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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

#141

Post by e12e »

balls187 wrote: post #114
OtomotO wrote: post #83

Earlier quoted context omitted.

Many people averted religion (which I can get behind with), but have never removed the dogmatic thinking that lay at its root. As so many things these days: It's a cult. I've used Claude for many months now. Since February I see a stark decline in the work I do with it. I've also tried to use it for GPU programming where it absolutely sucks at, with Sonnet, Opus 4.5 and 4.6 But if you share that sentiment, it's alway…

> I've used Claude for many months now. Since February I see a stark decline in the work I do with it. I find myself repeating the following pattern: I use an AI model to assist me with work, and after some time, I notice the quality doesn't justify the time investment. I decide to try a similar task with another provider. I try a few more tests, then decide to switch over for full time work, and it feels like it's a…

I think it might have to do with how models work, and fundamental limits with them (yes, they're stochastic parrots, yes they confabulate).

Newer (past two years?) models have improved "in detail" - or as pragmatic tools - but they still don't deserve the anthropomorphism we subject them to because they appear to communicate like us (and therefore appear to think and reason, like us).

But the "holes" are painted over in contemporary models - via training, system prompts and various clever (useful!) techniques.

But I think this leads us to have great difficulty spotting the weak spots in a new, or slightly different model - but as we get to know each particular tool - each model - we get better at spotting the holes on that model.

Maybe it's poorly chosen variable names. A tendency to write plausible looking, plausibly named, e2e tests that turns out to not quite test what they appear to test at first glance. Maybe there's missing locking of resources, use of transactions, in sequencial code that appear sound - but end up storing invalid data when one or several steps fail...

In happy cases current LLMs function like well-intentioned junior coders enthusiasticly delivering features and fixing bugs.

But in the other cases, they are like patholically lying sociopaths telling you anything you want to hear, just so you keep paying them money.

When you catch them lying, it feels a bit like a betrayal. But the parrot is just tapping the bell, so you'll keep feeding it peanuts.

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

#143

Post by paprikanotfound »

jpfromlondon wrote: post #75
Someone1234 wrote: post #4

Earlier quoted context omitted.

If money is no object, then nothing else is worth considering if it isn't Codex 5.4/Opus 4.7/SOTA. But for many to most people, value Vs. relative quality are huge levers. Even many people on a Claude subscription aren't choosing or able to choose Opus 4.7 because of those cost/usage pressures. Often using Sonnet or an older opus, because of the value Vs. quality curve.

anecdotally the quality of output isn't significantly different, the speed seems to be what you're really paying for, and since the alternative is free I'll stick to local.

What are the best models to run locally?

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

#145

Post by svnt »

bensyverson wrote: post #120
ecshafer wrote: post #110

Earlier quoted context omitted.

All people think dogmatically. The only difference is what the ontological commitments and methaphysical foundations are. Take out God and people will fit politics, sports teams, tools, whatever in there. Its inescapable.

Allow me to introduce you to Buddhism

Which one?

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

#147

Post by CamperBob2 »

thesz wrote: post #84
CamperBob2 wrote: post #58

Earlier quoted context omitted.

I'm a little more optimistic than that. I suspect that the open-weight models we already have are going to be enough to support incremental development of new ones, using reasonably-accessible levels of compute. The idea that every new foundation model needs to be pretrained from scratch, using warehouses of GPUs to crunch the same 50 terabytes of data from the same original dumps of Common Crawl and various Russian…

Change layer size and you have to retrain. Change number of layers and you have to retrain. Change tokenization and you have to retrain.

And yet the KL divergence after changing all that stuff remains remarkably similar between different models, regardless of the specific hyperparameters and block diagrams employed at pretraining time. Some choices are better, some worse, but they all succeed at the game of next-token prediction to a similar extent.

To me, that suggests that transformer pretraining creates some underlying structure or geometry that hasn't yet been fully appreciated, and that may be more reusable than people think.

Ultimately, I also doubt that the model weights are going to turn out to be all that important. Not compared to the toolchains as a whole.

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

#148

Post by Tepix »

wg0 wrote: post #82

Notice the pattern that Chinese providers are now: 1. Keeping models closed source. 2. Jacking up pricing. A lot. Sometimes up to 100% increase.

Are you talking about GLM 5.1, DeepSeek V3.2 or Kimi K2.6 (released one hour ago!)?

Oh wait, it doesn't apply to those…

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

#150

Post by runarberg »

svnt wrote: post #139
ehnto wrote: post #127

Earlier quoted context omitted.

I definitely find your last point is true for me. The more work I am doing with AI the more I am expecting it to do, similar to how you can expect more over time from a junior you are delegating to and training. However the model isn't learning or improving the same way, so your trust is quickly broken. As you note, the developer's input is still driving the model quite a bit so if the developer is contributing less…

Your version of the last point is a bit softer I think — parent was putting it down to “loss of talent” but yours captures the gaps vs natural human interaction patterns which seems more likely, especially on such short timescales.

I confusingly say both. First I say that the ratio of work coming from the model is increasing, and when I am clarifying I say “your talent keeps deteriorating”. You correctly point out these are distinct, and maybe this distinction is important, although I personally don‘t think so. The resulting code would be the same either way.

Personally I can see the case for both interpretation to be true at the same time, and maybe that is precisely why I confused them so eagerly in my initial post.

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