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Qwen3.6-Max-Preview: Smarter, Sharper, Still Evolving

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Re: Qwen3.6-Max-Preview: Smarter, Sharper, Still Evolving

#61

Post by wg0 »

atilimcetin wrote: post #33

Nowadays, I'm working on a realtime path tracer where you need proper understanding of microfacet reflection models, PDFs, (multiple) importance sampling, ReSTIR, etc.. Saying that mine is a somewhat specific use case. And I use Claude, Gemini, GLM, Qwen to double check my math, my code and to get practical information to make my path tracer more efficient. Claude and Gemini failed me more than a couple of times with…

I have said similar things about someone experiencing similar things while writing some OpenGL code (some raytracing etc) that these models have very little understanding and aren't good at anything beyond basic CRUD web apps.

In my own experience, even with web app of medium scale (think Odoo kind of ERP), they are next to useless in understanding and modling domain correctly with very detailed written specs fed in (whole directory with index.md and sub sections and more detailed sections/chapters in separate markdown files with pointers in index.md) and I am not talking open weight models here - I am talking SOTA Claude Opus 4.6 and Gemini 3.1 Pro etc.

But that narrative isn't popular. I see the parallels here with the Crypto and NFT era. That was surely the future and at least my firm pays me in cypto whereas NFTs are used for rewarding bonusess.

Re: Qwen3.6-Max-Preview: Smarter, Sharper, Still Evolving

#62

Post by wg0 »

wg0 wrote: post #61
atilimcetin wrote: post #33

Nowadays, I'm working on a realtime path tracer where you need proper understanding of microfacet reflection models, PDFs, (multiple) importance sampling, ReSTIR, etc.. Saying that mine is a somewhat specific use case. And I use Claude, Gemini, GLM, Qwen to double check my math, my code and to get practical information to make my path tracer more efficient. Claude and Gemini failed me more than a couple of times with…

I have said similar things about someone experiencing similar things while writing some OpenGL code (some raytracing etc) that these models have very little understanding and aren't good at anything beyond basic CRUD web apps. In my own experience, even with web app of medium scale (think Odoo kind of ERP), they are next to useless in understanding and modling domain correctly with very detailed written specs fed in…

Someone exactly said it better here[0] already.

[0]. https://news.ycombinator.com/item?id=47817982

Re: Qwen3.6-Max-Preview: Smarter, Sharper, Still Evolving

#63

Post by cyanydeez »

dandaka wrote: post #54
zozbot234 wrote: post #48

Earlier quoted context omitted.

You need to supervise the model anyway, because you want that code to be long-term maintainable and defect free, and AI is nowhere near strong enough to guarantee that anytime soon. Using the latest Opus for literally everything is just a huge waste of effort.

Waste of effort... of Opus? If "Opus effort" is cheaper, than dev hours managing yourself more dumb/effective model, what is the point?

rich people dont concern themselves with the cost of tokens.

Re: Qwen3.6-Max-Preview: Smarter, Sharper, Still Evolving

#64

Post by cmrdporcupine »

wolttam wrote: post #24
cmrdporcupine wrote: post #19

Earlier quoted context omitted.

Use them via DeepInfra instead of z.ai. No reliability issues. https://deepinfra.com/zai-org/GLM-5.1 Looks like fp4 quantization now though? Last week was showing fp8. Hm..

Deepinfra's implementation of it is not correct. Thinking is not preserved, and they're not responding to my submitted issue about it. I also regularly experience Deepinfra slow to an absolute crawl - I've actually gotten more consistent performance from Z.ai. I really liked Deepinfra but something doesn't seem right over there at the moment.

Damn. Yeah, that sucks. I did play with it earlier again and it did seem to slow down.

It's frankly a bummer that there's not seemingly a better serving option for GLM 5.1 than z.AI, who seems to have reliability and cost issues.

Re: Qwen3.6-Max-Preview: Smarter, Sharper, Still Evolving

#65

Post by qalmakka »

visarga wrote: post #41
ninjahawk1 wrote: post #29

The way to develop in this space seems to be to give away free stuff, get your name out there, then make everything proprietary. I hope they still continue releasing open weights. The day no one releases open weights is a sad day for humanity. Normal people won’t own their own compute if that ever happens.

I think it is in the interest of chip makers to make sure we all get local models

I think they're in a win-win situation. Big AI companies would love to see local computing die in favour of the cloud because they are well aware the moment an open model that can run on non ludicrous consumer hardware appears, they're screwed. In this situation Nvidia, AMD and the like would be the only ones profiting from it - even though I'm not convinced they'd prefer going back to fighting for B2C while B2B Is so much simpler for them

Re: Qwen3.6-Max-Preview: Smarter, Sharper, Still Evolving

#66

Post by WarmWash »

ninjahawk1 wrote: post #29

The way to develop in this space seems to be to give away free stuff, get your name out there, then make everything proprietary. I hope they still continue releasing open weights. The day no one releases open weights is a sad day for humanity. Normal people won’t own their own compute if that ever happens.

The Chinese state wants the world using their models.

People think that Chinese AI labs are just super cool bros that love sharing for free.

The don't understand it's just a state sponsored venture meant to further entrench China in global supply and logistics. China's VCs are Chinese banks and a sprinkle of "private" money. Private in quotes because technically it still belongs to the state anyway.

China doesn't have companies and government like the US. It just has government, and a thin veil of "company" that readily fool westerners.

Re: Qwen3.6-Max-Preview: Smarter, Sharper, Still Evolving

#67

Post by grttsww »

WarmWash wrote: post #66
ninjahawk1 wrote: post #29

The way to develop in this space seems to be to give away free stuff, get your name out there, then make everything proprietary. I hope they still continue releasing open weights. The day no one releases open weights is a sad day for humanity. Normal people won’t own their own compute if that ever happens.

The Chinese state wants the world using their models. People think that Chinese AI labs are just super cool bros that love sharing for free. The don't understand it's just a state sponsored venture meant to further entrench China in global supply and logistics. China's VCs are Chinese banks and a sprinkle of "private" money. Private in quotes because technically it still belongs to the state anyway. China doesn't hav…

So what?

I still prefer that over US total dominance.

Let them fight it out.

Re: Qwen3.6-Max-Preview: Smarter, Sharper, Still Evolving

#68

Post by darkwater »

WarmWash wrote: post #66
ninjahawk1 wrote: post #29

The way to develop in this space seems to be to give away free stuff, get your name out there, then make everything proprietary. I hope they still continue releasing open weights. The day no one releases open weights is a sad day for humanity. Normal people won’t own their own compute if that ever happens.

The Chinese state wants the world using their models. People think that Chinese AI labs are just super cool bros that love sharing for free. The don't understand it's just a state sponsored venture meant to further entrench China in global supply and logistics. China's VCs are Chinese banks and a sprinkle of "private" money. Private in quotes because technically it still belongs to the state anyway. China doesn't hav…

Well, isn't this what the US and really any other power in the world has always done, since forever?

Re: Qwen3.6-Max-Preview: Smarter, Sharper, Still Evolving

#69

Post by zozbot234 »

qalmakka wrote: post #65
visarga wrote: post #41

Earlier quoted context omitted.

I think it is in the interest of chip makers to make sure we all get local models

I think they're in a win-win situation. Big AI companies would love to see local computing die in favour of the cloud because they are well aware the moment an open model that can run on non ludicrous consumer hardware appears, they're screwed. In this situation Nvidia, AMD and the like would be the only ones profiting from it - even though I'm not convinced they'd prefer going back to fighting for B2C while B2B Is s…

If you want to run AI models at scale and with reasonably quick response, there's not many alternatives to datacenter hardware. Consumer hardware is great for repurposing existing "free" compute (including gaming PCs, pro workstations etc. at the higher end) and for basic insurance against rug pulls from the big AI vendors, but increased scale will probably still bring very real benefits.

Re: Qwen3.6-Max-Preview: Smarter, Sharper, Still Evolving

#70

Post by amarcheschi »

wg0 wrote: post #61
atilimcetin wrote: post #33

Nowadays, I'm working on a realtime path tracer where you need proper understanding of microfacet reflection models, PDFs, (multiple) importance sampling, ReSTIR, etc.. Saying that mine is a somewhat specific use case. And I use Claude, Gemini, GLM, Qwen to double check my math, my code and to get practical information to make my path tracer more efficient. Claude and Gemini failed me more than a couple of times with…

I have said similar things about someone experiencing similar things while writing some OpenGL code (some raytracing etc) that these models have very little understanding and aren't good at anything beyond basic CRUD web apps. In my own experience, even with web app of medium scale (think Odoo kind of ERP), they are next to useless in understanding and modling domain correctly with very detailed written specs fed in…

a semester ago i was taking a machine learning exam in uni and the exam tasked us with creating a neural network using only numerical libraries (no pytorch ecc). I'm sure that there are a huge lot of examples looking all the same, but given that we were just students without a lot of prior experience we probably deviated from what it had in its training data, with more naive or weird solutions. Asking gemini 3 to refactor things or in very narrow things to help was ok, but it was quite bad at getting the general context, and spotting bugs, so much that a few times it was easier to grab the book and get the original formula right

otoh, we spotted a wrong formula regarding learning rate on wikipedia and it is now correct :) without gemini and just our intuition of "mhh this formula doesn't seem right", that definitely inflated our ego

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