It is weird and almost alien how AI designs factories. When Claude announced Fable 5 they also did a Factorio demo that was the same kind of designs and a lot of hand feeding and direct insertion.
In some sense it proves to me that llms are not just copying and are actually creating a bespoke solution. But I honestly expected more from frontier models
To them the player character is just as good as a belt for transporting materials.
The player character actually does have an extremely high capacity so if you are constantly running between chests your factory will still be reasonably high throughput.
What are you talking about? Maybe at the beginning, yes. Everyone carries stuff around when you have 2 assemblers, but once you start building your bus, the throughput of the player is orders of magnitude worse than using belts (or, if you're into that, bots)
> In some sense it proves to me that llms are not just copying and are actually creating a bespoke solution.
Is the weirdness of the designs meaningfully beneficial in any way that the AI has figured out, or could it for example be that the training data the LLMs learned from are too different from the type of data it would need to learn from in order to interface properly and play the game better? And then because of that incompleteness, the LLM has to hallucinate a lot and it results in weird outcomes.
I think one of the problems is that AI has no sense of laziness or time. Unless it's in their training data or prompted they're not going to bother automating something
Actually if you estimate the time required it's plausible that player feeding comes out on top, especially if setting up automation is not that obvious. But human player would never play that way, they would get bored from the repetitiveness, and the micro scheduling would be a headache. So the game designers never really have to close that gameplay loophole (or closing it would make human experience worse).
I have a similar problem on my GK2 vibegamed playthrough (see https://news.ycombinator.com/item?id=50302771), if I want computer to play similar to how a human would, I need to artificially close that loophole (for example, AI prefers to gather/eat low-tier food to cooking better).
Anyone making this claim just hasn't played a lot of factorio, because this just isn't true. Player feeding is fast in the early game, but it just doesn't scale. There's too many buildings to feed and it takes too long to walk between them. People have already made tool assisted speedruns, and as expected it involves building a huge automated factory as quickly as possible.
I said it's plausible. Perhaps not for basic materials, but things like robot frames, higher tier research packs, modules, anything that has low component amount compared to time or amount required for crafting.
Even speedrunners do quite a bit of handfeed. And if AI/player cannot figure out the automation fast enough - the handfeed might easily come on top.
That sounds like my family, we do crazy setups and deals which make the game take hours and has made many people refuse to play monopoly with us. The most creative strategy my brother came up with is roll insurance, where if you are about to roll with a chance of landing on a bankrupting property, he would calculate the probably and let you pay him a flat fee to cover your roll.
I really think dwarf planet is a fine classification. Pluto is a planet, just a really small one. Same with Eris and the other planets in the Kuiper Belt. I don’t know why the planet count of our solar system is seemingly such a Religious number. It seems as if it is the focus of why we should define what a planet is.
When I think of what a planet is, I think of a destination. Somewhere that maybe in the far future we could visit one day. I think the definition for a planet should be a body with sufficient gravity that we could reasonably orbit and land on. And we can have as many sub-classifications as we want from nano-planet to Mega-planet.
Googles local gemma models which target roughly the same parameter count range, are known for being a lot better at vision tasks than qwen, no idea if 3.8 has changed that though
I think I am going to buy a second rtx 3060, as 27B has been just outside of my range for to long, and this looks like the parameter count tipping point
whats the tok/s you get on that. I have heard a few claims of around 30-50 with mtp, but for how cheap the setup is I am surprised I don't hear more about 3060 stacks so I assume there has to be some catch.
I used GPT-5.6 Sol high to optimize it, and it claimed it was getting 50. I'm seeing ~40 on my goto smoketest: "Make me a vector add in CUDA".
Funny side note. It successfully one shot the program, but it wasn't able to run it because there literally wasn't enough VRAM left to allocate CUDA memory. Watching it try to debug that was fascinating. I'm pretty sure it would have killed the llama-server (and thus itself) if it hadn't been running in a separate container.
Because of all the svg rendering stuff, I added a draw_svg tool to my harness and it has been really nice to get a quick mock-up of ui changes. And conveniently, the new DeepSeek models are really good at knowing when to use it. So I do look at pelican rendering as a small metric of useful capability.
I tried it for a bit, and It was not really worth its size. It got swept up in all the other AI news recently, but laguna s 2.1 I think is the best ~100B moe model right now
I didn't mention it above, but Laguna S is my other favorite model. I use Qwen a lot more, it's smaller and faster, but I like to switch to Laguna when I feel like I need a "heavy hitter" for certain huge or complex tasks.
What on earth hardwares do you guys have to be able to run 100gb models locally?! That's crazy! I'm here struggling to even get 27b models to run in somewhat usable way
Haha I'm on an Mac Studio with an M1 Ultra, 64gb ram. I bought it when it first came out, it just happens to be good for local LLMs. I have to use a smaller quant of Laguna S though (I think 4-bit? Not at my machine to check), as 8-bit and full size definitely don't fit in the 64gb I have.
Yeah, a good rule of thumb is that the weights take up ~100% of the size of the model, so 100B bytes (8-bit quant) would be, well, 100GB and a 4-bit quant would be half that.
Right. The rule of thumb is that the overhead size of the model that's not the weights is so vastly outweighed by the actual number of weights that it can be disregarded. My shorthand for that was to write "the weights take up ~100% of the size of the model". What then "follows", both in the sense that the explanation is written after the rule as well as that it logically follows, is that, well, 100 billion bytes is, you know, 100 GB. I don't see why you're, like, paying so much attention to this?
Right. That's, well, not a rule of thumb. Asking how much of a bottle of water is, well, water, and someone says "well, a good rule of thumb is that it's all water", is just an answer. You don't need an estimate when, well, there is nothing to estimate.
Anyway, more seriously, I hope it's obvious by now that I don't particularly care that my means of communication is so offensive to you. I think you should, like, cry a river, build a bridge, then, well... get over it, you know?
And on the, er, "topic"? A rule of thumb for me does not have to be one for you, even if it's explicitly presented as such a rule. I thought that would be obvious but, well, here we are. Anyway, how's life been treating you?
At this point, what isn't much of an estimation anymore is that you are here to explain things that nobody's asked for, and like to assume people around you have been waiting for your pearls of wisdom. You're unable to realize when this isn't so. You're so full of yourself that you don't notice.
Also, in this particular thread, you started by wrongly making a correction of something that was clearly not an error, nor a wrong use of words, not even a misspelling. The problem is you started to post a correction before realizing that you didn't read right. That happens when one is more eager to boast one's own greatness than one is interested in the topic at hand. The result is that in this thread, you were clearly, as a rule of thumb, well, 100% wrong.
I understand that's you're frustrated from some deeply held software beliefs not holding up to examination, but making things up in a different thread is not a healthy way to work towards acceptance.
Strix Halo as well. Bought it for $1,800 new on sale and shoved an extra 4tb drive into it. Been amazing for local AI. Maybe not the absolute fastest thing (usually around 30t/s depending on the task) but has been awesome for a local AI box that I can solar power.
Couple of rack mount batteries and roughly 5kw of solar panels. Feeds into a subpanel so I can flip it when I want a couple rooms of solar on the house, or hook a generator up if needed. Can't power the entire house, but works well for thinks like computers, lighting, etc. And if I want to expand, just throw on more panels, or realistically, just throw on more batteries to store the juice.
Yeah, here I am sitting deeply deeply deeply regretting not buying couple CMP 170HX at $200 or $350, knowing I could just flip them ethically at purchase price if nothing came of it... I could have just casually built a 128GB dual A100 local AI monster
I'm working with a lab that has a few Ampere GPUs on infiniband and they are just not compatible with the latest quants and vLLM updates. FP8 is about as low as you can go.
I have an RX 6700 XT with 12gb vram and 64gb system ram. running dense models like 27b is difficult, but i can run IQ4/IQ5 qwen 122b-a10b or 35b-a3b at ~20tok/s
IQ4 qwen 122b-a10b would mean 61GB total size and 5GB active, so about 5GB of the model loaded into GPURAM plus any generated context, and 61GB of weights loaded into system RAM? I don't know if that math is correct, but does that run well? Wouldn't that only leave 3GB of system RAM?
Not badly so because MoE models(identifiable by "CoolName-xxxB-AxxB" naming scheme) have bunch of branches in the middle that only one out of all gets non-zero values. Each of branches aka "Experts" as well as top/bottom parts are significantly smaller than the whole, and so CPU emulation of CUDA operations mixed with GPU taking as much as possible become not so out of question, unlike for dense models("CoolName-xxxB" without "-AxxB")
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