 --> 1:33:25 or a jab date before before whatever date that is i can't see it and i did that for every event 748 1:33:25 --> 1:33:30 and this this is telling me that they actually throttled deaths more than the other more than 749 1:33:31 --> 1:33:39 adverse events when i look at do it this way by a 50 demarcation okay uh next page 750 1:33:40 --> 1:33:47 and albert that's exactly what they did in the uk they were generous with the permanent disabilities 751 1:33:47 --> 1:33:52 i noticed on those figures there compared to the u.s but when it comes to deaths they're 752 1:33:53 --> 1:34:00 under reporting uh yeah hiding i would say yeah so this is another one this is kind of scary 753 1:34:00 --> 1:34:07 and i don't know what it means you know but i look at the bottom right this is march 17 2022 that 754 1:34:07 --> 1:34:17 that's around that's where i did when i did this study around march 17 2022 and at that time we had 755 1:34:17 --> 1:34:27 uh over we had 12 000 deaths for domestic and you can see that in doses we had what over 500 756 1:34:27 --> 1:34:42 million doses and when i saw where's the 50 mark by by jab date i can see that 6 000 of these deaths 757 1:34:42 --> 1:34:52 had a jab date of march 16th or older 2021 that's scary when you look at it this way i don't know if 758 1:34:53 --> 1:35:00 i'm interpreting the data correctly or whatever but you know that's how today when you look at 759 1:35:00 --> 1:35:07 all of the deaths for the domestic united states over 75 percent of all the deaths had a jab date 760 1:35:07 --> 1:35:16 of a year ago or older it's like man where's all the rest of them what how how does that happen 761 1:35:17 --> 1:35:23 it's because every week when they give us new data and they tell us you know 100 deaths for 762 1:35:23 --> 1:35:28 for every 100 deaths that they they tell us domestically you look at them closely and you 763 1:35:28 --> 1:35:35 look at their backs vaccination date and 40 of that hundred percent of that hundred of those 764 1:35:35 --> 1:35:44 hundred deaths the vax date is like a year a year ago um yeah so some of these people are having 765 1:35:44 --> 1:35:52 very delayed onsets like they got jabbed they must have been limping along for a long time long time 766 1:35:52 --> 1:36:00 and then they died you know just recently but then commingled in there the person was jabbed 767 1:36:00 --> 1:36:06 a year ago the person died a year ago and they're just telling about us just telling us about this 768 1:36:06 --> 1:36:14 death now i mean it's crazy they are throttling the data that's what i'm talking about they're 769 1:36:14 --> 1:36:22 throttling 75 percent of all deaths currently if you look at their vac state is over a year old 770 1:36:23 --> 1:36:29 i mean if they would have told us back in march there was 6 000 deaths we we might have stopped 771 1:36:29 --> 1:36:35 the program back then but back in march of 2021 they were telling us there was only there was like 772 1:36:35 --> 1:36:47 800 deaths so but we knew we we knew there were more than that from the phisa data oh yeah yeah 773 1:36:47 --> 1:36:54 absolutely that was february that was february of 2021 absolutely absolutely okay i think we're 774 1:36:54 --> 1:37:02 almost done next page we'll just fly through this stuff here um yeah it's a really really tiny tiny 775 1:37:02 --> 1:37:09 print but this is a death but there's three they bundled the deaths there's three toddlers on this 776 1:37:09 --> 1:37:21 one three babies that are died supposedly um so yeah that's bundling um to bundle multiple deaths 777 1:37:21 --> 1:37:27 and you know they delete it and this one i could not even find another toddler death in the state 778 1:37:27 --> 1:37:37 of virginia period at all so why did you delete this i don't know okay next page but charles they 779 1:37:37 --> 1:37:42 hadn't even got an emergency use authorization for for the for the children then so what what 780 1:37:42 --> 1:37:51 on earth were they doing that injecting the babies that too that too uh bundling so this one is really 781 1:37:51 --> 1:38:01 strange 726 patients dead what is going on the only thing about this is that it right in the 782 1:38:01 --> 1:38:08 beginning it says that the report was received from a consumer via company representative 783 1:38:09 --> 1:38:19 concerning 726 patients of unspecified sex so i highlight a second part the patients with the 784 1:38:19 --> 1:38:26 capital with the plural s patients died from unknown cause of death so i started to think like 785 1:38:26 --> 1:38:33 is this is this like the incredible hulk the green report like total fake like how did why didn't 786 1:38:33 --> 1:38:39 why is this still in there why didn't they delete it if it's if it's a false if it's false it's still 787 1:38:39 --> 1:38:46 there it's legit very rare that it actually says a company representative via company that's the 788 1:38:46 --> 1:38:56 manufacturer so i take that to mean that they got it via via a company via jansen i really think 789 1:38:56 --> 1:39:04 that jansen like got behind and and like you know let's stuff all let's stuff all these into one 790 1:39:04 --> 1:39:14 you know into one report but anyways okay you know for what it's worth there's there's a ton 791 1:39:14 --> 1:39:22 of bundled deaths of bundled uh you know there's studies we studied this or we studied that or 792 1:39:23 --> 1:39:31 you know it's like a nursing home 20 people dead and then they'll say they'll say this is um you 793 1:39:31 --> 1:39:37 know this is uh four of 20 this is five of 20 this is six of 20 and then when i go scoop up and say 794 1:39:37 --> 1:39:44 okay i can never it's rare that i find the complete set it's like it's like they say that this is one 795 1:39:44 --> 1:39:49 of 20 reports and then i go scoop them all together and i could only find like four or five of them 796 1:39:49 --> 1:39:55 it's like where's the rest of them so you know that's another thing if i could unbundle you 797 1:39:55 --> 1:40:01 allow me to go and unbundle all these deaths to the best of my ability and figure out i you know 798 1:40:01 --> 1:40:06 this is why i could tack on that's where i say hey i could do your steve kirch and tack on an extra 799 1:40:06 --> 1:40:10 thousand deaths okay next next slide 800 1:40:13 --> 1:40:20 um oh so this never lies again it's a total different one and this is interesting this was 801 1:40:20 --> 1:40:28 report number one of covid if you look closely there's no age it's foreign so it's not here in 802 1:40:28 --> 1:40:36 the united states but it was received by two vares vares received it october 19th of 2020 803 1:40:37 --> 1:40:45 i could tell by this id number starting in 890871 that's small they all start at the 900,000 804 1:40:46 --> 1:40:54 october 2020 wait a minute our official day when tiffany dover passed out on tv and you know we never 805 1:40:54 --> 1:41:03 heard from her again that was december 14th of 2020 this is two months before that in a foreign 806 1:41:03 --> 1:41:13 country and it says that the doctor refer performing a vaccination against covid 19 without fda approval 807 1:41:15 --> 1:41:22 so you know i just put in here that the entry date never lies and it's the vares data received 808 1:41:22 --> 1:41:29 claims plus one day okay so that's the day that they received it and this is just another example 809 1:41:29 --> 1:41:41 how timelines don't always fit the lying narrative they're lying uh okay so anyways this was actually 810 1:41:41 --> 1:41:51 this is actually uh bears covid report number one and it was deleted and and again it was received 811 1:41:51 --> 1:41:59 in october of 2020 but they didn't even show it to us flash it to us until um july of 2021 812 1:42:02 --> 1:42:09 so how did they i don't understand they weren't in they weren't injecting in october 2020 813 1:42:10 --> 1:42:17 yeah well that's the lie i mean not officially maybe this was a trial of course it usually would 814 1:42:17 --> 1:42:24 say trial but then you know the write-up says oh that the doctor's doing it against without the fda 815 1:42:24 --> 1:42:30 approval i mean i didn't even know they had them in a vial i didn't know they had the the juice 816 1:42:30 --> 1:42:39 the elixir packaged up in a vial in october they didn't wow not according to this this was received 817 1:42:39 --> 1:42:42 this was received by bears october 2020 818 1:42:42 --> 1:42:51 okay that's what i'm saying something doesn't pass the sniff test and you know this receive date does 819 1:42:51 --> 1:43:00 not does not lie i mean why did they but the pcr covid tests quote quote were being sold by the 820 1:43:00 --> 1:43:06 millions actually by the hundreds of millions around the world we have their bills of layings 821 1:43:06 --> 1:43:16 in 2017 specific for whatever uh whatever they're calling covid so it's possible that somebody 822 1:43:16 --> 1:43:25 actually did die from some toxic juice that was jabbed into them at that date yeah yeah well 823 1:43:25 --> 1:43:32 yeah you know to to to the narrative about the wuhan and the fish market and the whatever 824 1:43:33 --> 1:43:38 whatever it came out the bat soup and all of that stuff is like hey your timelines don't fit 825 1:43:40 --> 1:43:47 it doesn't seem um uh brand new it's like it's like you guys probably been working on 826 1:43:48 --> 1:43:56 working on some poison for for for a while you know possibly decades but i don't know i'm just 827 1:43:56 --> 1:44:02 a bean counter i'm just a bears guy so i don't want to get into that that uh debate uh but anyway 828 1:44:02 --> 1:44:11 we can go to the next slide uh okay now we're done so this is my mercola article with my dashboard 829 1:44:11 --> 1:44:20 next slide this was my epoch times got got mentioned in epoch times for the mass deletions 830 1:44:20 --> 1:44:29 next slide and uh all my visualizations uh my my uh powerpoint will be is made available 831 1:44:30 --> 1:44:39 to everybody to have um and uh my my little uh treasure trove of uh my shareable folder is 832 1:44:39 --> 1:44:45 is available to you guys i got a lot of juicy stuff in there so thank you so much for allowing 833 1:44:45 --> 1:44:52 me to present to you guys and i think i'm done well done albert round of applause for all that 834 1:44:52 --> 1:44:59 huge amount of work and jim jim thorpe has got dressed how very he's got out of his gym gear and 835 1:44:59 --> 1:45:06 anyways we'll get to you in a moment but you're looking very spiffy okay um um 836 1:45:07 --> 1:45:11 stephen you go first you've been asking questions we've got other we've got a string of hands up 837 1:45:11 --> 1:45:18 stephen so you know um how do you want to play this normally albert as you know stephen goes first 838 1:45:19 --> 1:45:26 so albert uh could i just ask you um so i've been saying concealment of crime um how much evidence 839 1:45:26 --> 1:45:34 is there from your point of view examining the data of concealment of crime oh my gosh 840 1:45:35 --> 1:45:41 i think there's a ton of evidence you know i i kept saying for a long time i think they're 841 1:45:41 --> 1:45:50 throttling and i mean i think that they don't they don't publish every legitimate claim that 842 1:45:50 --> 1:45:56 they receive i mean that's pretty that's very damning evidence that i believe that i think i 843 1:45:56 --> 1:46:05 can prove i'm trying to prove that i'm so so in my mind i'm trying to prove the most damning evidence 844 1:46:06 --> 1:46:12 that i feel could be possible and that's to say hey you guys are getting a bunch of reports 845 1:46:14 --> 1:46:20 and you're not publishing them even the legitimate reports sure there's going to be some duplicates 846 1:46:20 --> 1:46:26 and sure there's going to be some people that are trying to file false reports but that's minuscule 847 1:46:26 --> 1:46:33 i'm talking about you guys purposely sitting on on thousands possibly hundreds of thousands of reports 848 1:46:33 --> 1:46:43 and not publishing them at all that that's that's people so um the inappropriate age you said not 849 1:46:43 --> 1:46:50 administration oh sorry so i said not administrative area concealment of crime do you agree with that 850 1:46:50 --> 1:46:59 or not absolutely some some of the reports some of the reports are are fair are legitimate the 851 1:46:59 --> 1:47:09 mom snuck the snuck the kid in to get jabbed um the the kid was birthday was next week um something 852 1:47:09 --> 1:47:14 you know like that there's there's a there's you know there's a little share of that but then 853 1:47:14 --> 1:47:19 there's other ones that you that they say like oh the kid was six years old and we thought he was 854 1:47:19 --> 1:47:28 12 years old i mean that's not that's who said that in the report the reporter yes okay that the 855 1:47:28 --> 1:47:36 actual bears report said he uh you know patient was actually six years old the mother or or the 856 1:47:36 --> 1:47:43 family no no the while the the the person the administer the person who administrated the jab 857 1:47:44 --> 1:47:50 was the person who wrote the report saying that we thought the patient was six years old but it 858 1:47:50 --> 1:47:57 turns i mean we thought they were 12 years old but the patient was actually six years old yes but from 859 1:47:57 --> 1:48:04 what so am i right in thinking that even if the patient was 12 years old that was too young given 860 1:48:05 --> 1:48:08 that there was no emergency use authorization at that time for that age is that right 861 1:48:09 --> 1:48:14 yeah and and depending you know because it goes lower and the age is going lower and lower and 862 1:48:14 --> 1:48:20 lower so you have to so why did they say 12 instead of six when actually the emergency use 863 1:48:20 --> 1:48:27 authorization was only valid for over 16 isn't that right or not yeah well i mean at at the time 864 1:48:27 --> 1:48:32 whatever that whatever whenever that jab was given i'm just saying that that's what the report said 865 1:48:32 --> 1:48:39 either way it was inappropriate age and and maybe at the time 12 years old was an appropriate age 866 1:48:39 --> 1:48:46 but but they gave a six-year-old a jab but the point is they actually wrote it in the in the 867 1:48:46 --> 1:48:52 report that we thought this six-year-old was 12 that's pretty egregious right there how do you 868 1:48:52 --> 1:48:58 how do you do that how do you you don't i mean who makes who makes that mistake that they think a 869 1:48:58 --> 1:48:59 six-year-old is a 12 year old 870 1:49:02 --> 1:49:10 um incredibly and what the report says so i just wanted to you know the figures uh so i've looked 871 1:49:10 --> 1:49:20 at the figures today for the eu the uk and the us using um uh huge revigilance uh mhra and bears 872 1:49:20 --> 1:49:28 the uh total deaths at the moment is well actually this was june sorry may so it it was 873 1:49:28 --> 1:49:35 approaching 75 000 so it probably is 75 000 now but if you multiply that by um 874 1:49:37 --> 1:49:48 uh 10 which so you said uh steve kirch says 41 under reporting factor the uh the paper from 875 1:49:48 --> 1:49:59 harvard said um 100 if you remember another paper has said i think or other papers said 10 so 876 1:49:59 --> 1:50:10 people said 10 to 100 um but um so steve kirch settled on 41 um jessica rose settled on uh six 877 1:50:10 --> 1:50:20 to 14 according to an email she sent me david wiseman uh sorry david wiseman said six to 14 878 1:50:21 --> 1:50:30 um times the figure appearing for deaths now um um was appropriate and that he could defend 879 1:50:30 --> 1:50:38 anything between six and 14 jessica rose decided on six so she took the lower end of that i don't 880 1:50:38 --> 1:50:47 know whether she was using the same figures anyway point is so if you use 10 say multiply 75 000 by 881 1:50:47 --> 1:50:55 750 sorry uh by 10 you've got 750 000 obviously and then um if you uh bear in mind that the 882 1:50:55 --> 1:51:04 total population of the eu and the uk and the uh us is uh almost bang on 750 million which is um 883 1:51:05 --> 1:51:15 um a tenth of the uh world population it's actually a bit below but so so if you kind of 884 1:51:15 --> 1:51:26 multiply that 70 750 000 by four say instead of uh in excess of 10 because allowing for the so 885 1:51:26 --> 1:51:35 did i say 750 that meant 850 000 uh so 850 million sorry um and um so it's approximately nine times 886 1:51:35 --> 1:51:45 that uh the world's population so call it four to multiply seven that is um uh three million deaths 887 1:51:46 --> 1:51:54 and of course if it's um if you're using 10 times that which is what the harvard study said 100 888 1:51:54 --> 1:52:00 that's 30 million so we're talking about the kind of figures that the nazi holocaust 889 1:52:01 --> 1:52:10 that mount seitan's great leap forward and the great march and the uh great proletarian cultural 890 1:52:10 --> 1:52:17 revolution of mount seitan so um those are the kind of figures they're huge crimes against humanity 891 1:52:17 --> 1:52:21 and probably the great leap forward is that it's the biggest of them all which is estimated to be 892 1:52:21 --> 1:52:29 anything from 15 to 55 million but a lot of people settle for 42 million i don't know why 893 1:52:29 --> 1:52:35 that is but anyway so we're talking about huge numbers of deaths which people aren't aware of 894 1:52:35 --> 1:52:42 and um so even people in this group i think would be surprised to realize the so if you use 10 895 1:52:43 --> 1:52:50 10 times the um those deaths uh that we've got published um we get to a figure of um 3 million 896 1:52:50 --> 1:52:57 and if we use 100 times as the harvard study suggested um then it was um it's 30 million 897 1:52:57 --> 1:53:04 it's huge and you know easily enough to and peter mccullough when he was giving evidence to 898 1:53:06 --> 1:53:16 was it the texas senate he said 40 000 well you know for the eu the uk and the u.s. um but actually 899 1:53:16 --> 1:53:24 it's 75 000 so okay it's only twice as many but um so i just wanted to ask um 900 1:53:26 --> 1:53:31 is there any strategy from their point of view that you can see and how difficult is it to fiddle 901 1:53:31 --> 1:53:40 the data to conduct a cover-up with the data uh well i mean i think they're doing it now 902 1:53:41 --> 1:53:49 they're doing it now by own by by telling us only um 13 or 14 000 deaths are in bears for 903 1:53:50 --> 1:53:57 covet 19 jabs officially in bears when when i can you know forget you know just 904 1:53:58 --> 1:54:07 put us put aside the under reporting factor um just the data that's in there now um you know with 905 1:54:07 --> 1:54:14 the 14 000 i'm saying hey wait a minute i could take i could i could probably make that 16 000 906 1:54:15 --> 1:54:22 just in there now you know a couple extra thousand deaths but then when you know 907 1:54:23 --> 1:54:30 albert what i was trying to say was if someone was tasked with conducting a cover-up at the cdc the 908 1:54:30 --> 1:54:36 cdc runs there's doesn't it yeah yeah well they're working with the fda and phisa as far as i was 909 1:54:36 --> 1:54:43 and the others as far as i can see so um so they've got every reason to conduct a cover-up 910 1:54:43 --> 1:54:50 but what i'm saying is how difficult is it or how easy is it to conduct a cover-up 911 1:54:51 --> 1:54:58 using the data even if you know what you're doing so that there aren't crazy mistakes like you found 912 1:54:59 --> 1:55:06 yeah so on the next question that my other question is who do you think is fiddling the data 913 1:55:06 --> 1:55:13 if they are doing them which they are in my opinion well i think it's the very top brass of 914 1:55:13 --> 1:55:20 uh of the cdc and fda who are fiddling with the data i think they're in unison globally 915 1:55:21 --> 1:55:27 with the amount you know for example the amount of foreign reports that we get in every week in 916 1:55:27 --> 1:55:32 comparison to what our own domestic united states domestic numbers are 917 1:55:34 --> 1:55:44 you know that combination always basically mimics um what what the layman would expect for what our 918 1:55:44 --> 1:55:52 what our um our up our weekly uptakes just in the united states our weekly uptake for people getting 919 1:55:52 --> 1:55:58 boosters right now on the cdc tracker is abysmal thank god a lot of people are not drinking the 920 1:55:58 --> 1:56:07 kool-aid but with that what what the layman what the casual observer would expect the adverse 921 1:56:07 --> 1:56:13 events to be if you thought that theirs was only a couple of weeks behind and and and you know 922 1:56:14 --> 1:56:20 reporting all the adverse events and that's it that the for the most part they're caught up and 923 1:56:20 --> 1:56:26 and it takes them a week or two to receive a report and and make the ham make the sausage 924 1:56:26 --> 1:56:33 and then and then report it out um they're they're they're mimicking they're mimicking that 925 1:56:33 --> 1:56:39 that data so that it looks like it looks like they're uh you know that there is no inventory 926 1:56:39 --> 1:56:45 they're they're not behind on they're not sitting on a mound of reports and they're trying to process 927 1:56:45 --> 1:56:54 as fast as they can to get the reports out i don't you know uh i so albert they they they conduct 928 1:56:54 --> 1:57:00 something like this then the theirs people are asked to conduct a cover-up would that be 929 1:57:00 --> 1:57:10 frightening to data people in in this context from the point of view that that's so to go back to the 930 1:57:10 --> 1:57:18 question that i just said you know how difficult is it to fiddle the data i was expecting you to 931 1:57:18 --> 1:57:23 say very difficult but you haven't said that so far no i don't think it's difficult at all they 932 1:57:23 --> 1:57:32 they determine what they want to release each week okay and and if they can do that with with 933 1:57:32 --> 1:57:38 without any accountability they could say oh okay um you know only give the public a hundred deaths 934 1:57:38 --> 1:57:46 this week only give the public um you know 200 permanent disabilities this week and then clearly 935 1:57:46 --> 1:57:49 but clearly it is difficult albert because you found all these mistakes 936 1:57:51 --> 1:57:59 well well yeah i mean i'm i'm measuring i mean i'm i'm trying to measure you know something that i 937 1:57:59 --> 1:58:07 can't see but i can see it to say hey you you can't have your cake and eat it too um when you actually 938 1:58:07 --> 1:58:15 look at this closely of course not everybody has the um the visual the data the visualization 939 1:58:15 --> 1:58:20 dashboards that i do you know that i can see that i can just you know press the button here and all 940 1:58:20 --> 1:58:25 my graphs change and oh let's look at it here let's look at it this from this angle let's look at it 941 1:58:25 --> 1:58:31 from that angle um you know that's why i try to get my dashboard you know not me forget about me 942 1:58:31 --> 1:58:40 go with the dashboard my dashboard doesn't lie it just it reveals the data and the data is telling 943 1:58:40 --> 1:58:47 me that that they're throttling that there's all these mistakes in there all these oversights and 944 1:58:47 --> 1:58:54 and there's there's no help by intelligent design they want to obscure the data they want to 945 1:58:54 --> 1:59:00 obfuscate this data they don't want the truth but they haven't done a very good job of it 946 1:59:01 --> 1:59:06 that's what i'm trying to say yeah no they haven't done a good job of covering covering it up but 947 1:59:07 --> 1:59:14 you know my voice and and and our voice of of our side you know of the jessica roses and the crank 948 1:59:14 --> 1:59:20 part of cooper's and the you know where is such a tiny you know a tiny small voice in comparison 949 1:59:20 --> 1:59:26 to the to the globe yes well not if the lawyers get hold of it but anyway um so albert are you 950 1:59:26 --> 1:59:31 in touch with tort calendar no all right well i'll get you in touch with him then 951 1:59:34 --> 1:59:41 and um okay there's other possibilities if you if you email me um albert okay and then i'll 952 1:59:42 --> 1:59:51 yeah just remind me very good so okay charles has seen well daria we've got 25 minutes and we 953 1:59:51 --> 1:59:59 got eight hands up so go daria okay yeah there we go albert that was amazing i knew you were 954 1:59:59 --> 2:00:05 going to just blow the lid off this and i really appreciate what you did today thanks doctor as 955 2:00:05 --> 2:00:11 as i was looking at this and looking at your analysis uh we were having a conversation about 956 2:00:11 --> 2:00:19 uh a while back i think tom was on it and jerry it was on tom's uh call what is it telegram anyway 957 2:00:19 --> 2:00:29 about how the central banks will operate two sets of books the ones that they release to the public 958 2:00:30 --> 2:00:34 and then the other ones that have whatever on there that they don't want the public to see 959 2:00:35 --> 2:00:42 so as much as you've been able to pick out of what they actually put out to the public you know 960 2:00:42 --> 2:00:48 there's probably another set of books somewhere and we can only imagine with horror 961 2:00:50 --> 2:00:56 the extent of the damage and you in the insurance industry this is the first question i was going to 962 2:00:56 --> 2:01:08 ask you is it seems to me that insurance um what are the words actuaries and uh the adjusters 963 2:01:08 --> 2:01:15 claims adjusters and the companies themselves they have a certain amount of money allotted for 964 2:01:15 --> 2:01:21 claims and then if that money runs out they have to go to their reinsurance and a lot of them use 965 2:01:21 --> 2:01:29 like loyds of london for example so is this another is this mass death and all the insurance 966 2:01:29 --> 2:01:34 claims that are going to come out of it um actually an intentional mechanism to 967 2:01:35 --> 2:01:39 bring on a financial collapse do you think i know that's a lot of speculation but i just see a lot 968 2:01:39 --> 2:01:45 of parallels with this sloppy reporting this very suspicious concealing of information in the wrong 969 2:01:45 --> 2:01:53 part of the forms that you're doing as obfuscation like you said and what seems to be happening 970 2:01:53 --> 2:02:02 simultaneously in the financial world and so and again like you said these people are all in lock 971 2:02:02 --> 2:02:10 step these these corporations institutions governments are all operating off the same 972 2:02:10 --> 2:02:16 page and it is of a scale that i don't think i would ever expected to see 973 2:02:18 --> 2:02:23 we've never seen it before usually at some point they fight amongst themselves and everything falls 974 2:02:23 --> 2:02:30 apart but this has stayed so consistent and i did post in the chat somebody brought up the 975 2:02:30 --> 2:02:38 um vaccine lots going out in 2017 first of all from a pharmacy standpoint uh were they stored 976 2:02:38 --> 2:02:44 properly what's in them uh and we've been talking previously about these batches getting possibly 977 2:02:44 --> 2:02:50 different compounds in them and i can tell you that if even things like polyethylene glycol aren't 978 2:02:50 --> 2:02:56 properly manufactured uh there's additional contaminants that can add to autoimmunity 979 2:02:56 --> 2:03:04 and since these are emergency use authorized shots they could easily have adulterated 980 2:03:05 --> 2:03:13 compounds in them to be further responsible for some of these side effects so i know it's a big 981 2:03:13 --> 2:03:19 question but i'm just looking at this big pile of mess and then you hear Fauci saying in january of 982 2:03:19 --> 2:03:26 2017 january 11 to be exact no doubt trump will face surprise infectious disease outbreak in other 983 2:03:26 --> 2:03:32 words he was telegraphing exactly what they had planned out and wouldn't it be great if somebody 984 2:03:32 --> 2:03:40 would be a whistleblower and basically we need another um i just blanked out the guy's name 985 2:03:40 --> 2:03:48 who's in russia right now uh oh snowden yeah we need a medical snowden for this you know and 986 2:03:49 --> 2:03:54 because that's the only way people are going to even have a clue because for some reason we uh 987 2:03:54 --> 2:04:00 it just doesn't make sense that anyone would still be on this bandwagon doctors or patients 988 2:04:00 --> 2:04:06 but this denial is so powerful and at some point i think the doctors are getting pretty jaded and 989 2:04:07 --> 2:04:11 they're like yeah whatever you want it you got it and still not going with the informed consent 990 2:04:12 --> 2:04:15 and then you got the other ones that are kicking people out of the practice because they think for 991 2:04:15 --> 2:04:20 themselves and they don't want the shots and they're right so um it's just a mess across the board 992 2:04:20 --> 2:04:24 but from the insurance perspective specifically if you can address that that's what i was curious 993 2:04:24 --> 2:04:28 about because i wouldn't think an insurance carrier would put up with this and you were on 994 2:04:28 --> 2:04:32 the side of not paying for things because the only way to save money on health care is to not 995 2:04:32 --> 2:04:39 provide it or not pay for it and um and so i i don't know the complications here must be 996 2:04:39 --> 2:04:47 astronomical in terms of uh claims uh for hospital visits and everything else and if they're not 997 2:04:48 --> 2:04:52 since they were covering up that these were jab related and they're just going showing up as random 998 2:04:52 --> 2:04:58 other diagnoses uh so we're going to see more atrial fibrillations more strokes more dbt's 999 2:04:58 --> 2:05:04 as a primary diagnosis to cover up for the fact that it's a vaccine complication so it would be 1000 2:05:04 --> 2:05:09 interesting i don't know if you you probably you're the only person i know personally from our group 1001 2:05:09 --> 2:05:18 here that has the means to look at the cms data the um for the insurance carriers that you work 1002 2:05:18 --> 2:05:24 with the hmos to see how many of these other diagnoses don't even mention jabs when they come 1003 2:05:24 --> 2:05:32 in yet are way off scale perhaps that same 40 increase as what we're seeing with the deaths 1004 2:05:32 --> 2:05:39 and with the vaccine complications themselves and so that's kind of in the context of the big 1005 2:05:39 --> 2:05:45 corporate uh world the finance world and the insurance world they're all connected and they 1006 2:05:45 --> 2:05:49 have to see this and they have to know it and and i don't know they can't really get out of 1007 2:05:49 --> 2:05:54 paying all the claims because they can't say oh you you gave consent to a experimental treatment 1008 2:05:54 --> 2:05:59 so you don't get your life insurance payout they they're not going to be allowed to get away with 1009 2:05:59 --> 2:06:06 that uh very much so anyway that's kind of what i was going to ask you about thanks yeah thank 1010 2:06:06 --> 2:06:11 you there so yeah there's a there's a couple of things one thing um which is really interesting 1011 2:06:11 --> 2:06:20 i thought about you know as far as um uh insurance coverage back for vaccines i got a um my standard 1012 2:06:21 --> 2:06:26 medicaid alert you know because i get all the alerts for medicaid and here in california it's 1013 2:06:26 --> 2:06:37 called medi-cal for california but it's medicaid right so um but it says that that the fourth dose 1014 2:06:37 --> 2:06:45 is now going to be uh reimbursable by medicaid what the fourth really what does that mean wait 1015 2:06:45 --> 2:06:50 a minute and then it says and then in the fine print it said even if the person has um 1016 2:06:51 --> 2:06:57 um insurance coverage but their but their insurance doesn't doesn't pick it up it doesn't cover a 1017 2:06:57 --> 2:07:05 vaccine that medicaid will pick it up and so i realized oh my gosh this is like 1018 2:07:06 --> 2:07:13 the um the medicaid and all medicaid has is they have presumptive eligibility and this comes into 1019 2:07:13 --> 2:07:21 play with um pregnant pregnant people pregnant ladies that you know um don't have insurance 1020 2:07:22 --> 2:07:26 and then they come out and they they land in the hospital somewhere and they go you know they come 1021 2:07:26 --> 2:07:32 out of nowhere and they got to deliver right so medicaid steps in and says okay we're going to 1022 2:07:32 --> 2:07:37 presume that you're eligible and this is the admins of the hospital they go we're going to 1023 2:07:37 --> 2:07:42 presume that you're eligible with medicaid here fill out this 30 pages we'll help you fill it out 1024 2:07:43 --> 2:07:49 so that we can give you this this all this medicaid id number that's already hot ready to go we just 1025 2:07:49 --> 2:07:55 have to assign your name to it and that's called presumptive eligibility and then now that you have 1026 2:07:55 --> 2:08:00 presumptive eligibility you're covered for the basic stuff for your pregnancy right so that the 1027 2:08:00 --> 2:08:09 hospital can get paid but something like that is what they're going to use for these um these jabs 1028 2:08:09 --> 2:08:16 the fourth booster so so they're going to use like anybody that's how they're going to be able to 1029 2:08:17 --> 2:08:25 reimburse now the government to reimburse uh Pfizer i mean we just heard that Biden spent three 1030 2:08:25 --> 2:08:33 you know um three three billion dollars for the next 300 doses of of vaccines i mean i wouldn't 1031 2:08:33 --> 2:08:40 be surprised i go wait i just got this medicaid alert saying that it's reimbursable now so what 1032 2:08:40 --> 2:08:45 are they going to double dip are they going to double pay are they going to double pay pharma 1033 2:08:45 --> 2:08:52 once for this 300 it's three billion dollars and then once you know to you know build it build it 1034 2:08:52 --> 2:08:59 to medicaid and medicaid's going to pay them again yeah money laundering i have no idea what's what's 1035 2:08:59 --> 2:09:05 going on but but uh the fourth dose is going to be is going to be covered where the first three i guess 1036 2:09:06 --> 2:09:13 they weren't or okay come on we're going to get moving okay less prognostication more questions 1037 2:09:13 --> 2:09:21 so yeah hi albert uh i just think you are amazing and i want to thank you so much for your work 1038 2:09:26 --> 2:09:31 i have two questions and one comment my first question relates to the differing 1039 2:09:33 --> 2:09:40 dashboards and as a county supervisor in my county when i asked the public health officer 1040 2:09:41 --> 2:09:50 will you know are you know why aren't we looking at the bears and the response that i got was that 1041 2:09:51 --> 2:09:58 we're not really looking at the bears we have other reporting systems in california and i asked 1042 2:09:58 --> 2:10:06 for a list of all the reporting areas and i think i i just went and looked for it in my two big boxes 1043 2:10:06 --> 2:10:13 of cobit stuff and i can't find it but it was something like 11 different organizations i think 1044 2:10:13 --> 2:10:19 cal ready and pfizer and maderna and there's all these different places where a physician or patient 1045 2:10:19 --> 2:10:30 can go or a vaccine um uh you know a vaccine distribution center can go to report an adverse 1046 2:10:30 --> 2:10:39 event and so i'm my question to you are are you aware of these is this going on worldwide 1047 2:10:40 --> 2:10:49 is this further diluting you know what we know about because it's as far as i know the 1985 or 1048 2:10:49 --> 2:11:00 law required that cdc put together an adverse event tracking and and responsibly you know monitor it 1049 2:11:00 --> 2:11:05 to see you know what are the outcomes so they could immediately stop it if things are going wrong and 1050 2:11:05 --> 2:11:14 so um and then my comment regarding those you know bundled i i'm those bundled deaths i wonder if 1051 2:11:15 --> 2:11:20 you know those off the wall things that are happening in bears is just another way to 1052 2:11:20 --> 2:11:25 delegitimize bears because another one of the responses i got from public health was 1053 2:11:26 --> 2:11:35 that um bears is not really yeah it's not credible or not credible we're not looking we're 1054 2:11:35 --> 2:11:40 you know we're waiting for cdc to tell us and we're waiting for cdph and this and that and so 1055 2:11:40 --> 2:11:44 i wonder if you could comment regarding that and then i just have one other quick question 1056 2:11:45 --> 2:11:52 yeah so the the credibility part and the uh you know they a lot of people a lot of and a lot of 1057 2:11:52 --> 2:11:58 anti-bears people will say uh you know it's not credible it because it uh in part because it's 1058 2:11:58 --> 2:12:08 self-reporting that anybody can report to it right yes anybody can um but it is it is a federal crime 1059 2:12:09 --> 2:12:16 to file a false report that's that's first first off um so be careful don't you know don't file a 1060 2:12:16 --> 2:12:22 false report or don't don't file if you want to sabotage it sabotage bears by by putting in a 1061 2:12:22 --> 2:12:27 million deaths like let's say i want to sabotage it and put in a million deaths and to make it look 1062 2:12:27 --> 2:12:38 all wonky like this like this one 700 deaths on one report um you know it's a felony to do that 1063 2:12:38 --> 2:12:43 and then the other thing that i learned since i filed my own report on behalf of my uncle 1064 2:12:44 --> 2:12:51 was that they um you know they asked they ask you who who who are you how are you related to the 1065 2:12:51 --> 2:12:56 you know to the victim to the patient and they they asked for they asked for everything but 1066 2:12:56 --> 2:13:03 my social they asked for my address my telephone number my you know my name i don't think they ask 1067 2:13:03 --> 2:13:09 for my age but you know telephone number eight you know name everything right and it's all you know 1068 2:13:09 --> 2:13:15 um uh you know a felony to file a false report so i'm putting all that in there and i'm realizing 1069 2:13:16 --> 2:13:23 you know it's going to be really tough to to sabotage and file a you know a thousand false 1070 2:13:24 --> 2:13:33 reports if i wanted to try to sabotage bears um so on that respect even with that there is from the 1071 2:13:34 --> 2:13:40 in the cdc's own website there's some fine print in there that says that 85 percent of all the 1072 2:13:40 --> 2:13:49 reports are are actually filled out by some form of health care worker meaning uh a doctor a nurse 1073 2:13:50 --> 2:13:58 a bears representative a manufacturer rep you know like a pfizer person or moderna persons 1074 2:13:58 --> 2:14:04 some form of health care worker from a from a old folks home or you know a nursing nursing 1075 2:14:04 --> 2:14:12 facility or something like that 85 percent of all the reports so you know that that that would be 1076 2:14:12 --> 2:14:20 my comeback like hey this is very credible theirs is very credible 85 percent are are non-health you 1077 2:14:20 --> 2:14:26 know filled out by by health care workers somebody that's not related to the patient and is actually 1078 2:14:26 --> 2:14:34 a health care worker 85 right off the bat i heard this week that you know when they redefined what 1079 2:14:34 --> 2:14:44 a vaccine is um you know they read maybe redefined it at the who but it wasn't statute statutory uh 1080 2:14:44 --> 2:14:56 you know it's statutorily um redefined so is there a statutory you know a rule that you that bears 1081 2:14:56 --> 2:15:04 is the dashboard is that the dashboard you know as far as i know that's the spot you're supposed to 1082 2:15:04 --> 2:15:10 um you're supposed to file uh you know an adverse event you can't go to some other place and what's 1083 2:15:10 --> 2:15:18 more what's more the doctors are actually obligated to file a report under certain conditions 1084 2:15:18 --> 2:15:24 you know and it's and it's kind of documented it's well documented one of the conditions um that i 1085 2:15:24 --> 2:15:29 know about the top of my head is if the patient gets admitted into the hospital into like you know 1086 2:15:29 --> 2:15:35 past emergency room and into to an admin so basically if they have to spend one night in the 1087 2:15:35 --> 2:15:43 hospital they have it has to be the problem is is that the physicians in the hospital don't ask 1088 2:15:45 --> 2:15:52 about your vax history were you vaxed recently that's what happened to my uncle may have like 1089 2:15:52 --> 2:16:00 may have last year when he stroked 76 the day he stroked his age group like 65 of his age group 1090 2:16:00 --> 2:16:08 they said had already been back you know mid 70s right year old person and he gets a stroke a month 1091 2:16:08 --> 2:16:15 after his second his second jab you would think that the er here in san jose regional hospital 1092 2:16:15 --> 2:16:22 that's who it was on the east side regional they didn't ask nobody in there asked uh have you been 1093 2:16:22 --> 2:16:30 backed i was in there asking them albert that's that's my medical mispractice in my um opinion 1094 2:16:30 --> 2:16:37 yeah yeah yeah they don't ask it's not accidental either it's not accidental it's not accidental 1095 2:16:37 --> 2:16:43 they're not asking because then they would be obligated then that would kick in and they would 1096 2:16:43 --> 2:16:48 be obligated to file the report because it says they're on the cdc that yeah yeah you know so so 1097 2:16:48 --> 2:16:55 they write out in their in their stops and their standard operating procedures a beautiful picture 1098 2:16:55 --> 2:17:00 this is how our checks and balances work and this is how we're going to do it and we're legit except 1099 2:17:01 --> 2:17:07 except you know in in reality in practical application they're like let's not ask if 1100 2:17:07 --> 2:17:14 they're about the vaccine we don't want to know it gets worse than that albert because i have 1101 2:17:14 --> 2:17:21 friends who are severely damaged from the vaccine and their doctor says oh no you know it's it's 1102 2:17:21 --> 2:17:28 not the vaccine you just have a genetic predisposition to to have this you know wow billion 1103 2:17:28 --> 2:17:33 beret or whatever it's you know neurological you know you can hardly walk up the stairs it's 1104 2:17:34 --> 2:17:39 it's horrible what's happening but i won't okay come on we're supposed to be finishing in 10 1105 2:17:39 --> 2:17:44 minutes there's no way we're going with all these hands up okay but can i one more question just 1106 2:17:44 --> 2:17:53 if i'm quick i i want to say to the backing up um are you backing all of this up a snapshot in time 1107 2:17:53 --> 2:18:01 and are you and and duplicating it because for and i'm and i'm thinking for legal purposes 1108 2:18:01 --> 2:18:08 because i would presume there's going to be monkey business around this they're going to try and 1109 2:18:08 --> 2:18:13 blindside you with some well theirs wasn't even the dashboard we use or you know i i don't know 1110 2:18:13 --> 2:18:18 how to get to the bottom of that because there's so many places to report it's the only dashboard 1111 2:18:18 --> 2:18:25 board that exists that um they could have used reporting but they don't even acknowledge it in 1112 2:18:25 --> 2:18:31 my county they don't want to talk about it um well of course they don't want to talk about it but they 1113 2:18:31 --> 2:18:36 have to because that's the only way that adverse events from vaccines can be recorded in the 1114 2:18:36 --> 2:18:42 united states isn't that right no there's 11 other you can report directly to pfizer you can 1115 2:18:42 --> 2:18:50 report to this cdph in california the public health at the state level they say cdc manages 1116 2:18:50 --> 2:18:54 the bears and they'll let us know if there's something adverse or going on with bears that 1117 2:18:54 --> 2:18:59 we need to know about so we don't even worry about bears they don't want to talk about it and they 1118 2:18:59 --> 2:19:04 don't want to look at it and so i don't know what the rule is i'd love to know if there's 1119 2:19:05 --> 2:19:12 a question that's not a question well if there's a lawyer or an attorney in the group that can tell 1120 2:19:12 --> 2:19:16 them the mayor doesn't want to do this lots lots of people don't want to do stuff but that's the 1121 2:19:16 --> 2:19:22 question that you put hey what if they ignored the the only place you can put it is bears that's 1122 2:19:22 --> 2:19:26 that's the that's the answer the fact that someone doesn't want to pay attention to is irrelevant 1123 2:19:27 --> 2:19:33 exactly you ask no one here knows is what's the legal structure what's the legal standing of bears 1124 2:19:33 --> 2:19:36 now no one's put their hand up but that's the question it's a good question 1125 2:19:37 --> 2:19:44 i would make your answers snappier mate yeah anything else sir charles it does sound like um 1126 2:19:45 --> 2:19:50 that uh oh i forgot what i was going to say now go ahead i was just going to say it becomes 1127 2:19:50 --> 2:19:58 relevant if you're trying to make a point that there's enough adverse reactions in bears that 1128 2:19:58 --> 2:20:05 we should stop the shot right now um it's relevant if they delegitimize bears and you can't make your 1129 2:20:05 --> 2:20:11 point well they can't really delegitimize it as far as i know it's the only place that you can 1130 2:20:11 --> 2:20:15 record and if you record wrongly you're charged with a federal crime so that's pretty big 1131 2:20:18 --> 2:20:24 just okay that's the structure let me let me state it is a mandatory requirement to 1132 2:20:25 --> 2:20:33 to it's a mandatory to file no one has ever been charged so that that somehow someone has been 1133 2:20:33 --> 2:20:39 charged and convicted never happened that's one of the reasons it's a a toothless mechanism but 1134 2:20:39 --> 2:20:46 to your question that is mandatory reporting it is the only mandatory reporting on a national basis 1135 2:20:46 --> 2:20:52 is two bars just to clear it up to the group albert can you just in one sentence clarify where 1136 2:20:52 --> 2:20:59 theirs stands compared with the other authorities that sue was saying 10 or 12 other places you can 1137 2:20:59 --> 2:21:04 record it i don't think those 10 or 12 other places come anywhere near the credibility of 1138 2:21:04 --> 2:21:10 theirs am i right or not yeah well so i think that you know the what i've heard the 10 or 12 other 1139 2:21:11 --> 2:21:19 the different types of systems that exist like cms would be a cyst would be one of those 12 systems 1140 2:21:19 --> 2:21:26 or um you know but but they're not i mean that's like you know it's you know medicare is that is 1141 2:21:26 --> 2:21:32 that system medicaid is another is another one cms albert i'm just trying to drive down to the 1142 2:21:32 --> 2:21:38 central point which is which is the most credible system we've got so sue says there are 10 or 12 1143 2:21:38 --> 2:21:44 and there's you know theirs is discredited i i would like to believe that you spend so much time 1144 2:21:45 --> 2:21:53 that you would say that theirs stands alone as the premier yeah theirs is theirs is the alone 1145 2:21:53 --> 2:21:59 premier it's the only place you can there's there's truth for health that's macula's and 1146 2:21:59 --> 2:22:06 dr valet's new one truth for health but um you know that that's not you know that that's a that's a 1147 2:22:06 --> 2:22:12 adverse events reporting system um but that's the only other one that i know that the point i'm trying 1148 2:22:12 --> 2:22:18 to make albert is that sue is doing the enemy's work for them by saying oh well actually there 1149 2:22:18 --> 2:22:25 are all these other places you can report it and uh okay well i didn't i didn't say that the public 1150 2:22:25 --> 2:22:31 health officer said that i was trying to point out that there are all these deaths on bears 1151 2:22:31 --> 2:22:38 and she was delegitimizing me on the dais to say well we don't even look at bears we have other 1152 2:22:38 --> 2:22:44 reporting like we report to other areas but the point i'm trying to get to sue is what is our 1153 2:22:44 --> 2:22:52 opinion in this group you know what okay we've got it steven we've got it okay jim hi good afternoon 1154 2:22:52 --> 2:22:59 thank you very much for uh this incredible conference uh and i just want to say albert 1155 2:23:00 --> 2:23:06 incredible work incredible job uh god bless you keep up the good work uh my name is dr jim thorpe 1156 2:23:06 --> 2:23:12 and i i know nobody knows me out there but i'm a board certified obstetrician gynecologist board 1157 2:23:12 --> 2:23:19 certified maternal fetal medicine specialist i've been in practice uh high risk obi uh i see seven 1158 2:23:19 --> 2:23:27 thousand high risk obis a year i'm very busy i've been extensively published i've served on the 1159 2:23:27 --> 2:23:34 society of maternal fetal medicine board for four years i've refereed major journals um yada yada yada 1160 2:23:35 --> 2:23:42 so that's who i am it's been devastating in pregnancy we just completed a focused study 1161 2:23:43 --> 2:23:51 and we it's in pre-print it'll be published soon epoch times uh did a article on it on friday 1162 2:23:51 --> 2:23:59 actually two of them uh one on the tv and one on the uh actual news site what we found was just 1163 2:23:59 --> 2:24:05 it's really devastating it's perfectly consistent with what i've seen we have first of all before 1164 2:24:05 --> 2:24:12 we get pregnant of course there's over a thousand fold increase in menstrual abnormalities which is 1165 2:24:12 --> 2:24:20 very concerning in terms of the miscarriage risk over 50 fold greater compared with influenza 1166 2:24:20 --> 2:24:28 vaccine which has been used in pregnancy since 1998 these are all odds ratios compared to 1167 2:24:29 --> 2:24:37 influenza vaccine okay since 1998 through the various data fetal chromosomal abnormalities 1168 2:24:37 --> 2:24:46 a hundred fold greater let that sink in fetal malformations 50 fold greater by the way all these 1169 2:24:46 --> 2:24:53 95 confidence interval are so far away from unity they don't even come close fetal cystic 1170 2:24:53 --> 2:25:02 hygroma a specific fetal malformation 80 fold increase fetal cardiac disorders 40 fold increase 1171 2:25:03 --> 2:25:14 fetal cardiac arrhythmias 50 fold greater fetal vascular mal for mal perfusions 100 fold greater 1172 2:25:14 --> 2:25:22 fetal growth abnormalities 40 times greater fetal uh abnormal surveillance tests be it a 1173 2:25:22 --> 2:25:30 biophysical profile or an nst or a doppler 20 fold greater fetal placental thrombosis blood clotting 1174 2:25:30 --> 2:25:40 of the placenta 70 fold greater and fetal death 35 times greater um and and albert i i need you 1175 2:25:40 --> 2:25:47 i need your help i'm and and god bless these brave courageous whistleblowers one was on earlier 1176 2:25:47 --> 2:25:52 her name is dr trece along she's a friend of mine and she's a co-author on this paper 1177 2:25:52 --> 2:25:59 also uh dr stewart tankersley uh both of them are active duty now they're both whistleblowers 1178 2:25:59 --> 2:26:05 i don't know the third one but i i'm sure that i'll be connected with them um christianne northrop 1179 2:26:06 --> 2:26:13 is is on our team and many others and you will see this in print soon to the point of your 1180 2:26:13 --> 2:26:20 very important point of verus verus is the gold standard for pharmacovigilance it always has been 1181 2:26:20 --> 2:26:27 it always will be of course these people um the stakeholders are trying to discredit it 1182 2:26:27 --> 2:26:35 because it is golden for our position and i will say to the naysayers when people first of all 1183 2:26:35 --> 2:26:39 people say to me well why don't you publish your stuff in the new england journal of medicine 1184 2:26:39 --> 2:26:45 because it's corrupted just look at uh dr eric rubin and what he's done he's a stakeholder look 1185 2:26:45 --> 2:26:51 at the ridiculous stuff he's published the shima bakoro article which was mentioned earlier one 1186 2:26:51 --> 2:26:59 year ago it's a travesty that's fraudulent there were 22 authors all of them are stakeholders 1187 2:26:59 --> 2:27:07 they're paid by the federal government including tommy shima bakoro i believe you can connect the 1188 2:27:07 --> 2:27:15 dots that was a ghost written article it was taken directly from 5.3.6 pfizer 1189 2:27:15 --> 2:27:23 ranch post marketing survey except one thing they took out all the fetal deaths they didn't mention 1190 2:27:23 --> 2:27:28 the maternal deaths nor did they mention the pregnancy complications that were 50 percent 1191 2:27:28 --> 2:27:35 so i i just want to bring this to the attention of everybody i i so much appreciate your time 1192 2:27:35 --> 2:27:43 and attention to this matter my last comment is when people dog veres and say well it's not 1193 2:27:43 --> 2:27:50 accurate of course it's accurate you know steve kirsch myself many others can cite 12 sources 1194 2:27:50 --> 2:27:57 consistent perfectly they're independent of veres and they're perfectly consistent with veres albert 1195 2:27:57 --> 2:28:04 i agree with you the under reporting factor is far closer to 100 than it is to 40 because nobody 1196 2:28:04 --> 2:28:10 in my business no physician wants to report this you understand for those my colleagues listening 1197 2:28:10 --> 2:28:17 in europe physicians and nurses in the united states of america we have a legal gag order on us 1198 2:28:17 --> 2:28:25 from all of the governing bodies all the ngo's that are attacking me the american board of ob-gyn 1199 2:28:26 --> 2:28:31 dr peter mccullough the american board of internal medicine pierre corey the american board of 1200 2:28:31 --> 2:28:37 internal medicine why are they attacking us because we are getting in their face and and they don't 1201 2:28:37 --> 2:28:44 like it the american board of medic ob-gyn is trying to strip me of all my credentials which 1202 2:28:44 --> 2:28:50 i've worked for for 40 years despite the fact that none of them have the credibility that i do 1203 2:28:51 --> 2:28:57 they're bureaucrats i've showed them this data they won't receive it i've asked to debate them 1204 2:28:57 --> 2:29:03 in the senate with ron johnson and with dr mccullough and dr corbett they won't do it so 1205 2:29:04 --> 2:29:09 it's very very clear so if they don't want to believe theirs what about the 12 other sources 1206 2:29:09 --> 2:29:15 that steve kirsch has documented perfectly consistent with veres which i've documented 1207 2:29:15 --> 2:29:21 as well absolutely perfectly consistent with veres if you want to throw those 12 other studies 1208 2:29:21 --> 2:29:30 out are you going to believe the pfizer trance itself the 5.3.6 down they tell you in the first 1209 2:29:30 --> 2:29:39 in the first uh 90 days of use it killed associated with death in 1223 cases and in 274 pregnant 1210 2:29:39 --> 2:29:45 women it was associated with major complications the shima bakura article from new england journal 1211 2:29:45 --> 2:29:52 medicine literally as a journal reviewer i wouldn't have let it go in a market rocher tabloid 1212 2:29:52 --> 2:29:58 in the grocery store he said that there and there were three ob-gyn doctors i know tommy shima 1213 2:29:58 --> 2:30:05 becuro is a i think he's not an ob-gyn but these doctors knew best they took 700 patients in that 1214 2:30:05 --> 2:30:11 study and they put those patients in the denominator for the miscarriage in the first trimester when 1215 2:30:11 --> 2:30:17 they didn't get their shot until the third trimester so they claimed a 13 rate of miscarriage 1216 2:30:17 --> 2:30:23 which is on par with the population but that's wrong if you take those 700 patients out of the 1217 2:30:23 --> 2:30:28 denominator that should have never ever been put in there i believe it was put in there fraudulently 1218 2:30:28 --> 2:30:35 the miscarriage rate is 82 guess what an 82 miscarriage rate is more effective abortifacient 1219 2:30:35 --> 2:30:44 than ru 486 the abortion pill which is as you know methacrystone and it's arguably more effective so 1220 2:30:44 --> 2:30:53 the fda demands a black box warning on the ru 486 the abortion pill and demand informed consent 1221 2:30:53 --> 2:30:59 where is the black box warning on the pregnancy it is more effective than aborting pregnancies 1222 2:31:00 --> 2:31:06 abortion thank you very much wow thanks doctor that's incredible excellent jim thank you 1223 2:31:11 --> 2:31:20 oh i think um sorry i think charles maybe is eating something um thank you so much jim for that and um 1224 2:31:21 --> 2:31:29 uh we must try to have you on as well as therese long one day if you would be prepared to do that 1225 2:31:30 --> 2:31:34 thank you yeah um simon 1226 2:31:40 --> 2:31:48 are you there simon i am yes yeah it's your turn oh it's gerard's turn okay i can i can go 1227 2:31:50 --> 2:31:56 okay well anyway all right sorry uh yeah well thank you very much uh uh albert fantastic as as 1228 2:31:56 --> 2:32:06 as usual um my question was was uh what wait i thought it was it was not my turn sorry um 1229 2:32:07 --> 2:32:13 should we skip to to gerard yes please please if you want i will go after him thank you okay gerard 1230 2:32:13 --> 2:32:18 could we try to speed up the questions and also uh albert we do appreciate you but um 1231 2:32:19 --> 2:32:26 sometimes you're rather long in answering like i would be um sure pardon well 1232 2:32:27 --> 2:32:38 i certainly yeah okay so gerard well i just want to touch on two points one is that generally 1233 2:32:38 --> 2:32:43 there in in any criminal enterprise there are two elements to it there's the one of the crime itself 1234 2:32:43 --> 2:32:49 and then there's the cover-up in the politicians and other criminals the it's usually the cover-up 1235 2:32:49 --> 2:32:57 that gets them such as in nixon and um watergate watergate and those and that's what what they go 1236 2:32:57 --> 2:33:03 down for the cover-up i would think that the the crime in this case is the principal one where we 1237 2:33:03 --> 2:33:10 actually where the genocide you know the the actual genocide of the the world population or the 1238 2:33:10 --> 2:33:18 attempted genocide is the crime and the secondary crime is in fact the cover-up i would hope then 1239 2:33:18 --> 2:33:24 that perhaps within the people who who are complicit in the cover-up that we would get 1240 2:33:24 --> 2:33:32 whistleblowers like i i i i am tired of hoping for whistleblowers within the medical profession i 1241 2:33:32 --> 2:33:41 suppose most of the whistleblowers are here are in these sort of groups but maybe we get some 1242 2:33:41 --> 2:33:46 real whistleblowers from among the statisticians and that's within theirs the the point that you 1243 2:33:46 --> 2:33:53 know i'd like to ask i felt and it's been touched on by dr jim torp that only somewhere between 1244 2:33:53 --> 2:34:00 two and a half percent and one percent historically i adverse side effects were reported in various 1245 2:34:01 --> 2:34:05 if that's true if it's somewhere between two and a half one percent of side effects 1246 2:34:07 --> 2:34:14 there is a massive number of dead people out there directly related to the messenger rna 1247 2:34:14 --> 2:34:21 assault and that's really really frightening am i right in thinking that it is only somewhere 1248 2:34:21 --> 2:34:28 between one and two percent of adverse side effects are reported to theirs yes that's what 1249 2:34:28 --> 2:34:34 the harvard um the harvard study showed but i mean it could so other people argue it's 1250 2:34:34 --> 2:34:39 ten times you know the underreporting factor is 10 the harvard study it's at 100 1251 2:34:39 --> 2:34:46 yeah that's that's that's pretty frightening i as i often say i'm in a battle with the irish medical 1252 2:34:46 --> 2:34:51 council over my been struck off the medical register and i would hope that i'll be looking 1253 2:34:51 --> 2:34:58 for albert and jim thorpe perhaps to come as witnesses for me in my case when i eventually 1254 2:34:58 --> 2:35:06 come again before the medical council and when they get around to it which is a year and a half 1255 2:35:06 --> 2:35:13 when they get around to it which is a year and a half later so thanks very much and jim for a 1256 2:35:13 --> 2:35:22 fabulous sterling work yes excellent uh albert have you um submitted an affidavit to any lawyer 1257 2:35:23 --> 2:35:31 no i'd love to okay well maybe i can get toad candor to do that yeah and then you could have 1258 2:35:31 --> 2:35:39 that gerard yeah yeah that's what i would need like you know i i i don't have any legal representation 1259 2:35:39 --> 2:35:45 in my in my case with the medical council as such not formally legal representation 1260 2:35:45 --> 2:35:49 but to an extent i want to keep it that way because i feel that lawyers often get in the way you know 1261 2:35:50 --> 2:35:50 um 1262 2:35:53 --> 2:36:02 yeah so next um question is oh are you ready simon or not yeah ready sorry sorry for that 1263 2:36:02 --> 2:36:09 yes thank you albert again uh thank you for for great great talk um you made me realize actually 1264 2:36:09 --> 2:36:16 one out of thousand two hundred belgians have a permanent disability whoa because you you put the 1265 2:36:17 --> 2:36:22 population next to it but actually you have to look at only the vaccinated the vaccinated 1266 2:36:22 --> 2:36:27 population which is less and then it's uh about six thousand on eight million which is which is 1267 2:36:27 --> 2:36:33 crazy and i think these lines uh would really work when we try to convince people about the dangers 1268 2:36:33 --> 2:36:40 of the vaccine and a lot of these uh analysis that you do could actually become really good one-liners 1269 2:36:40 --> 2:36:46 uh that that people can can look at and at the same time i was as you know that we've talked before 1270 2:36:47 --> 2:36:54 about putting all the data in from all the other uh adverse effect databases like the australian 1271 2:36:54 --> 2:36:59 one the new zealand one and so on i think that would be fantastic especially with your knowledge 1272 2:36:59 --> 2:37:07 and with your cleansing of the data and the ability to also even add deleted reports i think if there's 1273 2:37:07 --> 2:37:15 a tool out there that people uh like in this group can use to uh facilitate their own research uh it 1274 2:37:15 --> 2:37:21 would be fantastic uh how to combine your knowledge with theirs uh my question was uh i remember you 1275 2:37:21 --> 2:37:27 had a story also when you also put in a data and the data was changed was that from your uncle or 1276 2:37:27 --> 2:37:34 something thank you simon i wanted to i wanted to tell you that that story real quick but yes they 1277 2:37:35 --> 2:37:40 i i filled out the report on my uncle who who had a stroke he's still alive but he had a stroke 1278 2:37:41 --> 2:37:48 permanent disability and i videotape myself and i have a three video series you know when i got the 1279 2:37:49 --> 2:37:57 data back when i got my report back and it turns out that they that they changed my write-up they 1280 2:37:57 --> 2:38:03 omitted phrases and omitted words from my write-up and i think it kind of changed the complexity of 1281 2:38:03 --> 2:38:10 my firsthand account but more egregious than that they invented diagnoses and symptoms that did not 1282 2:38:10 --> 2:38:17 exist primarily psychosis and parkinson's that they said they that my that my uncle had and he 1283 2:38:17 --> 2:38:22 he didn't i went back and asked him like hey i didn't know you had parkinson's and he goes what 1284 2:38:22 --> 2:38:28 i don't have parkinson's so so when when i first got the report i thought oh my god they must they 1285 2:38:28 --> 2:38:34 must have went into his historical data and and retrieved it and put it in there because 1286 2:38:34 --> 2:38:39 because nowhere in my report did i ever say parkinson's or anything to that effect 1287 2:38:40 --> 2:38:48 so there you have it they invent they invented they they manipulated my report they they filed 1288 2:38:48 --> 2:38:55 a false report on my behalf and and as far as i know there is not a case like that anywhere 1289 2:38:56 --> 2:39:02 in existence i want to be the first one there's filed a false report on my behalf 1290 2:39:02 --> 2:39:07 because you know my uncle did not have my he did not he's not psychotic 1291 2:39:07 --> 2:39:12 and he doesn't have parkinson's but yet you guys somehow extracted that information and put it in 1292 2:39:12 --> 2:39:19 there i wonder how many thousands of reports they've done that to crazy thank you thanks for 1293 2:39:19 --> 2:39:30 bringing that up simon um so the next question is zoom user we don't know your name you're in a car 1294 2:39:30 --> 2:39:38 though hello oh hi it's nick honstead i don't know why you put me up the zoom 1295 2:39:39 --> 2:39:44 sorry who is it nick nick was cotton steady all right 1296 2:39:46 --> 2:39:47 your it's your go 1297 2:39:49 --> 2:39:50 hi nick 1298 2:39:53 --> 2:39:55 nick have you got a question for albert 1299 2:40:00 --> 2:40:11 uh he's breaking up uh i i sent you a message about uh tarso's hello uh nick you're breaking 1300 2:40:11 --> 2:40:18 systematically misclassifying reports here i'll turn off my video hold on yeah you're breaking 1301 2:40:18 --> 2:40:26 up now is this better yes that's better yeah okay hi sorry hey al um i i sent you a little 1302 2:40:26 --> 2:40:33 message about tarsals mom with a link to an interview with wane road she wrote a paper 1303 2:40:33 --> 2:40:40 where uh she showed that the hpv injuries were being misclassified uh roughly 1304 2:40:41 --> 2:40:45 i know about 50 of the time as not being severe 1305 2:40:46 --> 2:40:51 listen to the interview you can probably take a look at it reach out to her but she she knows 1306 2:40:51 --> 2:40:58 how to systematically go through and find the misreported reports absolutely that's what i've 1307 2:40:58 --> 2:41:04 been shouting that's what i've been shouting about for the longest time that there's severely you 1308 2:41:04 --> 2:41:10 know the 65 percent of the reports that are they're telling us are none of the above not serious that 1309 2:41:10 --> 2:41:17 is not true there's hundreds of thousands of reports that are super serious and very severe 1310 2:41:17 --> 2:41:24 and should be classified as such and i think that would change you know if in theory if we could 1311 2:41:24 --> 2:41:31 reclassify these that would change the complexion of what we're looking at when we're studying 1312 2:41:31 --> 2:41:39 toxic lots and um and uh you know dose you know uh adverse events by dose and everything i mean 1313 2:41:39 --> 2:41:46 there's so much uh severe adverse events sitting in that lowest level bucket of not serious that it 1314 2:41:46 --> 2:41:52 would just it would just it would be a game changer make it look worse what you know significantly 1315 2:41:54 --> 2:41:59 yeah i agree she has it laid out there you can do it's pretty systematic how to go ahead and 1316 2:41:59 --> 2:42:05 reclassify it to the standard that is supposed to meet the other the other point was steve 1317 2:42:06 --> 2:42:11 steve kirsch recently has the he keeps updating the survey but he's doing the right work which is 1318 2:42:12 --> 2:42:19 he's going out and validating bears which the cdc's never done um and it's damning the and 1319 2:42:19 --> 2:42:25 just so everyone knows uh for disabilities the the numbers are through the roof the urf is around 1320 2:42:25 --> 2:42:33 127 i think i put a quote in there um the under reporting factor is about 127 for disability 1321 2:42:33 --> 2:42:41 and where it's really showing up i think is inflation it's it's wiping out the labor 1322 2:42:42 --> 2:42:48 we're talking you know he he's backed it off to five percent but when i first looked at the data 1323 2:42:48 --> 2:42:54 i was seeing around 10 percent of people who get the vaccine are disabled permanently they can't 1324 2:42:54 --> 2:42:59 work i mean it's it's ursha and when you think about it and everyone can think about this 1325 2:42:59 --> 2:43:04 you know people in my own group you know they're retiring early like they've got kids going to 1326 2:43:04 --> 2:43:10 college and they just retired you know no explanation the evidence is all around us and i think that's 1327 2:43:10 --> 2:43:17 why they really can't cover it up and about bears um because it's a public reporting system 1328 2:43:17 --> 2:43:23 we get to audit it that's why they can't cover up everything and but fabulous presentation you're 1329 2:43:23 --> 2:43:27 fabulous presentation you're doing good work good to talk to you 1330 2:43:27 --> 2:43:34 oh thanks nick thanks yeah you know um just just put out there's kirsch in one of his latest surveys 1331 2:43:34 --> 2:43:42 said um using the polefish and he came out and he basically said the study was 770 000 deaths 1332 2:43:42 --> 2:43:51 in the united states 774 000 deaths and so i recalculated and i put in a message in in you 1333 2:43:51 --> 2:43:56 know in the comments section of that article and i said hey steve i go that just made your factor 1334 2:43:56 --> 2:44:06 41 go to factor 57 based on you know the conversion the conversion rate of that you know 774 000 deaths 1335 2:44:07 --> 2:44:13 i said it all right when are you gonna um you know what are you gonna ditch that 41 and go you know 1336 2:44:14 --> 2:44:23 get it you know uh adjust it to something more uh more uh realistic and uh he he actually responded 1337 2:44:23 --> 2:44:29 back and he said well uh you know i'm going for a bigger study with 8 000 people so because this 1338 2:44:29 --> 2:44:35 little study it could be off by three or four points and i said you know and i didn't write 1339 2:44:35 --> 2:44:40 back but i said yeah three or four points in either direction so it can you know the factor 1340 2:44:40 --> 2:44:48 could go up to 61 instead of 57 but anyways i keep you know i i sorry but i keep poking steve that 1341 2:44:48 --> 2:44:53 way i hope he doesn't get mad at me but i keep saying no because i'm in the camp you know that 1342 2:44:53 --> 2:44:58 whatever whoever says the highest i'm i'm coming in higher i'm like no it's got to be more because 1343 2:44:58 --> 2:45:06 i believe it this is eugenics i believe that this is depopulation i believe that this is poison 1344 2:45:06 --> 2:45:11 there's nothing good that comes from these these jabs nothing thank you nico 1345 2:45:12 --> 2:45:16 um gerard did you have another question or have you finished 1346 2:45:20 --> 2:45:21 you've muted uh gerard 1347 2:45:24 --> 2:45:32 gerard oh i can't hear me uh great um no no everything else that i've i i wanted to ask has 1348 2:45:32 --> 2:45:38 been asked by other peoples thank you very good thanks uh so glenn macco 1349 2:45:41 --> 2:45:42 you muted glenn 1350 2:45:46 --> 2:45:53 still muted 1351 2:45:53 --> 2:46:01 so 1352 2:46:04 --> 2:46:12 okay uh cj i don't know who cj is but um hi yeah it's a very quick question for albert so 1353 2:46:16 --> 2:46:19 yeah i missed the beginning of this so forgive me if i've missed something 1354 2:46:19 --> 2:46:25 the way that you described what your work is to me suggests that um well the question is this 1355 2:46:25 --> 2:46:31 what formal structure are you in that feeds this data to teams who can use it and weaponize it 1356 2:46:31 --> 2:46:38 what strikes me in this entire thing is that um there are information and service providers so 1357 2:46:38 --> 2:46:44 that might be heart in the uk or it could be af lds or something like that who although they 1358 2:46:44 --> 2:46:49 do weaponize some things because they turn them into law legal challenges what formal structures 1359 2:46:49 --> 2:46:57 have you got that people know that you are a data guy feeding standardized products to those teams 1360 2:46:57 --> 2:47:03 in an open accessible way so they can go every day see your latest report get it interpret it 1361 2:47:03 --> 2:47:09 because it is presented in lay accessible format with what you see explained in idiot speak that 1362 2:47:10 --> 2:47:15 says this data means this that those people can then weaponize that information in their fight 1363 2:47:15 --> 2:47:20 so whether that's a lawyer whether that's an activist doctor or whatever do you have those 1364 2:47:20 --> 2:47:26 structures in place and if not how can it be you know should it be is that dr cartland 1365 2:47:27 --> 2:47:33 yeah no thank you thank you for asking that question because sorry who is cj is it dr 1366 2:47:34 --> 2:47:41 i'm the pilot who was on last week ah yes now i get it i recognize the voice yes okay 1367 2:47:41 --> 2:47:50 very good yeah so so i was saying that um yeah so i um i've been i've been um trying to to um 1368 2:47:51 --> 2:47:59 get my dashboard which is which is authentic uh genuine data from from the cdc and and everything 1369 2:47:59 --> 2:48:08 everything matches uh except for the fact that i i go and populate the um the ages like the all 1370 2:48:08 --> 2:48:16 the ages the unknown ages that i could find i populate it um and where i start to deviate 1371 2:48:17 --> 2:48:24 is like those those 20 000 reports that i've found that are that are classified as none of the above 1372 2:48:24 --> 2:48:29 and i and i reclassify them those now i'm starting to deviate and starting to change the data now 1373 2:48:29 --> 2:48:37 it's not exactly the same as what comes out of the cdc however in hindsight doing it all again 1374 2:48:37 --> 2:48:43 and i will do it all again saying if i could do this again knowing what i do now i would have 1375 2:48:43 --> 2:48:50 i would have captured all my changes so that i have in my next dashboard my magnus opus 1376 2:48:51 --> 2:48:55 i'm going to have it so that you can toggle back and forth between 1377 2:48:55 --> 2:49:02 uh wizzy wig what you see is what you get unedited that this is how this is it matches 1378 2:49:02 --> 2:49:09 meta alerts and and open bears like that it has not been touched or altered in any way and then 1379 2:49:09 --> 2:49:17 switch over filter back to this is the cleansed data and what it looks like so you know you want 1380 2:49:17 --> 2:49:23 to see the the 26 percent of unknown ages here it is you filter this way and you want it now you want 1381 2:49:24 --> 2:49:31 to filter back to see all the populated ages you filter it and and so forth you know so on and so 1382 2:49:31 --> 2:49:39 on so basically i've been trying to wanting to reach out to these to peter macula and dr 1383 2:49:39 --> 2:49:48 valet's group or meta alerts themselves um uh you know the national vaccine institute 1384 2:49:49 --> 2:49:57 uh you know whatever non-profit and say hey um let's create fun you guys fund let's but let's 1385 2:49:57 --> 2:50:06 create another bears um system reporting system not not reporting to it but like a like an open 1386 2:50:06 --> 2:50:15 bears or a or a meta alerts a third one a new one the next evolution that has all the functionality 1387 2:50:15 --> 2:50:23 of open bears and meta alerts with the additional functionality of a interactive dashboard 1388 2:50:23 --> 2:50:29 which is what i which is what i got and that interactive dashboard in addition has the 1389 2:50:29 --> 2:50:37 the cleansed cleaned up data and or you know going back to the authentic unedited data 1390 2:50:38 --> 2:50:44 that's what um you know and if i can't find uh if i can't find somebody willing to like 1391 2:50:45 --> 2:50:52 you know hire me give me a job because i need one um i'll do it myself i'll do it i'll do it myself 1392 2:50:52 --> 2:50:59 just like i've been doing all of this work just as as the hill that i'm gonna die on right here um i 1393 2:50:59 --> 2:51:07 put my whole career on pause for this this is the hill i'm gonna die on um because yeah okay look 1394 2:51:07 --> 2:51:10 sorry to interrupt i understand that but my point is though that no matter whether you're going to 1395 2:51:11 --> 2:51:17 change your work or not your existing work is a form of this information service provision right 1396 2:51:17 --> 2:51:24 that's useless if people don't use it okay in any in any walk of life yeah if you're not the guy who 1397 2:51:24 --> 2:51:31 weaponizes it and uses the information you just provide it then nobody picks it up it never gets 1398 2:51:31 --> 2:51:38 used right so what is it that you need right in order to basically um get your stuff into the 1399 2:51:38 --> 2:51:46 hands of people who will and can use it well you know i just need people it's it's there in 1400 2:51:46 --> 2:51:53 tablo public i just need more people to to access that i've asked i've asked the rubin for meta 1401 2:51:53 --> 2:52:02 alerts hey create a um you know put a supplemental url link onto onto meta alerts and and you know 1402 2:52:02 --> 2:52:08 point it to mine and he said ah you know security reasons and i said hey let me help you create your 1403 2:52:08 --> 2:52:16 own you know under your control it doesn't have to be mine um i'll help you it'll be yours you know 1404 2:52:17 --> 2:52:23 you know old dog new tricks that's what i say old dog new tricks for that but um you know anybody 1405 2:52:23 --> 2:52:30 else on on high wire on on um on bobby kennedy's they should have a supplement you know a 1406 2:52:30 --> 2:52:38 supplemental link to mine or if not mine something similar and if not similar their own they should 1407 2:52:38 --> 2:52:44 make their own i've proved it i'm just a one horse operation pony show and this is what i've done i 1408 2:52:44 --> 2:52:49 mean come on we got to get to the we got to bring this to the next evolution so that's all that's 1409 2:52:49 --> 2:52:54 all i'm saying i'm saying this is a prototype this is what you guys this is what the cdc should have 1410 2:52:54 --> 2:53:02 done for us 15 years ago because tablo's been in existence for more than 15 years so albert what 1411 2:53:02 --> 2:53:10 you've just said now can you say can you um say to um tot conlinder yeah absolutely oh what would 1412 2:53:10 --> 2:53:17 you like me to say to him well he isn't here at the moment but but i'll fix a meeting yeah yeah no i 1413 2:53:17 --> 2:53:24 i'd love to i mean this to me and to me this is my this is my labor of love to give everybody 1414 2:53:24 --> 2:53:31 full informed consent because i don't think we're going to get help from the court systems quickly 1415 2:53:31 --> 2:53:39 i appreciate todd calendar and rands and um and siri and god bless them but it's it's a dog fight 1416 2:53:39 --> 2:53:46 that's going to take years um before that we got to get this out to the public so that listen to 1417 2:53:46 --> 2:53:52 this nobody's ever heard this before i want the kids i want the youngsters to be able to play 1418 2:53:52 --> 2:53:59 bears like a video game on their cell phone that's that's weird what does that mean yeah like a video 1419 2:53:59 --> 2:54:06 game yeah but albert do you really think that people want to do that most people don't right 1420 2:54:06 --> 2:54:12 the thing that you have to focus on is the three percent of people in the entire globe right who 1421 2:54:12 --> 2:54:16 are basically motivated to act that's it right that means that yeah there are some of those big 1422 2:54:16 --> 2:54:21 strategic hitters like you're talking about with with mccullar and whatever but their access and 1423 2:54:21 --> 2:54:26 capacity is close to zero because of what they're doing or what they're already involved in right 1424 2:54:26 --> 2:54:30 and so you've got a massive mountain to climb plus you're already duplicating to an extent 1425 2:54:30 --> 2:54:35 there's an overlap with existing open bears and stuff this is classic it problems right 1426 2:54:35 --> 2:54:40 so what you have to do is basically get on the strategic radar of players who use and consume 1427 2:54:40 --> 2:54:45 your data and weaponize it right but at the same time you have to basically make people aware that 1428 2:54:45 --> 2:54:52 you've got it but it's not going to go and be used in the hands of morons right who are the 95 percent 1429 2:54:52 --> 2:54:56 of people who are just getting on with their daily life it's the it's got to go into the hands of 1430 2:54:56 --> 2:55:02 weaponizers right who have the ability to act in law really that's that's ultimately where this 1431 2:55:02 --> 2:55:09 plays out yeah yeah and it's the question of it's a question of so for example laura sextro the unity 1432 2:55:09 --> 2:55:15 project one step away from robert malone uh you've got glenn macco in here with the uac uac project 1433 2:55:15 --> 2:55:19 depending on if he can see a use for it they're the players you need to get into i'm surprised 1434 2:55:19 --> 2:55:24 that you're communicating with steve kirch through his comments on his substack you should have him 1435 2:55:24 --> 2:55:30 in the phone line and just say steve this data set exists use it cross verify and then basically 1436 2:55:30 --> 2:55:37 build it into all of your own analysis that's the level you want to be at oh yeah i totally agree 1437 2:55:37 --> 2:55:43 and i'm i'm uh more than communication with steve you know we're here in silicon valley i've had 1438 2:55:43 --> 2:55:48 lunch with steve rubin owner of meta alerts or the creator of meta alerts but uh yeah it's just 1439 2:55:48 --> 2:55:54 getting you know it's just getting this this data out there and it's nothing proprietary to me it's 1440 2:55:54 --> 2:56:03 just the next evolution of meta alerts and open bears um you know so that you can visualize uh 1441 2:56:04 --> 2:56:12 you know this atrocity uh let alone the additional part that everything's like undercoded 1442 2:56:12 --> 2:56:21 and uh you know there's there's covid shots covid adverse of x filed as uh uh hpb jabs and mmr jabs 1443 2:56:21 --> 2:56:27 and uh you know bundling of deaths into one report i mean that's all extra extra stuff 1444 2:56:29 --> 2:56:41 thanks yeah uh so glenn macco hi sorry before uh my my screen started time out it blanked out i 1445 2:56:41 --> 2:56:47 couldn't even see where the mute button was to get on so i got that cleared up um so my comments 1446 2:56:47 --> 2:56:53 a couple uh jim thorpe left but uh i i had a conversation with him last october and i'll 1447 2:56:53 --> 2:56:58 reach out to him later today or tomorrow uh because i'm quite sure he's going to be able to play a big 1448 2:56:58 --> 2:57:05 part uh and the same thing to you albert uh but let me first lay out as a base position i believe 1449 2:57:06 --> 2:57:11 there's really really really clear evidence that the mrna vaccine treatments are obsolete 1450 2:57:12 --> 2:57:18 completely obsolete and and you just need to be making the correct comparison the correct 1451 2:57:18 --> 2:57:25 comparison of vaccine treatments with with no early treatment is what the medical system is 1452 2:57:25 --> 2:57:33 is recommending and that should be compared to uh the option of of dealing with early treatment 1453 2:57:33 --> 2:57:41 and competent doctors uh that and we we know those results are dramatically better so my point here 1454 2:57:41 --> 2:57:48 is the instead of comparison between different outlets of which the virus is a small count and 1455 2:57:48 --> 2:57:54 undercounted and and steve kirch has gone through all the cycles of doing the uh the more extensive 1456 2:57:54 --> 2:58:00 you've done a certain amount of work of being able to say even even the virus data has more in it if 1457 2:58:00 --> 2:58:05 you analyze it and if you go through it you can normalize it to better data that which would be 1458 2:58:05 --> 2:58:11 natural if anyone were doing a rational audit trail they simply aren't but even even with their 1459 2:58:11 --> 2:58:17 weight washing the data the virus data that's there the low the lowest level of which yours is 1460 2:58:17 --> 2:58:25 higher and steve's is higher uh the lowest level is still damning in comparison to early treatment 1461 2:58:25 --> 2:58:33 with competent doctors and normal respiratory handling so that's would be my comment is i think 1462 2:58:33 --> 2:58:38 we need to move everyone forward to do that kind of side-by-side comparison don't compare against 1463 2:58:38 --> 2:58:45 other players that that are using the the safety data but it's you know it varies that's that's 1464 2:58:45 --> 2:58:52 that's how suit for us health organization is able to ignore it by saying well you know it it has 1465 2:58:52 --> 2:58:59 been verified as 100 accurate okay but it has been verified as lower than the real numbers 1466 2:59:00 --> 2:59:04 and and so let's let's take the lowest number compare it against early treatment and say 1467 2:59:04 --> 2:59:10 voila if you are the public what would you pick it's going to be obvious so uh i i will get in 1468 2:59:10 --> 2:59:16 contact with you after this uh and uh i'm i'm working on some things with a group called mama 1469 2:59:16 --> 2:59:22 bears that that'll come be coming out shortly and i think it'll weave in very well thank you thank 1470 2:59:22 --> 2:59:28 you excellent general clinton help in any way i can is that it glenn you finished 1471 2:59:29 --> 2:59:37 yeah that's it thank you thank you yeah um i think uh that just leaves anna is that anna de buisere 1472 2:59:39 --> 2:59:49 yes hello hi hi hi yeah um sorry i can't put my video on at the moment so um hopefully you can 1473 2:59:49 --> 2:59:56 just hear me instead hello everybody um albert thank you so much um it's really really appreciated 1474 2:59:56 --> 3:00:04 i'm i'm one of the lawyers um who are looking for exactly that kind of presentation to weaponize 1475 3:00:04 --> 3:00:12 um because you know that's the this kind of data that's being so hidden is precisely why 1476 3:00:12 --> 3:00:16 you know the um culprits are carrying on getting away with it so the fact that you've done all this 1477 3:00:16 --> 3:00:22 work you know i really really appreciate it so what i'd like to do um is to ask you and um was 1478 3:00:22 --> 3:00:31 it jim thorpe who made that presentation earlier was it jim the doctor yeah what it is is that 1479 3:00:32 --> 3:00:37 as a lawyer speaking out um i'm now being investigated by the solicitor's regulation 1480 3:00:37 --> 3:00:44 authority um because apparently five members of the public have raised a complaint about me 1481 3:00:44 --> 3:00:51 um attending the vaccine clinics and making statements that uh there was a live criminal 1482 3:00:51 --> 3:00:57 investigation being conducted by the metropolitan police here in the uk now i've gone back to the 1483 3:00:57 --> 3:01:03 solicitor's regulation authority on several points one of which is that i say to them you don't have 1484 3:01:03 --> 3:01:11 jurisdiction of me in this um matter because i was not attending as a solicitor acting in um 1485 3:01:11 --> 3:01:18 conducting reserve legal activity i was attending uh firstly as a mom but secondly as an army officer 1486 3:01:19 --> 3:01:25 um and because on the evidence these are bio weapons this is bio warfare and that's being 1487 3:01:25 --> 3:01:32 deployed against the uk population and others obviously but my duties to the uk um and therefore 1488 3:01:32 --> 3:01:39 working with an extensive military network both here and abroad serving and veterans you know 1489 3:01:39 --> 3:01:45 we've been analyzing the intelligence and the evidence and our strategy was boots on the ground 1490 3:01:45 --> 3:01:54 we'll attend these clinics and demand that they are um closed down the the vials are seized uh 1491 3:01:54 --> 3:02:02 treated as you know evidence and the the clinic is a crime scene um on the basis that on i think it 1492 3:02:02 --> 3:02:09 was the 20th of december uh mark sexton retired policeman dr sam white philip highland solicitor 1493 3:02:09 --> 3:02:15 lois baylor solicitor spent six hours at the metropolitan police station uh in hammersmith 1494 3:02:16 --> 3:02:25 uh presenting their evidence copious amounts of evidence um and alleging uh around 20 crimes 1495 3:02:26 --> 3:02:33 including genocide crimes against humanity war crimes etc now the police issued a um 1496 3:02:33 --> 3:02:39 um well we were told it was a crime reference they later said it was an incident reference i 1497 3:02:39 --> 3:02:45 think no it was a crime number yeah okay so um but do you remember the the sort of controlled 1498 3:02:45 --> 3:02:52 opposition backlash later that they conducted but anyway i'll stick to the timeline so the um the 1499 3:02:52 --> 3:02:57 metropolitan police issued a crime reference um but only in respect to two of the alleged crimes 1500 3:02:58 --> 3:03:03 one of which was misconduct in public office and the second was gross negligence manslaughter 1501 3:03:04 --> 3:03:10 i mean you know unbelievable they could reduce 20 crimes down to two but they did issue a crime 1502 3:03:10 --> 3:03:17 reference in front and they gave uh you know they said that they would inform the chief constables 1503 3:03:17 --> 3:03:22 of all the police forces in the uk that there was now a live criminal investigation underway 1504 3:03:22 --> 3:03:28 and that they would make a public announcement they were given the contact details of a huge 1505 3:03:28 --> 3:03:34 number of experts who are willing to come forward with their evidence and other lawyers such as 1506 3:03:34 --> 3:03:44 myself um none of us were contacted as i'm as far as i'm aware um the public announcement was never 1507 3:03:44 --> 3:03:52 made uh when the apparently the chief of the chief constables of the police forces were told 1508 3:03:52 --> 3:04:00 apparently right but what happened was that on social media and in the newspapers there was a 1509 3:04:00 --> 3:04:06 massive uh opposition that you know counter offensive being run which was to deny first of 1510 3:04:06 --> 3:04:11 all that there was a crime reference being being issued then they were denying that there was a 1511 3:04:11 --> 3:04:16 live criminal investigation then they were trying to say that they'd even though they'd issued a 1512 3:04:17 --> 3:04:22 crime reference they were under absolutely no duty to conduct a live criminal investigation 1513 3:04:22 --> 3:04:28 so we veterans were analyzing this and saying right what we need to do is create that find out 1514 3:04:28 --> 3:04:32 what their standard operating procedures are and so the only way we're going to be able to do that 1515 3:04:32 --> 3:04:39 is go boots on the ground and confront them turning up with our evidence with the crime reference 1516 3:04:39 --> 3:04:46 with our notices of liability cease and desist orders etc and see what we could do um what 1517 3:04:47 --> 3:04:52 happened was we videoed that from around the country and identified their standard operating 1518 3:04:52 --> 3:04:59 procedures which was um yes well you're welcome to protest but um we're not going to do anything 1519 3:05:00 --> 3:05:05 to which we responded well we're not protesting this is a criminal investigation these are crimes 1520 3:05:05 --> 3:05:09 against humanity etc and the police would absolutely refuse to do anything about it 1521 3:05:09 --> 3:05:14 they denied being told that there had been a a crime reference they denied that the chief 1522 3:05:14 --> 3:05:21 constable's knew anything about anyway the whole thing is stonewashed stonewalled so what happened 1523 3:05:21 --> 3:05:27 was that um i went to one of the clinics on behalf of the community because they called me in very 1524 3:05:27 --> 3:05:32 distressed the parents and said please help us because we've already asked the police to come 1525 3:05:32 --> 3:05:38 in and close these clinics down the police were refusing um you know what can we do so myself and 1526 3:05:38 --> 3:05:44 a veteran and three members of the community uh asked the police to go and down the police refused 1527 3:05:44 --> 3:05:50 so we went along and of course the clinic then asked the police to come and we said please do 1528 3:05:51 --> 3:05:58 six police officers turned up including two cid and long story short they refused to to do it 1529 3:05:58 --> 3:06:05 etc and despite the fact that i brought an entire box full of evidence including the mod's manual of 1530 3:06:05 --> 3:06:12 armed law of armed conflict and told them these were amounting to grave breaches of the geneva 1531 3:06:12 --> 3:06:19 conventions all the police did and the staff was talk over me laugh at me you know etc and we had 1532 3:06:19 --> 3:06:26 to leave because they were going to arrest us unless we left so these complaints um you know 1533 3:06:26 --> 3:06:30 are now being investigated by the solicitor's regulation authority and i've said i was 1534 3:06:30 --> 3:06:36 anna can i just ask you did did you get the names from the solicitor's regular sra did you get the 1535 3:06:36 --> 3:06:42 names of the people who are allegedly complaining or they won't give me those details and that's 1536 3:06:42 --> 3:06:46 fair enough to be franked as even because it is a data protection issue and you should be able to 1537 3:06:46 --> 3:06:52 complain anonymously right i don't object i don't know that there was a complaining well 1538 3:06:52 --> 3:06:57 do you know yeah i well well because i well imagine there are because whilst most people 1539 3:06:57 --> 3:07:02 have supported what i've done um i have heard some you know on social media some people complaining 1540 3:07:02 --> 3:07:07 about what i did and it's fair enough you know some people would think it's overstepping the mark 1541 3:07:07 --> 3:07:12 but the point i think but it's one thing to complain about it but not that you know the point is that 1542 3:07:12 --> 3:07:18 that's why kafka wrote the trial because um he was accused of a crime but he wasn't told what the 1543 3:07:18 --> 3:07:27 crime was you're accused of breaching regulations by the sra who we know are not honest um and uh 1544 3:07:27 --> 3:07:36 you've got no proof that any uh any complaints has been made against you well um i'm prepared to take 1545 3:07:36 --> 3:07:42 well first of all i'm prepared to accept that there have been complaints against me because 1546 3:07:42 --> 3:07:46 it's likely there would be right it was a controversial not to the sra though anna 1547 3:07:47 --> 3:07:52 yes oh yes absolutely because when i did it in social media people were saying oh is this a 1548 3:07:52 --> 3:07:57 solicitor i'm going to complain to the sra about her you know i was expecting it steve and it was 1549 3:07:57 --> 3:08:03 you know so i i don't have a problem with someone complaining and in fact what it does is provide 1550 3:08:03 --> 3:08:09 me with a perfect opportunity to have some kind of investigation conducted because of course the 1551 3:08:09 --> 3:08:15 police are refusing to do that yes so the solicitors what's interesting is that the mod hasn't raised 1552 3:08:15 --> 3:08:21 a complaint against me and indeed i've asked all kinds of members of the forces whether they um 1553 3:08:21 --> 3:08:26 disapprove of what i did and they also know it was absolutely in the line of duty and appropriately 1554 3:08:26 --> 3:08:32 conducted even swearing because as an army officer if you're not being heard swearing is actually 1555 3:08:32 --> 3:08:39 part of the standard operating procedure which is interesting but anyway um so the mod hasn't 1556 3:08:39 --> 3:08:45 raised a complaint against me yet the sra themselves didn't raise a complaint against me 1557 3:08:46 --> 3:08:52 so it was members of the public so their hand has been forced and what this means is it provides 1558 3:08:52 --> 3:09:01 us with a golden opportunity to flood the solicitors regulation authority with our evidence as to why 1559 3:09:02 --> 3:09:07 you know first of all there should have been a live criminal investigation and indeed 1560 3:09:07 --> 3:09:12 the police then said on the 22nd of february well in fact we have conducted a live 1561 3:09:12 --> 3:09:18 criminal investigation there's nothing to see here so either they had conducted one in which 1562 3:09:18 --> 3:09:22 case they hadn't conducted it properly for the reasons i said previously because they hadn't 1563 3:09:22 --> 3:09:29 contacted anybody you know yeah exactly or they hadn't conducted one in which case you know they 1564 3:09:29 --> 3:09:35 should still have been conducting one so the point is we've been conducting it you know as the first 1565 3:09:35 --> 3:09:39 speaker i think was saying you know when we can't rely on anybody else to conduct this we're 1566 3:09:39 --> 3:09:45 conducting it so the solicitors regulation is authority is now an open door and what it also 1567 3:09:45 --> 3:09:52 means is that everything that we military people have been doing can now be served on the notice 1568 3:09:52 --> 3:10:00 at the sra and indeed my veterans are already writing their statements a lieutenant colonel 1569 3:10:00 --> 3:10:07 submitted his para has submitted his saying this is genocide by a warfare etc so i've now got loads 1570 3:10:07 --> 3:10:12 of veterans and serving people drafting their statements and affidavits and putting their 1571 3:10:12 --> 3:10:18 evidence together but it also means that we're copying it to the mad because under the army 1572 3:10:20 --> 3:10:26 framework you know we have a duty to report to our commanding officers any breaches of the law 1573 3:10:26 --> 3:10:33 of arm come they let me drop the elders up yeah and i see seconds okay go ahead 1574 3:10:34 --> 3:10:41 and as i say these are grave breaches of the juneva conventions this is very very serious 1575 3:10:43 --> 3:10:49 and the issue i've found is that nobody i've met within the military and nobody i've met in the 1576 3:10:49 --> 3:10:56 legal profession has actually read the juneva conventions none of them have read the judgments 1577 3:10:56 --> 3:11:03 from the new and both trials which i find absolutely gobsmacking but it explains to me 1578 3:11:04 --> 3:11:10 why people aren't recognizing that these are indeed grave breaches of the juneva conventions 1579 3:11:10 --> 3:11:15 these are prohibited acts of unlawful warfare but if you haven't read the law you don't know that 1580 3:11:15 --> 3:11:23 they are but even if you had read the law and if you even if you had read the law if you're in a 1581 3:11:23 --> 3:11:30 massive gnosis then you you're not going to do anything about it yeah but the point is the law 1582 3:11:30 --> 3:11:37 of armed conflict is the regulatory framework of the military and the police etc so what on earth 1583 3:11:37 --> 3:11:45 is their excuse for not actually having read the law on it exactly exactly well especially 1584 3:11:46 --> 3:11:51 especially when it's appropriate to do so when when things are pointed out even if yeah okay 1585 3:11:52 --> 3:11:59 yeah so yes please i would love the evidence out but you know that's absolutely fantastic what 1586 3:11:59 --> 3:12:05 you've been able to produce and anybody else who's listening in if you're prepared to help um you know 1587 3:12:05 --> 3:12:11 so that we can actually use the uh solicitors regulation authority investigation as our means 1588 3:12:11 --> 3:12:17 of getting some form of investigation kicked off yes that's very good and hopefully hopefully the 1589 3:12:18 --> 3:12:24 mod will join in and then we can confront them too yeah yeah so thank you for listening 1590 3:12:25 --> 3:12:35 thank you very much um louise caramaris if that's how you pronounce your name yes thank you very 1591 3:12:35 --> 3:12:43 much and i just wanted to commend um the work of albert and all everyone else here and also um thank 1592 3:12:44 --> 3:12:51 charles for hosting this forum and um thank you for inviting me um yeah so just i'm here in 1593 3:12:52 --> 3:12:59 australia and when we're on country and um and i guess i've got a question around you know given 1594 3:13:00 --> 3:13:06 the the corruption and the all the corporations that we now know all sort of linked up with um 1595 3:13:07 --> 3:13:14 with the cabal etc um and i know here in in this country you know we have not actually um 1596 3:13:14 --> 3:13:19 allowed the constitution to be studied i mean lawyers have actually been dumbed down in terms 1597 3:13:19 --> 3:13:27 of their law qualifications and what they've been studying so we're now recognizing um how 1598 3:13:27 --> 3:13:35 seeped um this has been being planned for such a long time um and the need to actually get 1599 3:13:35 --> 3:13:42 this out to the masses so my question is um and this is something that that there's been 1600 3:13:42 --> 3:13:49 exploration here is utilizing internet radio and um there's multiple channels that currently i know 1601 3:13:49 --> 3:13:56 exist um for music um and i'm just thinking about you know all the different ways 1602 3:13:58 --> 3:14:05 communications out there to the mass to the mass citizens like there's here in australia we've had 1603 3:14:05 --> 3:14:10 um a beautiful woman by the name of kim berger she's been doing an extraordinary job of analyzing 1604 3:14:10 --> 3:14:17 the raw data of our equivalent of ours um here and discovered um the manipulation of the data for 1605 3:14:17 --> 3:14:25 2022 so the only reliable data is 2021 and it's being cleaned cleaned up but she's actually been 1606 3:14:25 --> 3:14:31 going out meeting with small groups in various communities and getting getting that information 1607 3:14:31 --> 3:14:37 um out there and kind of you know doing that more that personal engagement because you know 1608 3:14:37 --> 3:14:44 really breaking it down for for people um and you know through that hopefully there's more and more 1609 3:14:44 --> 3:14:50 whistleblowers that can come out and um you know bring this to the attention of um the masses so 1610 3:14:50 --> 3:14:55 yeah so that was my question around internet radio and other communication mechanisms that can be 1611 3:14:55 --> 3:15:08 utilized yeah no absolutely i um the film jedi got me on her uh radio station um just the other 1612 3:15:08 --> 3:15:15 week and i've been on some some radio stations but yeah absolutely and kim burges i think simon 1613 3:15:15 --> 3:15:23 de wolf introduced me to her and i know that she was doing doing uh analysis for for australia but 1614 3:15:23 --> 3:15:29 yeah it'd be great to come together and i think um for those of you guys who don't know um you know 1615 3:15:29 --> 3:15:37 i've been working with dr frost and charles covis to you know this group basically to get us all to 1616 3:15:37 --> 3:15:44 get us analysis types together um to kind of brainstorm think tank you know and have one of 1617 3:15:44 --> 3:15:50 these one of these zooms going on but you know some of it uh public and probably some of it 1618 3:15:50 --> 3:15:56 private we're talking um because we got a lot of things going on in the background at least at 1619 3:15:56 --> 3:16:02 least i do that i can't you know that i haven't said anything about yet but has to do around foya 1620 3:16:02 --> 3:16:10 requests and and whatnot but um but yeah hopefully uh hopefully we'll that will come to fruition 1621 3:16:10 --> 3:16:18 where all of us together you know um talk about the next steps albert are you in touch with um 1622 3:16:19 --> 3:16:21 uh what's his name uh david wiseman 1623 3:16:24 --> 3:16:30 no i can't say that i am jessica rose you're in touch with her aren't you uh yeah i don't 1624 3:16:30 --> 3:16:36 talk to her that much or hardly hardly ever i talked to steve more than anything but steve 1625 3:16:36 --> 3:16:41 puts us together on but yeah we we know of each other's work for sure but yeah i'd love to 1626 3:16:41 --> 3:16:48 great craig pala cooper and alexandra latopova yeah we that yeah i've been working with them 1627 3:16:48 --> 3:16:56 uh um closely for the last couple of weeks especially um because now i guess i guess they 1628 3:16:56 --> 3:17:02 you know people are catching wind of you know with the my my dashboard that's just the deletions 1629 3:17:02 --> 3:17:07 you know but i got like four dashboards of all various things they just caught wind of the 1630 3:17:07 --> 3:17:14 deletion one so they need to catch the rest of them so what so albert what would be useful to 1631 3:17:14 --> 3:17:22 you a zoom call with what you know craig pala cooper and alexandra latopova so those two and then 1632 3:17:22 --> 3:17:32 uh jessica rose david wiseman there uh i want to Matthew Steve Kirsch Matthew Crawford especially 1633 3:17:32 --> 3:17:40 because that that whole dod uh data and i know that Matthew Crawford has been asking or wanting 1634 3:17:40 --> 3:17:48 somebody to do a foyer or foyers or advice on how to do for because i guess you know um getting 1635 3:17:48 --> 3:17:55 being successful doing a foyer there's like a trick to it and like i was saying there is 1636 3:17:55 --> 3:18:01 there is yeah you know how to how to request how to request is how to query a foyer basically 1637 3:18:01 --> 3:18:06 my currently it's a very um you can ask the best questions if you know the answers 1638 3:18:06 --> 3:18:14 yes yes exactly and i have this international foreign uh group that reached out to me that was 1639 3:18:14 --> 3:18:20 successful in their they have a bunch of foyers that they've gotten and and they they they know 1640 3:18:21 --> 3:18:27 bobby kennedy's group knows and highwire knows of this of this international group but i want to 1641 3:18:27 --> 3:18:34 make sure that all of us especially like Matthew Crawford and the dod military people know of this 1642 3:18:34 --> 3:18:39 this particular group that i'm talking about and that's what i want to come together and 1643 3:18:40 --> 3:18:46 i think we're you know we all need to get to meet each other and so we could all pull in the and 1644 3:18:46 --> 3:18:55 push in the same direction so you never have met then you data people new did you say wise men 1645 3:18:55 --> 3:19:03 jessica rose steve kirch matthew crawford part of cooper and lazipova those and you that's seven 1646 3:19:04 --> 3:19:10 yeah all of those people except for wise men all of those i've i i know of and i and i talked 1647 3:19:10 --> 3:19:19 then i quasi talked to through through emails and whatnot but yeah is there more oh and uh 1648 3:19:19 --> 3:19:28 uh josh gets gal is another one um that i communicate with yeah or that i have in the past 1649 3:19:28 --> 3:19:37 have in the past very good um okay oh the pan the pan data people i've done a i've done a 1650 3:19:37 --> 3:19:43 um an interview with them so all of those all of those people what about clare craig are you in 1651 3:19:43 --> 3:19:51 contact with her no i'm not wow you know she does great work yeah hopefully we're going to get her 1652 3:19:51 --> 3:19:59 next on tuesday yeah i i you know i think they're gonna like they're gonna love my dashboard 1653 3:20:00 --> 3:20:13 yep i think they're gonna love it yeah um oh dr ericson now shasta yes hi thank you um i just 1654 3:20:13 --> 3:20:19 keep coming back to that i think what you just said dr steven that we need these experts to work 1655 3:20:19 --> 3:20:26 together because it's so much data and as albert has said he's had to clean a lot of them up but 1656 3:20:26 --> 3:20:33 he's just a one one person and i really am concerned about the 835 000 none of the above 1657 3:20:33 --> 3:20:40 and so so is albert and none of the teams have have looked into those that i know of and we don't 1658 3:20:40 --> 3:20:48 even know what's in there it's 60 64 percent of the reports people don't even know what's in there 1659 3:20:48 --> 3:20:54 so if you were me and i were you i would i would love to see you get all the get that all of those 1660 3:20:54 --> 3:21:01 teams together and let's let's figure out what's needed to scour the entire bears so that it really 1661 3:21:01 --> 3:21:09 reflects what's true and if it takes getting some sql experts as albert said then figure out who 1662 3:21:09 --> 3:21:16 could fund that because then you have a real accurate uh board not to say it's not accurate 1663 3:21:16 --> 3:21:23 it is accurate it does reflect very very much it's just there's a lot 64 needs to be looked at 1664 3:21:25 --> 3:21:34 i was also shasta it's very difficult to work on your own um as albert is and and we as human 1665 3:21:34 --> 3:21:40 beings we solve things in groups small groups i agree i i think that if they were to bounce things 1666 3:21:40 --> 3:21:44 off each other they could also double check each other they could vet each other's data and we'd 1667 3:21:44 --> 3:21:50 have a you know a very synchronized information coming all together from around the world 1668 3:21:50 --> 3:21:58 from different viewpoints and this you know like i said this 835 000 none of the above that is 1669 3:21:58 --> 3:22:04 their automatic default button i think as albert said he thinks there's hundreds of thousands of 1670 3:22:04 --> 3:22:12 serious reports in there but just i think there's a even in you know even the people we're working 1671 3:22:12 --> 3:22:20 with there seems to be variability about how um how important people see this you know yes well 1672 3:22:20 --> 3:22:29 albert said that it would take i i think like 10 three or three to five ssql for a month or two 1673 3:22:29 --> 3:22:35 to go through it but yes i i get it people are dismissing it but if every single there's 1674 3:22:36 --> 3:22:39 they don't want to think about um what could be coming 1675 3:22:41 --> 3:22:46 yeah for us to really know i mean i don't think that's true of craig part of cooper and uh 1676 3:22:48 --> 3:22:53 alexandra latopovic they they're really great i think but yeah they want to know steve kersh is 1677 3:22:53 --> 3:23:00 also trying hard but you know jessica rose is very able and david wiseman too but they won't work 1678 3:23:00 --> 3:23:06 with anyone it seems well that's what i mean i'll say i'll say it right here in public you know i 1679 3:23:06 --> 3:23:11 think kersh you know instead of betting his million dollars i bet you a million here i bet 1680 3:23:11 --> 3:23:19 you a million there you know put out a fraction of that in fund the the veres you know 3.2.0 uh 1681 3:23:19 --> 3:23:24 it's just like open bears and meta alerts has all that functionality a third one a better one 1682 3:23:24 --> 3:23:32 and he could do it yeah make sure exactly just sorry have you suggested that to him albert 1683 3:23:33 --> 3:23:40 yeah yeah and nothing well yes he's been saying i'll pay you a million dollars if you do this and 1684 3:23:40 --> 3:23:46 you do that but he makes sure that he i don't know you know it looks like he maybe has he paid out a 1685 3:23:46 --> 3:23:54 million to anybody i don't think so i don't know i'm sure he's he's a very philanthropic i know 1686 3:23:54 --> 3:24:02 he's helped our church out so i'm not saying that he's you know he doesn't help anybody out but you 1687 3:24:02 --> 3:24:09 know for what we want for what he wants to get done and what we all want to get done and want to 1688 3:24:09 --> 3:24:15 see you know he's not the only show in town but i'm sure he could really if he wanted if he wanted 1689 3:24:15 --> 3:24:22 to see it and willing to do what needs to be done you know we could we could do it his vaccine 1690 3:24:22 --> 3:24:27 safe and to be fair to steve he's not the only millionaire in our midst i don't think right right 1691 3:24:27 --> 3:24:32 that's right he's not the only show in town that's what i you know no i didn't mean that i think i 1692 3:24:32 --> 3:24:38 think um you know so it's it always people say when money's needed they say steve kirch you know 1693 3:24:38 --> 3:24:44 and that's not fair either yeah because there are lots of people there must be you know we've got 1694 3:24:44 --> 3:24:52 1200 people in this group so there must be people with money yeah i don't think people realize 64 1695 3:24:52 --> 3:24:59 percent is in the none of the above that's more than half it's it's pretty it's a lot yeah so it 1696 3:24:59 --> 3:25:04 and albert can't do it and neither can the other teams on their own they just can't but together i 1697 3:25:04 --> 3:25:10 think once they figure out that it's needed then then the request to the people who could possibly 1698 3:25:10 --> 3:25:15 fund it could be there and i think would be really amazing so i would like to see it happen 1699 3:25:16 --> 3:25:23 and you know the messaging deriving from what albert has told us tonight um there might be 1700 3:25:23 --> 3:25:32 different opinions about what would um uh weigh heaviest with the public um you know i'm so that 1701 3:25:32 --> 3:25:37 might be important but i wonder what albert thinks you know what are the if you have to 1702 3:25:37 --> 3:25:41 pick five points albert what would you say the public would be most interested in 1703 3:25:43 --> 3:25:51 of the bears data yeah what would they find the most shocking um well one is that only initial 1704 3:25:51 --> 3:25:59 reports are are made public i mean that alone because of that alone knowing just pondering 1705 3:25:59 --> 3:26:07 that for a second and how many um severe adverse events are actually in there how many of those 1706 3:26:07 --> 3:26:15 people are now since dead you i would bet i would bet everything i have that you could easily double 1707 3:26:15 --> 3:26:23 double the death count right now that alone on that one thing alone that's just one um the the 1708 3:26:23 --> 3:26:30 second one is what is what even shasta is referring to and what el gato malo just said today on her 1709 3:26:30 --> 3:26:39 on their sub stack what steve refers to all the time el gato malo um that uh of the undercoded 1710 3:26:39 --> 3:26:43 all the undercoded severe adverse events that are basically you know what we're talking about under 1711 3:26:43 --> 3:26:51 none of the above people really knew how many there were right now we can only just point to 1712 3:26:51 --> 3:26:57 the 60 65 percent of the entire database it's none of the above but we we don't know how much 1713 3:26:57 --> 3:27:02 of that is really severe adverse events i i can point to 20 000 reports and say look at these i 1714 3:27:02 --> 3:27:10 got 20 000 right here that are super severe i got 60 that are dead i mean that's that's the starter 1715 3:27:10 --> 3:27:16 that's i mean if i could find 60 that are dead and they're in none of the not serious imagine how many 1716 3:27:16 --> 3:27:24 there there there are all together i think those two things excellent and also there is evidence 1717 3:27:24 --> 3:27:31 of a cover-up and albert how much money albert do you think it would take to audit the 64 percent 1718 3:27:35 --> 3:27:43 well hard hardware wise like a server and you know that that thing the computer a couple of laptops 1719 3:27:43 --> 3:27:48 a little a little office to so that two or three two or three people can sit at 1720 3:27:50 --> 3:27:59 you know i'll work for uh for 30 40 bucks an hour and a couple other sqls i mean on the cheap um so 1721 3:27:59 --> 3:28:07 maybe two two or three people for how how long and how much money do you think well well the initial 1722 3:28:07 --> 3:28:13 the initial setup you know um you know the the weekly i mean a weekly just a couple people 1723 3:28:13 --> 3:28:21 weekly because you know the data comes out weekly and you have to like scrub it weekly and um i think 1724 3:28:21 --> 3:28:27 that you know the automated part that's the reason why i can't do it because i would have to be 1725 3:28:27 --> 3:28:34 scrubbing weekly but once we got the sql person in there to write code once the code's written 1726 3:28:35 --> 3:28:41 it's gonna you know it's like it's like systematic the data comes out and it gets 1727 3:28:41 --> 3:28:47 translated and scrubbed and cleaned within minutes and okay so how much do you think it would cost to 1728 3:28:48 --> 3:28:52 scrub the 64 percent and set up the code for the weekly drops 1729 3:28:53 --> 3:29:02 well it's about a thousand it's about um a thousand a day for for three people at that rate you 1730 3:29:02 --> 3:29:10 mentioned um yeah and for so five and one week for a couple of weeks for a couple of weeks to get it 1731 3:29:10 --> 3:29:16 to get it to where it's automated and then back down to like one person just kind of monitoring 1732 3:29:16 --> 3:29:21 like like steve rubin steve rubin is like a one-man show he practically runs meta alerts all by 1733 3:29:21 --> 3:29:28 himself okay but like what i'm asking is for the six for the 835 000 to be audited how much do you 1734 3:29:28 --> 3:29:35 anticipate that might cost well how many weeks would it be for three people albert approximately 1735 3:29:37 --> 3:29:43 well i say that again dr frost how many weeks for three people as you suggested you know 1736 3:29:43 --> 3:29:49 weeks for three people as you suggested you know three people um how many weeks 1737 3:29:50 --> 3:29:58 maybe about maybe about two or three weeks you know go through the 835 000 no no i i mean i 1738 3:29:58 --> 3:30:05 can go through the 835 000 in the probably about probably about a week or two if that's all i did 1739 3:30:07 --> 3:30:12 but if one person but then i would be the question that you know the people are asking 1740 3:30:12 --> 3:30:17 it's if people who are who don't understand it don't don't know what question to ask is this 1741 3:30:18 --> 3:30:25 you're describing the automated t-sequel or sequel translation of a fixed data set that's 1742 3:30:25 --> 3:30:30 that's basically one query a dba you might be able to look at that data set and write a query in 1743 3:30:30 --> 3:30:38 hours okay if i'm you know i understand enough about that to know right so people are asking 1744 3:30:38 --> 3:30:44 the wrong question right how how technically complex is it to scrub the data and then and 1745 3:30:44 --> 3:30:53 then automate it a dba might be able to do that in hours could they well i think it'd probably 1746 3:30:53 --> 3:30:58 take a little longer who the person who does something close to that is bears analysis dot 1747 3:30:58 --> 3:31:06 info way my my my my my yoda his name is wane and he's bears analysis dot info if you see his 1748 3:31:07 --> 3:31:13 he's he's an sql expert and he's written the code for most for most of this stuff are you in touch 1749 3:31:13 --> 3:31:20 with him albert yeah absolutely he's yeah him more than anybody i'm in touch with so why do you ask 1750 3:31:20 --> 3:31:28 him to do it then well he's um he he actually has like he's like a really low profile because he has 1751 3:31:28 --> 3:31:34 like a he has like a job where he doesn't want anybody to know what he's doing and he works in 1752 3:31:34 --> 3:31:41 he works in pharma actually so you have you have the opportunity here but you mean he wouldn't do 1753 3:31:41 --> 3:31:49 it well i mean he would participate but i don't think he would do it like per se do it but this 1754 3:31:49 --> 3:31:56 this is the kind of it work that you can literally farm out on the open market to a dba yeah yeah 1755 3:31:56 --> 3:32:03 yeah i think we could so how much do you think that would cost i i don't know shasta i don't know 1756 3:32:03 --> 3:32:09 there you go nobody knows yeah nobody knows but one of the other things you can do to generate 1757 3:32:09 --> 3:32:14 funds is whatever your partial dashboard is at the moment you could literally open that onto the web 1758 3:32:14 --> 3:32:19 and then basically create a window of time where people who could go and use it for two weeks and 1759 3:32:19 --> 3:32:24 they go shit this is amazing i can do x y and z and then you shut it off and switch it to a 1760 3:32:24 --> 3:32:29 subscription system and then basically say so if steve kirch wants to use this he can pay 1761 3:32:30 --> 3:32:35 100 a week right and just switch it to a subscription system that way at least part 1762 3:32:35 --> 3:32:40 whatever you've got running at the moment becomes accessible to people who want to use it yeah 1763 3:32:41 --> 3:32:48 yeah it is accessible right now it is public it is it is public to the world right now as it stands 1764 3:32:48 --> 3:32:54 yeah okay so you could why would you introduce a subscription which would be a barrier because 1765 3:32:54 --> 3:32:58 basically he's trying to solve a problem which is he's saying he needs money and resources 1766 3:32:58 --> 3:33:03 to do some extra work well if you've already got a value-add product that basically people can use 1767 3:33:03 --> 3:33:08 and they go shit it's free now i know about it great i'll start using it and actually it only 1768 3:33:08 --> 3:33:12 cost me ten dollars a week to do yeah you know what i'll chuck some i'll chuck ten dollars at it 1769 3:33:12 --> 3:33:18 this is how substack works this is like how everything works today right that model that 1770 3:33:18 --> 3:33:22 model can get you thousands of dollars in a very short space of time if your product is of use to 1771 3:33:22 --> 3:33:27 people and you don't even have to do it you could even base donations around that rather than 1772 3:33:27 --> 3:33:34 a closed off system that has to have cash all of these are standard fixes like for the problem of 1773 3:33:34 --> 3:33:41 money and skills and if you need a dba just put it out into the open market for for a dba for a week 1774 3:33:42 --> 3:33:49 so it's easy enough to pick up a dba are you the problem is the problem is that you have to 1775 3:33:49 --> 3:33:56 set up a set of specifications for the data you want of course yeah but so if you but if 1776 3:33:56 --> 3:34:03 albert knows what needs to be done he'll have that won't he well okay so so if you're familiar 1777 3:34:03 --> 3:34:13 with microsoft bi it's enterprise level dashboarding del big trees own it people's name was john 1778 3:34:13 --> 3:34:20 fitzpatrick he reached out to me early on because highwire wanted to create their own exact 1779 3:34:20 --> 3:34:27 interactive dashboard i gave them all the historical files for the that i was collecting 1780 3:34:27 --> 3:34:34 for viruses that's what he needed and he then he disappeared right and that never came to 1781 3:34:34 --> 3:34:40 fruition they tried i'm sharing you i'm sharing this information with with you guys but 1782 3:34:40 --> 3:34:47 the uh but it never came to fruition it was him and dr mehan fidel big tree and dr jim mehan 1783 3:34:47 --> 3:34:54 coming together to do to to create this for whatever reasons it didn't it didn't come to 1784 3:34:54 --> 3:35:00 fruition but the point about they were using and i still have it that they were going to use 1785 3:35:00 --> 3:35:07 microsoft bi but to use microsoft bi now you're talking about subscriptions to microsoft for that 1786 3:35:09 --> 3:35:16 the thing about the thing about the way if you think about this problem from a from a very basic 1787 3:35:16 --> 3:35:20 level right and i was trying to fix this problem this was my problem in this group right now this 1788 3:35:20 --> 3:35:25 is what i would do i would come on here and i would sell the solution that i envisage and show my 1789 3:35:25 --> 3:35:31 partial work which you have done okay and then i would go and ask steven why don't you email all 1790 3:35:31 --> 3:35:37 1200 of your users and ask them for 10 pounds each right and sell those users sell sell all 1791 3:35:37 --> 3:35:42 your subscribers the reason why they should give 10 quid then you'd have 12 grand up to 12 grand 1792 3:35:42 --> 3:35:48 and then you spend that 12 grand by share get by contracting a dba with the right skills to 1793 3:35:48 --> 3:35:55 read your functional spec not as easy as that why not because there is about a thousand things we 1794 3:35:55 --> 3:36:02 need to do so which one do we go for with limited time you try it because this is one job this to 1795 3:36:02 --> 3:36:06 fix this one job with albert if he knows the functional spec for his thing right yes but 1796 3:36:06 --> 3:36:10 here and you need to seek an sql dba and then we all backed it you're assuming 1797 3:36:12 --> 3:36:15 you're assuming that we all agree that this is the most important thing to do 1798 3:36:16 --> 3:36:20 of course but then you would have a fun drive for any other task and then people can pick 1799 3:36:20 --> 3:36:25 and then if albert ends up with 600 quid that's 600 quid if he ends up in 12 grand great but you 1800 3:36:25 --> 3:36:30 could you could do this overnight right and you could do it by emailing your people with a list 1801 3:36:30 --> 3:36:34 of things that you're interested in doing and asking for 10 quid it's not it's not as easy as 1802 3:36:34 --> 3:36:40 that because there are some people who complain about the number of emails if i start using the 1803 3:36:40 --> 3:36:47 lists okay but look what what you're what you're immediately doing is finding a reason to not do 1804 3:36:47 --> 3:36:54 something no no no no you've got no idea if you want to get involved in organizing things then 1805 3:36:54 --> 3:37:00 email me and then you'll find out just how difficult it is what we're doing so it's not 1806 3:37:00 --> 3:37:07 it's not just albert who's working for nothing i'm working for nothing i spent 15 hours hours a day 1807 3:37:07 --> 3:37:14 working on this yeah yeah we all are but but where albert has if albert has a specific it problem 1808 3:37:14 --> 3:37:18 right where people he doesn't even know the cost of it but he knows what the functional work is 1809 3:37:18 --> 3:37:24 and i can imagine what it is i can imagine some of what it is based on previous experience that 1810 3:37:24 --> 3:37:28 that if he just writes a functional spec of what he needs the dba or the sql guy to do 1811 3:37:28 --> 3:37:33 like and he puts it into the open market he can place it anybody who's going to pay is anything 1812 3:37:33 --> 3:37:42 worthwhile in this present situation surely doesn't need paying so i would so craig part of cooper 1813 3:37:42 --> 3:37:49 and alexandra latopova the chart um can't you work with them albert or what about scott mccloughlin 1814 3:37:52 --> 3:37:57 so martin neal and scott mccloughlin they may be able to literally do something for three hours 1815 3:37:57 --> 3:38:05 that would get you past this hurdle uh yeah yeah you know them yeah i do yeah are they reliable 1816 3:38:05 --> 3:38:11 because this is a problem yeah well if they if they back what you're doing yeah they've got this 1817 3:38:11 --> 3:38:16 they potentially have got the skills to do it so i can drop scott a line to mark tonight yeah but 1818 3:38:16 --> 3:38:21 are they okay they don't have to be the ideal people but you you get what i mean so a lot of 1819 3:38:21 --> 3:38:27 people promise a lot and then nothing happens well yeah i mean i wouldn't be i'll be but i need a 1820 3:38:27 --> 3:38:35 functional spec to know what the job is well albert can help you with that yeah even maybe albert and 1821 3:38:35 --> 3:38:41 sasha and craig could brainstorm on what it would take and then and then have a very clear proposal 1822 3:38:43 --> 3:38:50 albert so why don't you speak to craig and sasha and yeah well that was because they're very they're 1823 3:38:50 --> 3:38:59 very driven those two yeah exactly and and you know i i will i will again i have you know it was 1824 3:38:59 --> 3:39:04 you know up to now it was always me that reached out that reached out first i mean they've known 1825 3:39:04 --> 3:39:16 of me for over a year i was a sasha and craig part of cooper um you know so uh but regardless 1826 3:39:16 --> 3:39:23 you know that that was all part of the the idea about having having you guys the the medical 1827 3:39:23 --> 3:39:30 doctors for covid ethics graciously host one of these zoom meetings where it's it's kind of like 1828 3:39:30 --> 3:39:38 semi-private so to speak in the sense that you know more of us techie types can talk talk shop 1829 3:39:39 --> 3:39:45 and talk about what what we need or what we want to do or what's it going to take or whatever and 1830 3:39:45 --> 3:39:54 then um you know and then push in that direction but um yeah i mean i i'd love i'd love to um you 1831 3:39:54 --> 3:39:59 know get my message or get my dashboard in front of anybody who's willing to see it and see the 1832 3:39:59 --> 3:40:08 value in it um you know i'd love to make it a subscription but at the end of the day it's like 1833 3:40:08 --> 3:40:14 gosh i want it you know i want the people in india you know the poorest people in india that have a 1834 3:40:15 --> 3:40:23 to see this stuff not that you know because it has to be free i mean med alerts is free and so is 1835 3:40:23 --> 3:40:28 open bears and look at how many people don't know about those yep you know it's like oh 1836 3:40:29 --> 3:40:35 i'm just thinking ray fernandez who spoke up a bit ago he knows a lot about computers i think 1837 3:40:35 --> 3:40:44 are you there ray yeah i am stu um look i actually write sql um but i'm self-taught 1838 3:40:45 --> 3:40:53 i am and i'm happy to have a look at it but as i said earlier i think that the i know what you're 1839 3:40:53 --> 3:41:03 talking about with visual bi um but i use visual studio um and and all of that is free um and you 1840 3:41:03 --> 3:41:10 can play around and get that working on a small scale and then if you really need to then you pay 1841 3:41:10 --> 3:41:17 for it but you could get it working with that oh yeah i think yeah no i i talked to another uh i've 1842 3:41:17 --> 3:41:24 been working with another guy who's who writes in writes code in python and uh and uh i you know i 1843 3:41:24 --> 3:41:30 saw the vision i saw the writing on the wall because he was using his python probability 1844 3:41:30 --> 3:41:38 something or another to help with the data cleansing of the lot numbers and i i quickly 1845 3:41:38 --> 3:41:45 went i quickly went and told team enigma about them you know and i said i envision this is like 1846 3:41:45 --> 3:41:52 this is part of what we need when we you know when when we pull down the data weekly from bears and 1847 3:41:52 --> 3:42:00 then run it through this algorithm of python the python algorithm or the sql you know whatever 1848 3:42:00 --> 3:42:08 that algorithm is that cleans up uh lot numbers that cleans up ages you know and i know uh you 1849 3:42:08 --> 3:42:17 know wayne from bears analysis dot info for the for the ages you know he wrote a code it's a big 1850 3:42:17 --> 3:42:22 code that runs through and he and he says it goes through the write-up and it's if it says if it finds 1851 3:42:22 --> 3:42:31 like 11 11 year old like and then the year year space old and then year dash old you know if it 1852 3:42:31 --> 3:42:38 finds that then it was okay so multiply those three you know those three different way 11 year old 11 1853 3:42:38 --> 3:42:44 dash 11 year dash old you know all the different combinations you could write 11 year old it's like 1854 3:42:44 --> 3:42:49 three or four you know of course it's garbage in garbage out yeah and that's why it's so important 1855 3:42:49 --> 3:42:55 to have pick lists and drop down boxes where you put the numbers in so for your age you have a box 1856 3:42:55 --> 3:43:00 with the age unit rather than allow people to type in it but that's normal standard i would have 1857 3:43:00 --> 3:43:07 thought bears did that well that's what i'm saying you would have thought but no it doesn't because 1858 3:43:07 --> 3:43:14 it's clearly written in the write-up how old the person is but yet the um the age field is is totally 1859 3:43:14 --> 3:43:23 blank so okay so to fix it um you you know this this code that wane wrote is basically for you 1860 3:43:23 --> 3:43:30 know 400 lines so you know with the age and it's like if if it's you know from from ages zero to 1861 3:43:30 --> 3:43:37 age 100 if it says it this way all the different ways it could say one year old two year old three 1862 3:43:37 --> 3:43:44 year old you know space and then you got a mixture of capitals lowercase and application yeah all of 1863 3:43:44 --> 3:43:52 but sql is designed to to do that sort of stuff why did you two um communicate are you in touch 1864 3:43:52 --> 3:43:59 with each other no yeah i know i i am with uh with wane yeah absolutely with ray oh with ray no i'm 1865 3:43:59 --> 3:44:06 not in touch with i'd love to be in touch with me okay um i will um i think i think you've flicked 1866 3:44:06 --> 3:44:13 your email through um somewhere um but i'll um or you can email me ray and i'll pass it on can i okay 1867 3:44:13 --> 3:44:18 i'll do that stephen that's easier for me ray ray can i ask you are you the mk ultra guy 1868 3:44:19 --> 3:44:27 mk ultra oh no i don't know who no don't know about mk ultra sorry um yeah okay 1869 3:44:27 --> 3:44:35 steven i'll email you sorry i'll email you yeah very good yeah how do you know that you um were 1870 3:44:35 --> 3:44:41 good on computers i don't know i'm wondering that myself because the only person that would know 1871 3:44:41 --> 3:44:46 that i've dealt with this sort of stuff because i did a bit of my own analysis that's how i got 1872 3:44:46 --> 3:44:52 involved in this with the australian staff um and i talked a little bit with charles covesse 1873 3:44:53 --> 3:45:01 very early on um and then there was yeah so maybe charles spoke to you i don't know i i i have no 1874 3:45:01 --> 3:45:06 idea simon i've also spent some time with oh yes i wonder whether the same he's not on the call now 1875 3:45:06 --> 3:45:11 is he he's dropped out now because he might help as well albert that's another possible 1876 3:45:11 --> 3:45:18 yeah absolutely i bet you simon would if if have you spoken you're in touch with simon aren't you 1877 3:45:18 --> 3:45:24 yeah no we talked extensively for a while and um you know when he showed me he took me on a carpet 1878 3:45:24 --> 3:45:32 ride through his program and i thought oh my god i see i see the vision so he's amazing yeah we 1879 3:45:32 --> 3:45:37 definitely need simon in the you know at the table when we're all when we're all discussing this 1880 3:45:37 --> 3:45:46 so ray if you just email me and remind me that's uh that i need to include simon um yeah okay all 1881 3:45:46 --> 3:45:52 right well i'll do that can i just reply to one of your um invites yeah that's fine yeah that's the 1882 3:45:52 --> 3:45:58 only way i have absolutely fine yeah okay i don't want anybody else but i'm getting tired um so have 1883 3:45:58 --> 3:46:07 we had enough now thank you thank you thank you nick wants to speak now do you nick have you got 1884 3:46:07 --> 3:46:14 a question there yeah it's quick everyone can leave it's mainly for al quick question um 1885 3:46:15 --> 3:46:23 when you do your dip is that uh are you just going with it it's echoing nick for some reason it's 1886 3:46:23 --> 3:46:31 echoing your end i think oh really oh that's odd that's better is that better okay um 1887 3:46:31 --> 3:46:32 um 1888 3:46:35 --> 3:46:42 is is the cdc maintaining the differences in the databases or do they just override it 1889 3:46:43 --> 3:46:51 let's say they override it each week they just give a new set of a new set of data yeah they 1890 3:46:51 --> 3:46:58 blow it away so that's that's kind of the challenge in that in that to figure out to figure out what's 1891 3:46:58 --> 3:47:03 what's been changed from the week before what's been deleted from the week before 1892 3:47:03 --> 3:47:09 um now you're tracking all those files absolutely yeah so i i messaged you you 1893 3:47:10 --> 3:47:17 what what might be a nice easy exercise and maybe someone could help even fund the effort for you 1894 3:47:17 --> 3:47:23 would be to just upload every single download from the fda on to get and then you can dip it 1895 3:47:24 --> 3:47:28 you get you get automatic tracking of what's been added what's been deleted 1896 3:47:28 --> 3:47:36 and you kind of get that for free that that's kind of a nice benefit you know what i you know 1897 3:47:36 --> 3:47:42 and i wouldn't be surprised if that's what um steve rubin does for meta alerts because i i rely 1898 3:47:42 --> 3:47:48 on his heavily he has a he has a bears wayback machine attached to his little not the official 1899 3:47:48 --> 3:47:53 machine but he calls it the bears wayback machine and he and it you know and right away 1900 3:47:53 --> 3:48:01 10 minutes after it's published on the cdc his meta alerts data is updated and even talking to 1901 3:48:01 --> 3:48:06 him he doesn't get he doesn't get it through the conventional way the way everybody else gets it at 1902 3:48:06 --> 3:48:13 the cdc and they wait for it to be published his system pings that the the system the cdc system 1903 3:48:13 --> 3:48:19 right at the time that it becomes available and and but but to answer your to let you know 1904 3:48:20 --> 3:48:28 that his his wayback machine captures every single uh change and deletion um because there's more 1905 3:48:28 --> 3:48:36 there's more than just just what's been deleted they actually do a small changes in in lot numbers 1906 3:48:37 --> 3:48:41 or genders or even vaccination dates they'll change it a little bit 1907 3:48:43 --> 3:48:51 so um yeah yeah i have to admit his stuff's pretty good it's pretty easy to basically output 1908 3:48:51 --> 3:49:00 a query to get the table which is pretty nice yeah he said he said something about seven different 1909 3:49:00 --> 3:49:08 pieces of software that he actually uses to get to get to get the exact number his his numbers 1910 3:49:08 --> 3:49:14 match to the report you know that every week matches to the you know whatever the exact 1911 3:49:14 --> 3:49:23 1.3 million that they report his is exactly the same but but yeah i'd like to i'd like to know 1912 3:49:23 --> 3:49:28 more about that so i'd love to learn more and i'm sure that's all the information that he has 1913 3:49:28 --> 3:49:33 to learn more and i'm sure that's all the stuff that we need you know that's going you know whether 1914 3:49:33 --> 3:49:38 it costs money or how much is it going to cost or all of that stuff you know that's what needs to 1915 3:49:38 --> 3:49:45 be talked about there's probably stuff that i don't even realize um you know there exists or 1916 3:49:45 --> 3:49:51 there's a cost involved okay um has everybody had enough now