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Qwen3.8-Max: A New Bar for Coding and Cowork

qwen.ai

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Re: Qwen3.8-Max: A New Bar for Coding and Cowork

#361

Post by ListeningPie »

satvikpendem wrote: post #334
me551ah wrote: post #257

This makes me wonder if AI companies even have a MOAT in the first place. All requests to an LLM are idempotent, for every API call you need to send it the entire conversation history so that it can process it. LLMs do not learn or remember anything, which makes it super easy for users to switch LLMs on the fly. Most popular AI frameworks, make this a one-liner change these days. And that makes me wonder if the trill…

Google figured this out with their paper from 2023, We have no moat and neither does OpenAI . The moat now is the harness and being able to recursively self improve from RLHF, a great example is how Grok used to be pretty bad but since SpaceX bought Cursor, they used that data to train Grok 4.5 which is now very competent at coding and even exceeds frontier models in certain benchmarks. https://www.semianalysis.com/p…

And yet investment is continuing. What are they counting on?

Re: Qwen3.8-Max: A New Bar for Coding and Cowork

#362

Post by orbital-decay »

qeternity wrote: post #354
jcoder wrote: post #305

Earlier quoted context omitted.

> All requests to an LLM are idempotent, for every API call you need to send it the entire conversation history A more appropriate term is “stateless”. LLM responses are certainly not idempotent, as they are not even deterministic.

LLMs are, in theory, deterministic. Sampling is not intrinsic to LLMs. Greedy decoding a single batch in most libraries will give you mostly deterministic outputs. Higher batch sizes can increase variance. But all of this is down to CUDA and/or kernel implementation issues.

It's a more or less solved problem (e.g. DeepSeek has batch-invariant kernels, vLLM also has reproducible outputs etc), with an obvious caveat that you need to lock your hardware setup, model snapshot, and inference stack if you want any useful reproducibility across longer time periods. It's up to specific providers to actually implement it to any extent, of course commercial providers are incentivized to do the opposite.

Re: Qwen3.8-Max: A New Bar for Coding and Cowork

#363

Post by kcb »

tesnorindian wrote: post #242

AWS Bedrock still lacks the support for the latest open weights models like GLM 5.2, DSV4 Flash 0731, Kimi K3 etc. Even they only support Qwen 3 which is a very old model. Any specific reasons they are reluctant to support open weights models?

If I recall most of the open weight models have provisions about large scale commercial hosting. So probably licensing issues.

Re: Qwen3.8-Max: A New Bar for Coding and Cowork

#364

Post by kopirgan »

bonesss wrote: post #235
applicative wrote: post #108

Earlier quoted context omitted.

Does this desideratum make any sense? The whole point is that you write to it in English or Urdu, and it writes the specified code. If you cut off everything but the code writing, you cut off everything.

Domain modelling as well: we have clear programming concepts, but the kind of autocomplete that can autocomplete a customer sales process needs to be able to refer/leverage ontological connections on words like ‘bass’, ‘season’, ‘rebate’, and ‘jamboree’. Intuitively it feels like focused models should be better models, but human programmers are ‘better’ knowing programming alongside general stuff. We’re not reduced b…

Thanks..non AI expert here just wanted to understand..cos one of the most talked about topics is running models locally on cheaper hardware. Helps many esp in poorer places.

Would be nice if a genuinely useful coding model can fit into an old laptop, give decent performance and know nothing but (say) Go or c language.

But I see yours + others points.

Re: Qwen3.8-Max: A New Bar for Coding and Cowork

#365

Post by SillyUsername »

toshinoriyagi wrote: post #8

They've also announced Qwen3.8-27B being released open-weight next week. Qwen3.6-27B is widely regarded as one of the best local models, especially since nothing else comes close to it, that isn't benchmaxxed, without being significantly larger. If 3.8 truly improves upon it that would be awesome.

Do you have a news source for 3.8 27B pls?

Re: Qwen3.8-Max: A New Bar for Coding and Cowork

#366

Post by Gracana »

jbellis wrote: post #127
kopirgan wrote: post #53

Can a model be stripped off anything not relevant to coding and get a lot lighter? Or is that impossible? Just like we have professors with specialisation wondering if AI models can also be so.

Yes, look up REAP.

Linking here since the pruning method you're talking about wasn't the top search result when I looked: https://github.com/CerebrasResearch/reap

Re: Qwen3.8-Max: A New Bar for Coding and Cowork

#367

Post by owebmaster »

credit_guy wrote: post #353
me551ah wrote: post #257

This makes me wonder if AI companies even have a MOAT in the first place. All requests to an LLM are idempotent, for every API call you need to send it the entire conversation history so that it can process it. LLMs do not learn or remember anything, which makes it super easy for users to switch LLMs on the fly. Most popular AI frameworks, make this a one-liner change these days. And that makes me wonder if the trill…

They have 2 moats. The first is the compute. OpenAI and Anthropic secured huge amounts of compute, Google, Meta and xAI have their own huge datacenters. Now anyone can rent some cloud machines and start serving Kimi K3, but it's going to be impossible to get to a similar scale as the big 5 above. And inference has economies of scale: the more people you serve in parallel, the more efficient you are. The second is the…

your post helped me realize a change Meta is pursuing on Instagram that is to give more weight to captions and long text posts so they can have more data to training that would usually go to websites/Google. Even AI slop is good for this.

Re: Qwen3.8-Max: A New Bar for Coding and Cowork

#368

Post by marci »

SillyUsername wrote: post #365
toshinoriyagi wrote: post #8

They've also announced Qwen3.8-27B being released open-weight next week. Qwen3.6-27B is widely regarded as one of the best local models, especially since nothing else comes close to it, that isn't benchmaxxed, without being significantly larger. If 3.8 truly improves upon it that would be awesome.

Do you have a news source for 3.8 27B pls?

https://xcancel.com/Alibaba_Qwen/status/2084100707423289643#...

Re: Qwen3.8-Max: A New Bar for Coding and Cowork

#369

Post by owebmaster »

ryanjshaw wrote: post #314
me551ah wrote: post #257

This makes me wonder if AI companies even have a MOAT in the first place. All requests to an LLM are idempotent, for every API call you need to send it the entire conversation history so that it can process it. LLMs do not learn or remember anything, which makes it super easy for users to switch LLMs on the fly. Most popular AI frameworks, make this a one-liner change these days. And that makes me wonder if the trill…

> Cause if that is justified, then Kimi, Qwen, Deepseek etc are also valued at a trillion dollars It’s more like a bunch of people are placing different bets. Only a few bets are going to generate a return, possibly only one, but the profit on that one bet will make it all worthwhile. That’s the theory, anyway.

There's no mandate that says any of those bets are going to generate a return

Re: Qwen3.8-Max: A New Bar for Coding and Cowork

#370

Post by jurgenburgen »

websap wrote: post #291
badsectoracula wrote: post #222

Earlier quoted context omitted.

A local model needs 0 investment and 0 commitment, takes literal minutes to get started (especially if you have someone who is into that stuff showing you the ropes) and if you end up disliking the experience of using AI you can just `rm -fr` it and forget the whole thing existed.

Needs 0 investment and 0 committment? - You at least need a capable machine, so that's not 0 monetary investment. - You need to spend at least an hour decicding between ollama, llamacp, mlx, etc. - You need to find the correct quantized version of the model that works for you based on the architecture. - You need to figure out the correct context window size to get reasonable performance. - You need to setup a harnes…

> Or, you could sign up for Opencode for $10 and just be productive.

You forgot the step before where you spend months waiting for security to vet it, legal to sign off and procurement to approve it.

Or you could use hardware your team has lying around. Everyone isn’t working on cloud-hosted CRUD APIs.

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