A vector looks like an arrow; anyone studying math or physics in college learns about these. Bivectors are important too - though less widely taught. You can create a bivector by taking the 'wedge product' u ∧ v of two vectors u and v, and you can visualize this as the parallelogram shown at left here. But in fact, an ellipse or any other flat surface with the same area lying in the same plane is just as good a picture of this bivector. Further, you can add bivectors, so there are bivectors like u ∧ v + w ∧ z that you can't picture as just a single surface. Thus, you need to be good at the al…
Any piece of software that depends on open source (which is almost every piece of software) has a network of human beings who are potential attack vectors - everyone with publishing rights to any of the packages in the dependency network for that software.
and I think most people experience it as like flat triangles and squares in a biology textbook where it's like you know there's an arrow between like this triangle and this triangle is just like doesn't make any conceptual sense and is like confusing and annoying
Shopkeeping by Peter Miller. This was recommended from a couple of different vectors — pretty sure one was Robin Sloan. Lots of resonance to my work here and how I think about it (and want to think about it). (A-)
FLOP is the vector that the government has cared about historically, but the other two vectors are arguably just as important. And especially when we come to this new paradigm, which the world is only just learning about over the last six months: reasoning.
For the most part, it’s like these names are just utterly arbitrary, so you have no thing to latch on to. It’s not really a thing that our brain does very well to learn meaningless, arbitrary stuff. So what you need to do is build connections somehow, visualize a connection, and sometimes it’s obvious or sometimes it’s not.
Um but nobody has done research on the positive side of these heuristics. And so, I think that's a bias on the part of the decision bias researchers that they're only looking at the down arrow. How do these heuristics get us in trouble? And they're not looking at the strength of these heuristics.
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