verginer

Like others have pointed out, it reads like a horoscope. The example images give a reasonable approximation of what I'd profile them as too, but after trying a few of my own picture it's clearly BS. Garbage in, garbage out.

This "use LLMs as psychometric/political polling substitutes" idea seems to have jumpstarted a weird cottage industry of "synthetic" surveys. The model is pattern-matching on superficial visual cues and dressing it up as insight (I have a long beared and hence I vote for the green party).

Nate Silver put it well recently: [AI polls are fake polls][1].

An LLM inferring personality from a photo is even further down that chain of abstraction. That's not profiling, it's stereotyping with extra steps.

[1]: https://www.natesilver.net/p/ai-polls-are-fake-polls

I finally found a job for my Raspberry Pi 1 Model B from 2012. It’s been sitting in a drawer for years, but about a 2 years ago added it to my Tailscale network as an exit node.

It’s a single-core 700MHz ARMv6 chip with 512MB of RAM. It's a fossil—a Pi 5 is 600x faster (according to the video). But for the 'low-bandwidth' task of routing some banking traffic or running a few changedetection watches via a Hetzner VPS (where the actual docker image runs), it’s rock solid. There’s something deeply satisfying about giving 'e-waste' a second life as a weekend project.

Good observation, but they also acknowledge: > there are considerations inherent in our use of the case insensitivity shortcut, which trusts the YouTube search engine to provide all matching results, and which oversamples IDs with letters, rather than numbers or symbols, in their first ten characters. We do not believe these factors meaningfully affect the quality of our data, and as noted above a more direct “brute force” method - even for the purpose of generating a purely random sample

In short I do believe that the sample is valuable, but it is not a true random sample in the spirit that the post is written, there is a heuristic to have "more hits"

Admittedly, I did not read the paper linked. But my point is not about google doing something funny. Even if we assume that ids are truly random and uniformly distributed this does not mean that the sampling method doesn't have to be iid. This problem is similar to density estimation where Rejection sampling is super inefficient but converges to the correct solution, but MCMC type approaches might need to run multiple times to be sure to have found the solution.

The author notes that they used "cheats". Depending on what these do the iid assumption of the samples being independent could be violated. If it is akin to snowball sampling it could have an "excessive" success rate thereby inflating the numbers.

> Jason found a couple of cheats that makes the method roughly 32,000 times as efficient, meaning our “phone call” connects lots more often

What is meant by the disclaimer at the end,

> This is not an official Google product, it is just code that happens to be owned by Google.

Is the code developed by an external group and google just happened to be where first prototype was developed?

There is a typo in the title you might want to fix it from asteriod to asteroid, which I assume is the correct name given the domain.