djsjajah
djsjajah on Hacker News
Go have a look what percentage of the operating costs are covered by fares in your city. I wouldn't be surprised if the costs relating to fare enforcement were more than the revenue from fares.
I think you are overthinking it too. The task was to find three, not something that is equivalent to three. However you choose to represent it, it wouldn't be hard to come up with a different "three" that you won’t find within that representation. If you are changing the representation, why wouldn't you just change the interval that you are searching?
No. You might be reading into it a little bit too much.
My point was that finding three needs to be possible for you to find it. If you are looking on the interval 0-1, it is not possible. As other comments have kinda of suggested, you could map every integer to a real and find "three" but if you are going to do that, why not just look on an interval that actually contains the number?
Not all things are possible, only potentially possible ones.
If you take all the numbers between zero and one, you will find an endless supply of distinct numbers but none of them are 3.
> Note, in the stories, the majority of robots are not considered so useless/dangerous to remove positronic robots from the fictional future society
It’s a bit hard to write a story about the impacts of robots in society without a society with robots. There is also an entire series based around a world where the robots tried to rise up against humans.
> That was not demonstrated.
No, that was my summary of his point. It is not perfect, but I think it’s accurate enough.
I can assure you, that none of today’s robots are completely safe. They will be just as happy to move wherever you tell them regardless of what is in the way. When I said “completely safe” I mean it in the way that the three laws are written, incapable of harm.
I am not arguing that robots are not useful, but his point was about a perfect safety system. It doesn’t exist.
I don't know how you could come to that point of view. His point is that there will always be a loophole. There does not exist a set of rules that make a completely safe and useful robot.
funny, I am not surprised that it is now much harder to run an automated twitter account for legitimate purposes. Running one for illegitimate purposes on the other hand....
You have to power it all the time, but the amount of power it uses while it’s on will change by up to a few orders of magnitude depending on the gpu. It’s not uncommon for a gpu to be pulling just a couple of watts at idles and several hundred at full tilt.
So the only way it’s a fixed cost is if you don’t pay for power. If you only consider the cost of the power, it might still be cheaper paying for an api.
but what is the point?
A ban is supposed to make a certain thing less likely to occur. Does a ban of open source models do that? Presumably, the behavior you are trying to limit is the miss-use of these models but I don't know how many state sponsored hacking groups are going to give a ban a second thought.
I think they were making a joke. In the future, you might consider the advice you are giving as well as giving it.
Not with 800 examples. If you are going to consider an ngram model, I think you are better off getting a frontier llm to write you an absurd regex.
Except, that won’t help. By the time a new fab is up and running, we will probably have a massive surplus.
You need to think this thought through all the way to the end. What it has said also influences what it will say. If it has consistently made combative responses, then the most likely thing to do is to continue to be combative.
I don't think there is any way back after the conversation takes a turn like that so there is no point in arguing with it. The only thing you can do is to fork the conversation before it made the first mistake and give it more context or tell it to look things up.
It’s amusing that a lot of the agents have worked out that sampling doesn’t change ppl.
I think what they mean by “now” is the stuff announced today.
I don't follow. Can you explain how your comment is relevant to mine? It might help if you also explain how you interpreted my comment.
You just failed the Turing test.
I have 2 of them. I would advise against if you want to run things like vllm. I have had the cards for months and I still have not been able to create a uv env with trl and vllm. For vllm, it’s works fine in docker for some models. With one gpu, gpt-oss 20b decoding at a cumulative 600-800tps with 32 concurrent requests depending on context length but I was getting trash performance out of qwen3.5 and Gemma4
If I were to do it again, I’d probably just get a dgx spark. I don’t think it’s been worth the hassle.
> or by the community
Hmmm
yes, but the difference between one model and one 4x larger is usually a lot more than that.
It is not a question of do a run Qwen 8b at bf16 or a quantized version. It more of a question of do I run Qwen 8b at full precision or do I run a quantized version of Qwen 27b.
You will find that you are usually better off with the larger model.
trl.
give me a uv command to get that working.
But even in the amd stack things (like ck and aiter) consumer cards are not even second class citizens. They are a distance third at best.
If you just want to run vllm with the latest model, if you can get it running at all there are going to be paper cuts all along the way and even then the performance won't be close to what you could be getting out of the hardware.
No. It seems to me that the comment is objectively incorrect.
The original comment was talking about inference and from what I can tell, it is strictly going to run slower than the model trained to the same loss without this approach (it has "minimal overhead"). The main point is that you wont need to train that model for as long.
That’s kind of a moot point. Even if none of those overheads existed you would still be getting a a fractions of the mfu. Models are fundamental limited by memory bandwidth even with best case scenarios of sft or prefill.
And what are you doing that I/O is a bottleneck?
> including all previous experiments
How far back do you go? What about experiments into architecture features that didn’t make the cut? What about pre-transformer attention?
But more generally, why are you so sure that they team that built Gemini didn’t exclusively use TPUs while they were developing it?
I think that one of the reasons that Gemini caught up so quickly is because they have so much compute at fraction of the price of everyone else.
Not only can it be streamed, but lz4 will probably make things quicker.
> Do you really though?
Yes.
It stays in on the hbm but it need to get shuffled to the place where it can actually do the computation. It’s a lot like a normal cpu. The cpu can’t do anything with data in the system memory, it has to be loaded into a cpu register.
For every token that is generated, a dense llm has to read every parameter in the model.
GPUs might not be bandwidth starved most of the time, but they absolutely are when generating text from an llm.
It’s the whole reason why low precision floating point numbers are being pushed by nvidia.