I’ve made up my mind. Don’t confuse me with facts!

They’re ’Muricans. You gotta give them some slack. Thinking doesn’t come naturally to them.

I don’t know who is password, or why is password, or when is password, but I do know where is password, and it’s out there!

Jesus, Buddah, Spongebob! I don’t have time to be picky!

Testurteil: „Befriedigend”

A little joke for the Germans.

To save people a click: ISO is 51200 (in layman’s terms: holy shit it goes this high?) and they still exposed for 1/4s (somewhat normal for low-light photography) at an aperture of f/2.8 (gaping hole).

Suppose the average person p₀ has n acquaintances. Then a naive approach would say that each of p₀’s acquaintances (call one of them p₁) also has n acquaintances, leading p₀ with n² acquaintances of the second degree.

However, in a social network, many of p₁’s acquaintances are shared between p₀ and p₁. Let’s say that r⋅n (1/n≤r≤1) of p₁’s acquaintances are actually first-order acquaintances of p₀. The lower limit for r is 1/n because naturally one of p₁’s acquaintances is p₀ themselves.

This gives us n⋅(1−p)⋅n = n²⋅(1−p) as the number of second-degree acquaintances, if my math is mathing. Increase n for more extraverted people in the network, and increase p for more closely-knit networks.

To model the headline X % know someone who knows, we solve 1 / [n²⋅(1−p)] ≥ x where x is X% expressed as a fraction. Plugging in n=100 and p = 1/10 (I pulled these numbers out of my ass) and X=20% we get 1 / [100² ⋅ (1−.1))] = 1 / [ 10^4 ⋅ 0.9 ] = 1 / 900; again, if my math is mathing.

So this headline is true if about 1 in 900 people are in a relationship with AI.

No stars! They haven’t learned from their stupid mistake 60 years ago! /s

“Asterix” being some spin on the latin word for “star”, aster.

I wonder how many AI-relationships it actually takes to get 20% of a network to know one of them.

That’s why, in every new place I get, I strip naked and run around shouting GERONIMOOOO

I found that I do my morning exercise way more reliably than my afterwork exercise. I try to get 30 minutes in each session.

The biggest game changer, however, was not working 8 hours a day anymore. It also helps to have a boss who’s fine with delays, so you can extend the morning session if you feel like it.

The browser can never know what information is needed for a certain use case. So it needs to be permissive in order to not break valid uses.

For instance, your list does not include the things a user clicks on the website. But that’s exactly the info I needed to log recently. A user was complaining that dropdowns would close automatically. We quickly reached the assumption that something was sending two click events. In order to prove that, I started logging the users’ clicks. If there were two in the same millisecond, then it’s definitely not a bug but a hardware (or driver or OS or whatever) issue.

At least these ancient versions won’t try to update in the middle of a sea fight.

In this case the developer hasn’t designed this with the user in mind. Their rock is clearly too small for the user to sit on comfortably. Therefore it’s the dev’s fault.

TIL that the Higgs itself has mass.

On the contrary, websites are incredibly sandboxed. It’s damn near impossible to find out anything about the computer. Off the top of my head: Want to know where the file lives that the user just picked? Sure, it’s C:\fakepath\filename. Wanna check the color of a link to see if the user has visited the site before? No need to check. The answer will be ‘false’. Always.

First comment from the link:

Every time you open LinkedIn in a Chrome-based browser, LinkedIn’s JavaScript executes a silent scan of your installed browser extensions. The scan probes for thousands of specific extensions by ID, collects the results, encrypts them, and transmits them to LinkedIn’s servers.

That is very different from “searches their computer for installed software”