naasking

What is the actual problem being solved though?

Driving the compressor with software is not intrinsically a problem, but critical functions have to be isolated to a minimal safe kernel that will always run, like the kernel space/user space distinction. Smart features can read and send messages to the minimal kernel, but should not themselves be in the critical loop. Boggles the mind that this is not standard practice.

My Samsung 3D TV is still going strong after 13 years.

There were too many unsolved problems with diffusion to commercialize it, where autoregressive loops had a more straightforward roadmap. Doesn't mean the diffusion problems are insurmountable though.

> $20M for 1.7MW of capacity. Over $10 per watt. Recent utility scale installations in California are more like $1/W.

It's a) just a trial, and b) not just generating power so that cost isn't factoring in the other benefits.

I don't think so, I typically use Opus 5 on High, and 5.5 scores lower on token use:

https://artificialanalysis.ai/models/claude-opus-5-5?models=...

llama.cpp has significant AI contributions. The Linux kernel too is receiving AI patches. You are seriously out of touch.

> LMs generate text output that appears to be useful, but regularly is not.

No, they are empirically useful, and only getting more useful. This is not even a debate anymore.

Is there a known source of internal flaws/porosity in an otherwise solid part? Presumably laser melting produces a puddle which shouldn't allow for internal pores, as long as it isn't printed too fast (or solidifies too fast, which is why I think most chambers are internally heated to near melting temp).

Re: surface roughness, I can understand that the powder grain size creates a sort of minimal structure size, and can in principle be the start of a crack if a surface grain gets knocked loose. Is that the sort of thing you mean? I can see that for any internal or external surfaces, and a rocket engine combustion is certainly applying a lot of heat and pressure on surface grains. Can this be alleviated by smaller grain sizes, or is there some limit there?

Re: repeated heating/cooling and internal stresses, this strikes me as just requiring standard post-printing stages like tempering to alleviate internal stresses.

Sure, but I mean what's the technical reason a material isn't it 3D printing friendly? Are we talking grain structure here? Is it something that can be at least partly mitigated by some post-printing heat treatments, like tempering?

Individual incidents aren't representative of accuracy or well-tuned truth seeking processes, that can only be assessed over time.

> LLMs are vectorial databases with losses that index statistically filled data

Yes, and that statistically filled data is insanely useful. It remains true that it's a relatively poorly understood how this can be applied in various scenarios and what processes are needed to ensure robust results (or quantify the uncertainty).

What's the current theory for why this is?

Model, Q4_K_M: https://huggingface.co/agentionai/Signal-3.8-27B-GGUF

DFlash2, Q8_0, --spec-draft-n-max=7: https://huggingface.co/z-lab/Qwen3.8-27B-DFlash2-GGUF

I run llama.cpp with -ctv=8, -ctk=q4. Vulkan has better throughput if you're doing single-stream decode, but ROCm has better throughput if you have "--parallel 2" or higher. If supporting parallelism, unified kv cache should be off, especially with Vulkan.

Of course, some of these may be specific to my card so try variations for your hardware. Hermes can concoct a test suite and run some tests for different llama.cpp parameter permutations to find something optimal.

I've found the opposite on my R9700 (n-max=7, no other speculative decoding like ngram-mod, which I found slows it down). I think it depends whether your workload and system are bandwidth limited or compute limited. I see draft acceptance around 0.55, so 0.55 * 7 = 3.8 tokens per pass, which on my bandwidth-limited card takes me from 30tps to a peak of 80tps on llama.cpp (MTP peaked at ~65tps). I'm also running a Qwen fine tune whose speculative execution is better than the base model.

Strix Halo has lower compute than the R9700 but the RAM is also slower, so not sure what would be the ultimate limiting factor.

Move fast on the concentric (increases power), move slowish on the eccentric.

Resistance training should be a form of stretching. If you're taking the loaded muscle through full ranges of motion, as you should be, then that also means ranges where the muscles are fully stretched. Unfortunately many people cut the range of motion short so they can add more load, which is counterproductive for both flexibility, injury risk and progress.

> We have hope for these approaches but the reality of this stuff is way more nuanced than you think it is.

Sure, everything has more nuance. The point is this stuff is available now; this isn't some future sci-fi, it's only going to get better, it's not the only research on gene targeting, and AI is starting to help with this research. By the time AGI is actually here, consider the breadth of knowledge and capabilities that will be at its disposal.

> Alphafold can't reliably predict thermal energy landscapes or make functional predictions - and how could it? It wasn't trained on anything that could capture structure - function relationships.

If your point is that the only reason an AI like AlphaFold can't make functional predictions is that we haven't trained an AI to do that, then unless you're arguing we can't or won't ever do that, I'm not sure how that's supposed to be an objection to the argument that AI will be able to make use of this information without doing all of the experiments people seem to think would be necessary.

Like I said, we're already going to be doing these experiments because it's useful to us, and we will train AIs to make these predictions, again, because it's useful to us. Stop imagining what an AGI has access to now, and start thinking what it will have access to with the inevitable march of progress that we're already on.

Edit: and of course, this doesn't even take into account the fact that an AI could acquire resources to pay people to do this research. The internet provides ample opportunities like this now.

We're already doing those experiments. Tailor made mRNA vaccines targeted to one's own specific cancer mutations can be bought right now. The techniques are getting more sophisticated and more targeted every year. Our ability to predict what happens at these levels is improving by leaps and bounds too thanks to AI like AlphaFold. Anyone can be reasonably confident that no AI or teen can do this now or in the next few years, but are you really so confident what might be possible in 10 years?

We're doing that science right now. We're using AI to do it. By the time AGI truly spreads, we'll have a lot of the knowledge you say will be needed. This is simply not as far fetched as you seem to think. Next few years, unlikely. 10-15 years? Increasingly possible. 20+ years, I'd hazard even likely.

> The said user (Tristan Buckmaster) didn't solve the millennium problem. He didn't really accuse that OpenAI stole his research either. The beef came from the fact OpenAI asked him to remove another mathematician, who works for Anthropic, from the credit.

Not quite accurate, Buckmaster was taking an approach that nobody else was, and this new proof uses this same approach just weeks after he saved those results to OpenAI workspaces. He asked OpenAI if they used chat logs for training the new model, and they did not confirm or deny.

Asking to remove his collaborator is also totally over the line though.

Edit: although this OpenAI post is not comforting: https://x.com/OpenAI/status/2097375276384567642

Quote: "While unlikely, we cannot rule out that de-identified data derived from their usage of our products helped improve our models. "

Q4 isn't an extreme quant, and I average 75 toks/s on code, 45 tok/s on prose with MTP.

Yes, both the output should be "milestones" of sorts, like lemmas and theorems in math. Important plateaus that serve as a launching pad to the next phase. Regurgitating every thought potentially degrades signal:noise ratio.

It's not easy to test as there is no formal definition or formal criteria for AGI, only exclusionary criteria like "not X". That's why he phrased it that way, he's saying it's going to be clear with hindsight once we have a better understanding of things that this time and/or this model will be the inflection point of AGI.

They detected a single weird event, not necessarily a particle.

BlueSky in no way represents a very large number of humans. Most laypeople I've spoken to are fairly neutral on AI at this point.

There's actually already a fork that implements the preliminaries:

https://github.com/ggml-org/llama.cpp/discussions/21961

Instead of creating your own engine, would it really be that hard to add paged attention to llama.cpp?

For one, by synthesizing the results of multiple papers and suggesting novel experiments. If one paper sets constraints X for some system, and another paper sets constraints Y where Y!=X for a system that's similar but slightly different, then that's fertile ground for an experiment that can extract the more general underlying principles. This has already happened for domain-specific AI in fact, but the idea here is that it will become routine with general AI systems, as is happening now with math.

> amazon would publish every single boon without giving a cent to writers. it would be reqlly hard for writers to make a living.

Not quite, Amazon would just become the publisher that buys new books/articles/whatever directly from the writers, one upfront fee. Not saying it's a sustainable living for writers, just saying that's probably what would happen.