Whereas if the library returns something that is unambiguously an option type, consumers unambiguously call unwrap at their own risk.

Of course you do have a point (assuming I'm picking up what you're suggesting correctly) that all pointers in C are option types. But I think there's a cognitive ergonomics aspect to this where an option type more clearly relays the library authors' intent.

Isn't jetKVM software open source? I thought it had a public buildroot fork? Been a while since I checked, maybe something has changed.

> The numbers tell the story bluntly:

I wouldn't waste too much compute trying to parse it, lots of LLM tells in the article. Pangram puts it at 100% AI.

If the draft was wrong you can kill the speculative decode, and you haven’t lost anything except for idle time

It’s true we can’t show the user the token until we have the true decode finished, but we can launch more work internally before we’re certain

More and more, I try to ask it for low prose responses because its writing just seems like such a low signal to noise ratio

I'm curious about why LLM writing fails. Particularly whether LLM writing is fundamentally flawed, or if it's just distinctive and since it often reflects low effort, that distinctive voice is associated with low quality.

I find its reliance on extremely consistent rhetorical patterns concerning. The fact that it always finds a way to talk about how "It's not the X, it's the Y Z" no matter what topic you feed it, makes me concerned that the tail is wagging the dog

Maybe I'm taking the "all" too literally here, but I read the article, and I'm not seeing anywhere the author ran a substantial portion of his corpus through Pangram to determine the false positive rate. That would be really interesting to see.

He does

* give an example of a piece of his writing that was, when ran in segments, flagged as generated (which he disputes)

* multiply the size of his corpus by pangram's published false positive rate and estimate that a few of his pieces would be flagged

* get the pangram model to label a piece "100% AI" when it only has 3 generated sentences

* demonstrate the ability to intentionally trigger a false positive

It's driving me nuts, I constantly have to prompt it to "explain in plain, simple English"

Is there something about tuning for desirable qualities that forces LLMs to have this voice?

I think the second requires deeper concentration, but is still quite readable compared to the kind of low-content engagement / SEO stuff one read on the internet even before LLMs