The problem is that you didn’t bring much, and it sounds like you’re trying to spread FUD yourself:
why are you making a post instead of replying to a comment?
so… some really basic shit that should have been expected in a pre-2010 update + AI
Well done, guys. I guess you gotta start somewhere.
you’re the one comparing it to Linux
I don’t think you can have both
You don’t need that assumption. Your assumption can just be “the person and vessel (or a point in the vessel, like its center of mass) don’t diverge significantly over time”.
Then, if you treat velocity as a vector and compute the person’s average velocity vector over time, you’ll have a pretty close estimation to the vessel’s velocity vector.
After all, if those two average vectors (vessel’s and person’s) were to differ much, they would end up in different locations.
The average basically zeroes the vector for each lap the person does, so the remainder must be the vessel’s.
yeah, I think the whole “water” argument really dilutes the case against data centers.
On a serious note, the argument works for areas that already struggle to supply enough water for consumers. Otherwise, we should be focusing more on the power stress to the grid, and the domino effect on supply chain of hardware cost increases that it’s happening across many industries. It started with GPUs, now it’s CPU, storage, networking equipment, and other components.
If these prices are too high for a couple of years, we’ll start seeing generalized price increases as companies need to pass along the costs to consumers.
It’s not, I read the code. It’s not merely asking the LLM for recommendations, it’s using embeddings to compute scores based on similarities.
It’s a lot closer to a more traditional natural language processing than to how my dad would use GPT to discuss philosophy.
Ok, I’m not suggesting replacing humans with AI and I despise companies trying to do this unsustainable practice.
With that out of the way, I’ll restate that LLMs follow some rules more reliably than humans today. It’s also easier to give feedback when you don’t have to worry about coming across as a pedantic prick for pointing out the smaller things.
On your point that LLMs are not improving; well, agents and tooling are definitely improving. 6 months ago I would need to babysit an agent to implement a moderately complex feature that touches a handful of files. Nowadays, not as much. It might get some things wrong, but usually because it lacks context rather than ability. They can write tests, run them, and iterate until it passes, then I can just look at the diff to make sure the tests and solution makes sense. Again, something that would fail to yield decent results just in the last year.
No, it also doesn’t do that. It gets embeddings from an LLM and uses that to rank candidates.
can’t be worse than that screen
It’s true, I was the bowling pin
that’s how it looks when you walk/run in circles in a vessel that’s moving
some people get sick looking at screens?
lol macs have the glossiest screens out there, almost like they make it a mirror on purpose
no one is saying everyone has to ask an LLM for movie recommendations
wow, this dystopic update is reality not disappointing
yet another reason to not use Google
chill, this is extracting text embeddings from a local model, not generating feature-length films
that’s like saying “no jet use is benign” meant for comparing a private jet to a jet-ski
the generative aspect is not even used here
There’s no training, the LLM embeddings are used to compare the plots via a cosine similarity, then a simple weighted score with other data sources is used to rank the candidates. There’s no training, evaluation, or ground-truth, it’s just a simple tool to start using.
you can’t compare it to the islands, because the GPS trace is in an area within that green circle, with a different scale. You can only look at the 300m scale in the bottom right, which looks in the ballpark of an aircraft carrier to me