yuliyp

Each of those agents ends up making "decisions" that lead it to look at some things over others. Given infinite the same agent could eventually fully explore all those options, but each one explores things a bit differently due to different forks in the road due to randomness in token generation. Thus sharing information is useful.

Fine. "The LLM produced a chain of reasoning consistent with a belief that X is happening."

I've found it fairly helpful to anthropomorphize systems to communicate things about their behavior, despite them obviously not being human ("The load balancer thought the West US region was chilling", or "that cluster was unhappy because of bad packet loss"). I don't really have many problems with people taking those statements that I think computers have emotions.

An LLM is not me. It is capable of doing more "work" but it also doesn't have my mental models and experience. My colleagues know roughly my areas of expertise. If I say "I think that X is happening" they'll assume that is the result of my expertise being applied and react accordingly. If I say "[LLM] thinks X is happening" they'll react differently and I want them to. Just like I might say "I suspect X is happening because Y" or "X is definitely happening" when I want to modulate their trust in what I'm saying.

But the write should have triggered a page fault immediately in that case, and should have been blocked on the page being actually allocated before resuming.

So the theory is that the page is faulted in, then somehow evicted within 10 instructions, then re-faulted in somewhere else, resulting in writes not making it to the page? That would need a context switch and another page fault to happen in short succession. But the context switch to evict the page back out would have necessitated pending writes to have finished. Nothing actually makes sense with that explanation.

There is not a perfect solution. There are many bad solutions. There are a few solutions that are OK but with different tradeoffs.

Hey, that's my name too!

Not sarcastic, but I probably didn't convey the subtlety of what I was trying to say in a one line comment. I was objecting to the defeatist "oh the tech is there, so we can't do anything about it" attitude. I tried to choose the examples I chose that the tech being there definitely has some consequences and significant privacy implications, but some controls exist too (like, wiretaps are still applied very selectively, there's been a growing movement against Flock cameras and scaling back of their deployments in some places recently).

The tech's also been there to put cameras everywhere, and to wiretap every phone, etc. We put guardrails in place to control how that tech is deployed.

To pull the example of Discord since the ExHashRing was mentioned in the OP: Needing to hash a few hundred things instead of one thing adds up when you do it a lot of times. They went with consistent hash ring over rendezvous hash because of that; every message needs to do one of these hash ring lookups, also whenever someone connects, they need to do a lot of these hash ring lookups to find all of their servers and friends.

There's plenty of scale below FAANG where efficiency matters.

The hierarchical (log(n)) approach to bucketing here is fine for an "I just want to shard this N ways, N will never change" but is extremely intolerant of bucket mutations.

Part of the point of rendezvous hash and consistent hashing is that adding and removing elements minimizes the amount of things reassigned. That is, if you add nodes, the only items being reassigned are those that are moving to the new nodes. If you remove nodes, the only items being reassigned are those leaving the departing nodes.

If you know your set of nodes never changes, or you don't care about the cost of reassignment, you don't need a rendezvous hash or consistent hash, you just need a plain old hash function.

> Now, if someone searches for an espresso machine, Gemini will pull up your most relevant products and instantly write a custom explainer highlighting why your product may be the right choice for them.

This is like the essence of the evil of AI ads distilled down to one sentence. For an advertiser this is a dream. For a user this reads like getting bombarded with ads tailor made just for you based on the context of what would be most effective.

But intelligent beings are fundamentally fallible? That's kind of the nature of doing leaps of reasoning: sometimes those leaps are amazing, sometimes they're wrong. It's what's advertised.

Glyph binning looks for any chunks in the image that are similar to eachother, regardless of what they are. Letters, eyeballs, pennies, triangles, etc without caring what it is. OCR looks specifically to try and identify characters (i.e. it starts with a knowledge of an alphabet, then looks for things in the image that look like those.

If the image is actually text, both of them can end up finding things. Binning will identify "these things look almost the same", while OCR will identify "these look like the letter M"

what at all does this comment have to do with what it's replying to?