I found the second part of Seven Eves to be a surreal experience. I think I might have stopped reading Stephenson after that.

Howso?

(Not parent)

The second half differs dramatically in tone. If you were really into the specific feeling of the first half, it is very jarring.

I found the whole thing very interesting and enjoyable, but I can imagine being excited for more content similar to the first half and being disappointed by the drastic shift in scale/tone/focus/etc.

Sounds like theft to me.

You didn’t read the article. This tax is for 2 years then as assessments are fixed it changes calculation.

"Yes this is why the higher level org functions are in love with AI. "

Interesting, I thought it was because so few of them have any idea how their organizations actually function, because so much of their work is performative.

(I have been a developer, sysadmin, director (x2), and president).

Isn't that the same? They don't know how the company works, instead think everything is done, by them talking to sycophants, so think that a perfect replacement for the sycophants is a perfect replacement for the company.

Strong sentiment against the US (government) in the US as well.

I live in a purple state (also a Canadian citizen but that's beside the point).

"Please prove your content was created by a flawed biological organism."

Seems better than the alternative: "This content was created by a machine, but being pushed/promoted by a flawed money-grubbing biological organism".

Them capital class is all in on those things and owns all the media. But the majority of us are not.

Article kind of lost me at "It can no longer argue that costs aren’t falling; they are."

They absolutely are. She has even linked a source for that. It’s almost indisputable that the prices (per capability) is going down. I think you should read it more deeply to understand the argument.

Has anyone else used LLMs to fact check other LLMS?

I hate to say it, but Gemini lies less frequently than paid models from OPenAI and Anthropic (Open AI is worst in my use cases).

My guess is that Google has better training data (and uses less synthetic data which might be creating training feedback loops in other models), has more of a "be calibrated" model than a "be helpful" model, but it could just be that they leverage more RAG than leveraging weights more.

But, I really shouldn't speculate the "why" as I'm out of my domain. Just curious if others use all the models they can and compare outputs as much as I do.

They do a lot of mental heavy lifting to support a corrupt and incompetent administration- sunk cost fallacy I imagine.