I think I've permanently confused KiCAD
it's reminding me that I need to connect this capacitor to Nothing
they have an ISA footprint but it doesn't show up when you search for "ISA" because they called it BUS_AT despite it being clearly called "ISA" since THE LATE 80s
The full AT bus, the 16bit extension to the 8bit PC/XT bus!
this will fix and/or cause all my problems
look at this.
it's the wrong 3D library, as that's just the footprint not the socket. I don't even know if here's a socket, so I'd like to look.
See the 3D model path? it's in Package_LCC.3dshapes
1. the source for kicad which points at this non-existent file
2. @TubeTimeUS's PlaidBib project which points at "Housings_LCC.3dshapes/PLCC-68_THT-Socket.wrl", which is a slightly different path!
https://t.co/6pHY6xZkCZ
Drinks available:
— foone (@Foone) March 24, 2019
Sprite
Diet Coke
\uff34 \uff28 \uff25 \uff36 \uff2f \uff29 \uff24 pic.twitter.com/t2FXeaJAyy
why
TWO HOURS LATER I'M DESIGNING A FLOPPY DISK CONTROLLER
Obviously I let a computer generate the randomness, I'm not a barbarian.
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More from Science
Variants always emerge, & are not good or bad, but expected. The challenge is figuring out which variants are bad, and that can't be done with sequence alone.
Feels like the next thing we're going to need is a ranking system for how concerning "variants of concern\u201d actually are.
— Kai Kupferschmidt (@kakape) January 15, 2021
A lot of constellations of mutations are concerning, but people are lumping together variants with vastly different levels of evidence that we need to worry.
You can't just look at a sequence and say, "Aha! A mutation in spike. This must be more transmissible or can evade antibody neutralization." Sure, we can use computational models to try and predict the functional consequence of a given mutation, but models are often wrong.
The virus acquires mutations randomly every time it replicates. Many mutations don't change the virus at all. Others may change it in a way that have no consequences for human transmission or disease. But you can't tell just looking at sequence alone.
In order to determine the functional impact of a mutation, you need to actually do experiments. You can look at some effects in cell culture, but to address questions relating to transmission or disease, you have to use animal models.
The reason people were concerned initially about B.1.1.7 is because of epidemiological evidence showing that it rapidly became dominant in one area. More rapidly that could be explained unless it had some kind of advantage that allowed it to outcompete other circulating variants.
2) The leading hypothesis is that the new variant evolved within just one person, chronically infected with the virus for so long it was able to evolve into a new, more infectious form.
same thing happened in Boston in another immunocompromised person that was sick for 155 days.
3) What happened in Boston with one 45 year old man who was highly infectious for 155 days straight before he died... is exactly what scientists think happened in Kent, England that gave rise to #B117.
Immunocompromised 45 year old suffered from #COVID19 for 155 days before he died. The virus was changing very quickly inside the man's body\u2014it acquired a big cluster of >20 mutations\u2014resembled the same ones seen in #B117 & #B1351. (NPR audio Part 1 of 2)\U0001f9f5https://t.co/7kWiBZ1xGk pic.twitter.com/ZJ7AExB78Y
— Eric Feigl-Ding (@DrEricDing) February 8, 2021
4) Doctors were shocked to find virus has evolved many different forms inside of this one immunocompromised man. 20 new mutations in one virus, akin to the #B117. This is possibly how #B1351 in South Africa 🇿🇦 and #P1 in Brazil 🇧🇷 also evolved.
2) NPR report audio part 2 of 2:
— Eric Feigl-Ding (@DrEricDing) February 8, 2021
Dr. Li couldn't believe what they found. "I was shocked," he says. "When I saw the virus sequences, I knew that we were dealing with something completely different and potentially very important." pic.twitter.com/HT3Yt6djFd
5) “On its own, the appearance of a new variant in genomic databases doesn’t tell us much. “That’s just one genome amongst thousands every week. It wouldn’t necessarily stick out,” says Oliver Pybus, a professor of evolution and infectious disease at Oxford.
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He has been wrong (or lying) so often that it will be nearly impossible for me to track every grift, lie, deceit, manipulation he has pulled. I will use...
... other sources who have been trying to shine on light on this grifter (as I have tried to do, time and again:
Ivor Cummins BE (Chem) is a former R&D Manager at HP (sourcre: https://t.co/Wbf5scf7gn), turned Content Creator/Podcast Host/YouTube personality. (Call it what you will.)
— Steve (@braidedmanga) November 17, 2020
Example #1: "Still not seeing Sweden signal versus Denmark really"... There it was (Images attached).
19 to 80 is an over 300% difference.
Tweet: https://t.co/36FnYnsRT9
Example #2 - "Yes, I'm comparing the Noridcs / No, you cannot compare the Nordics."
I wonder why...
Tweets: https://t.co/XLfoX4rpck / https://t.co/vjE1ctLU5x
Example #3 - "I'm only looking at what makes the data fit in my favour" a.k.a moving the goalposts.
Tweets: https://t.co/vcDpTu3qyj / https://t.co/CA3N6hC2Lq