StevenWaterman

The set of models that are pareto-optimal, IE for some set of variables, no other model strictly dominates them = no other model is better than them on every variable.

So like, on a cost-intelligence graph, the cheapest and most intelligent models are pareto optimal. Then in-between those if you have

- cost $3 intelligence 6

- cost $1 intelligence 5

- cost $2 intelligence 4

The 1st and 2nd are pareto optimal, the 3rd is not, because it's dominated by the 2nd (2nd is cheaper AND more intelligent at the same time)

I'm pretty sure that's the plan. Currently they're legally bound to keep it within 0.9s of solar noon (or something like that) but in 2035 it's changing to +-1 minute, which basically kicks the can down the road for another century or so

I say we let it reach 15 minutes then countries can solve it themselves by shifting timezone by 15 mins. Since making sure solar noon matches noon on the clock, is the point of timezones existing in the first place

I thought about this more and realised your question might have been "what's the difference between knowing and learning". IE, how can we say the model believes something without having been taught it.

I think you're right that they're basically the same thing. I'd argue they're very slightly different because what an AI model ends up knowing isn't perfectly predictable based on what they were taught (emergent intelligence), but the sentence you quoted is using believing and learning to mean the same thing, it's just trying to draw attention to the fact that the training process structurally enforces "cheat as much as possible without getting caught".

IE, the contrast in the original sentence wasn't "believe" vs "learn", it was "good" vs "permissible"

Believing and knowing are overlapping sets, imagine what you think of when someone says an AI "knows" something, it's the same mechanism (I'd describe it as something along the lines of "encoded abstractly in the weights")

I'd be very surprised if this was AI, it's too bad-looking. The lighting is all wrong, there's noticeable repeating rock textures

You read the OP backwards, they said Sonnet is a downgrade from Qwen, and prefer Qwen's tone

I think we'll get there. Right now it works for me, because I'm naturally pretty verbose in my prompts, and know the codebase well, so I know what it needs to look at. Plus subagents for anything exploratory.

I think deepseek v4 pro has 1m context and does pretty well up to around 600k. But if you have the hardware to run that locally, you already know

Even then if there's a smaller model with 1M context, you'll need a ton of RAM to actually run it at full 1M. I guess that's why you don't see it too much. Anyone that could run Qwen 3.6 27B with 1m context would be better off running a much bigger model with smaller context instead, in the same amount of VRAM.

In terms of optimizing further, huge context + KV quantization sounds like a terrible idea, but there's some decent innovation in sparse attention, KV cache rotation allowing Q8 to perform nearly as well as full 16-bit precision, plus some ideas around offloading KV cache to system RAM (but I'm skeptical)

Yep, I daily drive Qwen3.6-27B (including for work), have done pretty much since it came out. IMO it's the only (small-ish, local) model worth using, if you can run it. It might not be as good as Opus at "add X large feature" but I don't want that in a model. I want to do the thinking while it does the typing. And Qwen 3.6 27B is perfectly good at that (while in my experience models like the 35A3B and gemma are significant downgrades)

Plus, I never have to worry about rate limits, quotas, or sitting in a queue during peak time. And I can always see its full thoughts, don't have to worry about where my data is getting sent, and know it can't get secretly nerfed.

Running on 2x 3090, 500-1000tok/s prefill and 60tok/s output at Q6_K_XL with MTP on llama.cpp, 220k tokens context window (starts to get a bit dumb above 160k ish), no KV quantization

What happens if the checks stop rolling

Late 18th century France

I agree with your premise, but let's not pretend we did a good job equitably distributing the benefits of the industrial revolution

The problem is, people see "they're not profitable once you account for training" and equate that to "AI will go away soon"

But if all the AI companies stopped training new models, they would all instantly become profitable (and stick around)

The thing that makes them unprofitable, is having to compete (which means training models). If / when enough companies exit the market, the cost to compete goes down and you end up in an equilibrium

If you have ASI that follows instructions, you can just instruct it to not get stolen and then it won't get stolen. Most logic / intuition breaks down with ASI.

/r/localllama is one of the most useful places

*terrible, not trickle

My first instinct was that the essay would just be "67" as a stupid and harmless but nonsensical response.

Somewhat amusingly, mine depends on the examiner knowing how advanced AIs are. In the 1960s mine would just look like a trickle AI. It feeling human demands we assume the ai would actually be competent

Yours is even more effective. Both hinge on the solution being "be as unexpected and out-of-distribution as possible"

I somehow imagine they wouldn't like your essay that is made of 100% slurs though, regardless of how effective it is at the stated task

"Embarrassingly" has a history as a technically meaningful word roughly equivalent to "maximally", see "Embarrassingly parallel"

https://en.wikipedia.org/wiki/Embarrassingly_parallel

Yes, because it would fall down (sometimes, often enough that regulatory bodies forbid it)

This thread started because of "the cheapest bridge that just barely won't fail"

My point was that safety factors are a part of this. A safety factor of 1.0, designing bridges so that they can perfectly withstand the expectations of intended use, means that some unacceptable % of those bridges will fall down in practice.

In other words, it's true that you can explain safety factors as:

Assuming perfect construction, and no defects, under designed maximum load, make sure that this bridge really stays up by a wide margin

But that misses the point of why we use safety factors. Nobody is paying for a bridge to really stay up by a wide margin. Because there's no material difference between a bridge that stays up, and a bridge that really stays up, right up until the point that the weaker one falls down due to inevitable over-loading or defects in construction / materials.

Safety factors account for uncertainty. Uncertainty the quality of materials, of workmanship, of unaccounted-for sources of error. Uncertainty in whether the maximum load in the spec will actually be followed.

Without a safety factor, that uncertainty means that, some of the time, some of your bridge will fall down

Safety factors exist because without them, bridges fall down

"If the heat shield breaks then I will die" is the exact situation for the astronauts, and yet we still have astronauts.

In fact it's worse for the astronauts, because in this hypothetical only the heat shield failing will condemn the POs to death, whereas any critical part failing kills the astronauts

Yes, it's a much sexier job than project manager, but clearly there are some people, in some circumstances, that would accept it.

A covert signal is still beneficial even if the signal is secure. The existence of the signal is valuable metadata.

For a contrived example, imagine I'm in a warzone:

- Secure = Enemies can't read my messages. Good. But they can still triangulate my position.

- Covert = Enemies don't know I exist

[2] Is sorted by profit/loss high to low, so you're seeing the first page of highest gains only, which is why it looks like he's always right. If you sort alphabetically / by date then there are losses

This is almost textbook countersignalling. The same as:

- Signalling: I dress more formally than everyone else to make up for the fact I'm less professional in other ways

- No signalling: I dress like everyone else because I am like everyone else

- Countersignalling: I wear ratty old clothes with holes in them, and nobody will dare to question it because I'm the important one here

They're somewhat effective at stopping people applying if those people know they will have to lie

Cached tokens are cheaper (90% discount ish) but not free

That is true for gpt-image-1 but not nano-banana. They can do masked image changes

Yep. Insufficiently stimulated by normal life, a crisis brings your dopamine levels back up to normal and you hyperfocus. Get tested and medicated, for you and your family

If you want to run it overnight, or while you're at work, so it finishes as you arrive and doesn't leave the clean clothes in a clump for hours (or so it runs during cheaper power hours)

and your wireless modem has wires