Topic: Wall Observer BTC/USD - Bitcoin price movement tracking & discussion - page 3688. (Read 26731515 times)

Non-Fruitful Twat

I agree completely. The new investors in the last 2 years failed miserably, with the exception of Michael Saylor. I thought they've learned the lesson after the first crash a year ago. I mean, it took only 3-4 months to return to 65K after the crash to 28K. It was obvious that this price was achievable soon again. Then why should someone sell at 30K? But no, they did it again and again. Not to mention all those "crypto experts" who mocked BTC maximalists. I doubt that they will learn terra's lesson and quit investing in shitcoins. These people think they are smart, but they are not. They rely on their personal experience and achievements, but are not used to doubt every project and the man behind it. The cost of this lesson will be devastating one day.



Meanwhile, only the OG hodlers continue to accumulate silently. They sell only what is needed, when it is needed. The hard thing is to plan and keep the balance between the present needs and the desire to have as much BTC as possible. The inflation is making this task even harder, but even if there are some mistakes on the way, crashes like this can be used to correct them. I'm content with my current balance, but if the bear market brings the price below 25K, I will be even happier to reinvest in BTC for even better balance.

Explanation

I thought we all agreed on using "Fuggable" according to the new spelling rules.

I would say you have a lot of accuracy here. The key question is what is a pandemic.

here is the world of meter chart.

let us all pretend it is perfectly correct. all I care about is deaths per million number



https://www.worldometers.info/coronavirus/country/us/


-------State-------Deaths/1M pop

1   Mississippi      4,186   
2   Arizona      4,153   
3   Alabama      4,003   
4   Tennessee      3,846   
5   West Virginia   3,846   
6   Arkansas      3,783   
7   New Jersey   3,776   
8   Louisiana      3,720   
9   Oklahoma      3,634   
10   New Mexico   3,628   

11   Michigan   2,472,596      36,140      2,364,850   71,606   247,585   3,619   25,085,890   2,511,890   9,986,857   [1] [2] [3] [4]    [projections]
12   Georgia   2,530,918      38,086      2,464,921   27,911   238,374   3,587   24,947,545   2,349,680   10,617,423   [1] [2]    [projections]
13   New York   5,451,816      69,098      5,271,761   110,957   280,248   3,552   109,308,407   5,618,941   19,453,561   [view by county] [1] [2] [3]    [projections]
14   Kentucky   1,336,858      15,797      1,297,871   23,190   299,229   3,536   13,675,281   3,060,940   4,467,673   [1] [2] [3] [4]    [projections]
15   Indiana   1,713,834      23,636      1,572,463   117,735   254,572   3,511   19,769,370   2,936,531   6,732,219   [1] [2] [3]    [projections]
16   Nevada   724,364      10,798      701,439   12,127   235,171   3,506   6,875,532   2,232,203   3,080,156   [1] [2]    [projections]
17   Pennsylvania   2,849,663      44,814      2,760,545   44,304   222,595   3,501   26,188,995   2,045,697   12,801,989   [view by county] [1]    [projections]
18   South Carolina   1,481,975      17,869      1,447,995   16,111   287,834   3,471   16,394,775   3,184,247   5,148,714   [1] [2] [3] [4]    [projections]
19   Florida   6,041,987      74,178      5,842,331   125,478   281,314   3,454   57,555,629   2,679,781   21,477,737   [view by county] [1] [2] [3] [4]    [projections]
20   Rhode Island   381,271      3,552      361,775   15,944   359,907   3,353   7,891,500   7,449,302   1,059,361   [1] [2]    [projections]
21   Missouri   1,428,117      20,382      1,390,754   16,981   232,690   3,321   14,632,189   2,384,091   6,137,428   [1] [2] [3] [4] [5] [6] [7] [8]    [projections]
22   Ohio   2,724,041      38,550      2,647,566   37,925   233,041   3,298   22,272,602   1,905,416   11,689,100   [view by county] [1] [2]    [projections]
23   South Dakota   238,635      2,917      234,664   1,054   269,748   3,297   2,176,218   2,459,951   884,659   [1]    [projections]
24   Montana   275,113      3,381      270,881   851   257,409   3,163   2,789,012   2,609,534   1,068,778   [1] [2] [3] [4]    [projections]
25   Wyoming   157,031      1,817      154,406   808   271,324   3,139   1,396,443   2,412,823   578,759   [1] [2]    [projections]
USA Total   84,174,521      1,026,527      81,207,019   1,940,975   254,302   3,101   1,016,652,419   3,071,433          
26   Connecticut   779,460      10,914      730,020   38,526   218,625   3,061   15,534,710   4,357,212   3,565,287   [1] [2] [3]    [projections]
27   Texas   6,861,934      88,677      6,693,059   80,198   236,652   3,058   64,567,227   2,226,772   28,995,881   [view by county] [1] [2] [3]    [projections]
28   Iowa   866,696      9,555      844,674   12,467   274,699   3,028   8,492,575   2,691,723   3,155,070   [1] [2] [3] [4] [5] [6]    [projections]
29   Delaware   267,265      2,931      257,113   7,221   274,466   3,010   1,026,007   1,053,651   973,764   [1] [2]    [projections]
30   Illinois   3,209,341      38,005      3,044,371   126,965   253,266   2,999   57,898,053   4,569,040   12,671,821   [1] [2] [3] [4] [5]    [projections]
31   Kansas   778,045      8,691      762,294   7,060   267,065   2,983   2,269,976   779,173   2,913,314   [1] [2] [3] [4] [5] [6] [7] [8] [9] [10]    [projections]
32   North Dakota   242,462      2,272      239,113   1,077   318,166   2,981   618,121   811,116   762,062   [1] [2]    [projections]
33   Massachusetts   1,809,309      20,393      1,738,970   49,946   262,504   2,959   44,490,508   6,454,913   6,892,503   [1]    [projections]
34   Idaho   447,913      4,933      439,977   3,003   250,642   2,760   2,925,021   1,636,774   1,787,065   [1] [2] [3]    [projections]
35   Wisconsin   1,639,365      14,502      1,578,666   46,197   281,560   2,491   16,279,372   2,795,974   5,822,434   [1] [2] [3] [4] [5]    [projections]
36   Maryland   1,051,938      14,520      1,003,407   34,011   173,998   2,402   20,621,268   3,410,910   6,045,680   [1] [2]    [projections]
37   Virginia   1,733,188      20,317      1,661,419   51,452   203,056   2,380   19,172,533   2,246,206   8,535,519   [1] [2]    [projections]
38   North Carolina   2,691,805      24,588      2,640,452   26,765   256,654   2,344   26,356,209   2,512,967   10,488,084   [1] [2] [3] [4]    [projections]
39   California   9,349,698      91,070      9,009,110   249,518   236,628   2,305   164,471,446   4,162,546   39,512,223   [view by county] [1]    [projections]
40   Colorado   1,407,405      13,269      1,340,900   53,236   244,395   2,304   17,608,421   3,057,689   5,758,736   [1] [2]    [projections]
41   Minnesota   1,479,047      12,849      1,447,550   18,648   262,259   2,278   19,479,012   3,453,951   5,639,632   [1] [2] [3]    [projections]
42   Nebraska   482,305      4,234      474,103   3,968   249,330   2,189   5,506,012   2,846,355   1,934,408   [1] [2] [3] [4] [5]    [projections]
43   District Of Columbia   144,675      1,341      137,819   5,515   204,995   1,900   3,067,883   4,346,989   705,749   [1] [2] [3]    [projections]
44   New Hampshire   316,691      2,500      309,664   4,527   232,911   1,839   4,764,871   3,504,326   1,359,711   [1] [2] [3]    [projections]
45   Oregon   738,830      7,560      695,963   35,307   175,172   1,792   11,347,601   2,690,448   4,217,737   [1] [2] [3]    [projections]
46   Maine   254,427      2,335      236,442   15,650   189,276   1,737   5,218,447   3,882,161   1,344,212   [1] [2]    [projections]
47   Alaska   247,865      1,235      240,756   5,874   338,824   1,688   4,107,614   5,614,985   731,545   [1]    [projections]
48   Washington   1,530,430      12,791      1,458,797   58,842   200,979   1,680   13,478,234   1,769,983   7,614,893   [view by county] [1]    [projections]
49   Utah   938,864      4,761      925,511   8,592   292,850   1,485   9,406,861   2,934,181   3,205,958   [1] [2] [3]    [projections]

50   Vermont      1,038   
51   Hawaii      1,013   






52   Puerto Rico   593,513      4,250      484,974   104,289   175,236   1,255   4,175,246   1,232,748   3,386,941   [1] [2] [3] [4] [5]    
53   Guam   48,496      361      47,902   233         450,478         [1] [2] [3]    
54   United States Virgin Islands   18,037      113      16,726   1,198         396,103         [1] [2]    
55   Northern Mariana Islands   11,305      34      11,262   9         107,810         [1] [2]    
56   American Samoa   5,999      30      5,796   173         16,082         [1] [2] [3] [4]    
57   Veteran Affairs   635,180      21,926      604,634   8,620         8,626,902         [1] [2]    
58   US Military   627,042      687      601,100   25,255                  [1] [2] [3]    
59   Federal Prisons   69,144      302      68,671   171         129,677         [1]    
60   Navajo Nation   53,643      1,770      51,737   136         524,926         [1] [2] [3]    
61   Grand Princess Ship   122      7      115   0                  [1]    
62   Wuhan Repatriated   3            3   0         3            
63   Diamond Princess Ship   46            46   0         46            
Total:   84,174,521      1,026,527      81,207,019   1,940,975   254,302   3,101   1,016,652,419   3,071,433       





So why do the top 10 deaths states have about 4 in 1000

and Vermont and Hawaii have about 1 in 1000

worse yet why isn't that big news.

Personally I would think

I think since it is the weekend this sagging price is not as scary as it might be during the week.  We are starting to clear out the mempool, and as long as we do not re-test that V I think we still have a decent chance.

But it is fairly disappointing that we are not bouncing the way we should be if that V was a bottom...  

I really don't think we can go LOTS lower... and I still hope this is the bottom but it aint acting like it.

However, it is hard to imagine we do not see lots of support for Bitcoin at these prices...  But again the volume DOES scream weekend...

I might regret not leaving myself more $$$ to spend.





I actually slipped on my vote here... I picked a zone which i think is MOST unlikely.

After this post I will observe this threads preference not to talk about this topic...

But you are talking about data you know nothing about and drawing conclusions in your imagination.  I helped set up covid ICUs at multiple hospitals.  I have been on those units, full of dying people at times during the pandemic.  (The one you said did not exist)

The point that the pandemic is exaggerated, and was used to control the greater population, and that there were lots of dirty data in some settings is something I not only agree with, but probably know more about than you.

I just get sick of the constant insinuation that the people who i saw struggling to breathe and dying in hospital beds did not die of Covid, and the other insinuation that the good people who worked double shift after double shift to save as many of them as possible were in on some giant healthcare scam bug the SHIT out of me.

I know those nurses.  Including the ones that would not touch the vaccine with a 10 foot pole because of what they saw.

Last time I say it.  Nuance is important.

There are dictators all around the world that would have bought today, Marilyn Monroe 3D pussy just because they wanted to understand how power and greatness could be drawn towards a piece of rubbery-leathery dangling piece of human skin.      (Or maybe not human, she might have been some Pleia.indian.jew)    

Explanation

Didnt say there wasnt a virus. Corona virusses have existed for millenia and people have been dying from it since they first started animal husbandry.

Pandemic is the word I attacked.

Also your "dash boards" are full of false info. People falling down stairs and breaking their neck are counted as Covid death. Likewise people dying after taking covid shots (note: they arent vaccines), but before 2 weeks have passed since taking the second dose, are counted as "unvaxxed Covid death".

The data set has been dirty from the start. You've been visualizing false data. Garbage in garbage out.

That's weird.  I know three people who died from the imaginary virus, and one who it gave a heart attack to.  All people in risk groups.  Also as a hospital data professional I was in part responsible for building the dashboards that tracked the progress of the imaginary disease.

Nuance is important.  There is no doubt that the greater contagion was the mind virus you mention.  But the design of that is to polarize us into extreme groups so we can be easily controlled.

When I was a kid you could also divide the population into two groups.  There were the "popular" kids who knew what to wear, and what music was currently cool, and what Saturday Night Live punchline should be currently said.  And then there was the other group, who was also aware of all those things but rejected them. We (I was in the latter group) made sure NOT to wear those clothes, made fun of the music and mocked the Saturday Night Live one liners.

It took me a while to realize we were really all playing the same game.

Explanation

The only pandemic that occured is a serious affliction of the mental capacities of humans and it seems far from over 😅

Luckily some of us seem to be immune to its effects

Non-Functioning Thing

I hear it makes a great beer coozie...

Non Fuckable Thingy?

Explanation

Can't wait for the parodies. Flies instead of butterlies, butterflies tumbling and going to the ground, dying...
Much potential in this NFT.

You are definitely resistant to irony.