Eager Eagle

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

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.

No, it also doesn’t do that. It gets embeddings from an LLM and uses that to rank candidates.

that’s how it looks when you walk/run in circles in a vessel that’s moving

no one is saying everyone has to ask an LLM for movie recommendations

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.

that’s pretty cool, this is just the wrong crowd, don’t worry about the downvotes

CEO James Cuda

the irony