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

Post by cicko » Mon, Aug 03, 2026, 9:59 AM UTC

nozzlegear wrote: post #27

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.

Qwen3.6-35B is my daily driver for AI, and what convinced me to cancel my Claude subscription back in April. The Qwen3.6 line is easily the best local model I've tried, and I've tried a lot. I've got it diligently grinding away on my laptop right now, reviewing and fixing some bugs in my F# code.

Post by me551ah » Mon, Aug 03, 2026, 10:00 AM UTC

regularfry wrote: post #269

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…

With their current API approach they're essentially a commodity. They need to start moving parts of the harness behind the API, otherwise they'll remain a commodity. Recursive self-improvement changes the parameters a bit, especially for the market-leaders, and it's the one thing that makes me wonder if they'll be able to extend their lead faster than the smaller labs can keep up, but it's an option available to ever…

Post by jcoder » Mon, Aug 03, 2026, 10:04 AM UTC

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…

Post by noodletheworld » Mon, Aug 03, 2026, 10:06 AM UTC

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…

Post by victorbjorklund » Mon, Aug 03, 2026, 10:17 AM UTC

seanmcdirmid wrote: post #236

perbu wrote: post #147

Earlier quoted context omitted.

First and foremost, China isn't energy-limited like the US is. A modern grid, lost of cheap power coming online every day. The worlds most advance ultra-high voltage transport links are tying the deserts in the west to the cities in the south. It probably sucks for you if the proposed powerline goes through your house, but the need of the many come first. Having cheap and plentiful power is a huge market advantage. I…

I think this is really a rosy image of China’s electrify situation. They have huge needs in the east and green energy in the west, they can’t build their UHV links fast enough. They are doing ok at keeping up, but there isn’t a surplus of cheap electricity lying around in the same places where you have cheap water as well.