These are eleven fake voter registrations. All attached to the same name, same date of birth, same address, and registered within the same 24 hour period. They were canvassed. Verdict: no such person. ALL ARE FAKE. These are not a clerical error. This is a group of 11 phantoms inserted just before the 2020 election in NY. I asked for the paperwork for these and about a million other records like them, curious whether there is any paperwork. The county refused. Other counties provided them, but not New York City. Next question: The voter rolls distributed to researchers don't include driver's license numbers or social security numbers. Do these records, and others like them, have Driver's ID or SSN? These are fake entries. If those numbers are in the records, are they fake also, or do they belong to real people (identity theft)? This, and many other registration problems like it, were reported to AG James, the entire NYSBOE, and other NY officials. Was anything done? Can the @FBIDirectorKash or @CivilRights @HarmeetKDhillon please check on this.

Mail-in registration=fraud Early voting=fraud Mail-in ballots=fraud Corrupted voter rolls=fraud Unauditable tabulators=fraud Late ballots accepted=fraud Non-citizen voters=fraud Internet/LAN connections=fraud Foreign-printed ballots=fraud Foreign components in election machinery=fraud Blocking view of oservers=fraud Refusal to hear election cases=fraud Multi-day counting=fraud Touch screen voting=fraud Saying "safe and secure elections"=fraud Algorithmic manipulation of voting=fraud Algorithmic record tagging=fraud J6=fraud I'm kind of thinking there are more than a few fraud vectors in our elections, none of which seem to be defended against. It's almost as if there are people within the system who want to commit fraud.

There was a time not so long ago when coins were used to buy things. These days, the smallest meaningful unit is a full dollar, and sometimes even more. My lunch today cost nineteen dollars, and it was just two nori rolls, the equivalent of two quarter pounders in 1976, which would have cost a dollar for both of them. Once, my mom dated a man in Santa Cruz who let me and my friend Geoff split all the coins we could find in his car if we washed it for him. It held slightly over twelve dollars. Geoff got six, and so did I. For that much money, I could buy thirty comic books. That's a stack about five inches thick. You couldn't buy that many comics in one trip to the 7-11. I bought fifteen over a couple of weeks and saved the rest for emergencies. On another occasion, my mom gave my sister Debbie and me fifty cents for ice cream at Swensen's, so we walked over there from about a mile and a half away. In the parking lot, I saw a set of keys sticking in the door lock of an Alfa Romeo sports car. Debbie wanted to leave the keys in the door because they weren't ours. I wanted to take them in to the Swenson's to find the owner in case someone else saw the keys and stole the car. I was older, so I won the debate. Inside, we found the owner, and he gave each of us a silver dollar as a reward. That was enough to get a paperback at the bookstore on the way home, which I did. I don't know what Debbie did with her reward, but she was very pleased with it. One time, my mom hadn't had any money for a little while, but we were hungry. She drove Debbie and I around town, checking every payphone we found for change in the coin return slots. After an hour, we'd scrounged thirty-five cents. Enough for a cheeseburger at McDonald's. We went there, and split it three ways.

This image compares the voter rolls of California's District 28 with New York's Nassau County. There are problems with both, but what should be obvious is that one or both of them are experiencing an X-Files level statistical anomaly. These rows tell us a few things: 1. The vast majority of all first and last name combinations are unique, not common names. 2. Almost all name plus age combinations are unique. Meaning, name + age is much more discriminatory than the average county clerk, reporter, or citizen realizes. 3. In Nassau, all but one of the Name/Age combinations that repeat also match on Date of Birth (DOB) — 166 of 167. 4. In District 28, only 5 of 2,019 repeating Name/Age combinations also match on DOB. The Nassau and District 28 data are the opposite of each other. In District 28, under 1% of repeated Name/Age matches also match on DOB. In Nassau, over 99% do. What does this tell us? ONE OF THEM, at a minimum, is artificial.