If they trained it well, and can do computer use, it will be a new era. Companies can keep PCs, put Qwen 3.8 27b on it and get rid of the employees, lol...
I am surprised that they keep going with it, seeing how fast it improves and basically soon running themselves too out of business.
What's even their end goal? Open source models make sense, if profit is not the target, but for OpenAI and the rest, once they achieve "AGI", don't they basically become useless?
It wasn't the point, but early LLMs would make mistakes so often that writing fiction full of hilarious nonsense was one of the only applications. Now that they make mistakes less often, you need some other source of high-entropy randomness to reinject creativity into the story.
Kinda, more accurately the current goal for labs is to produce economic value to justify high valuations / capex, thus having to RL on correct answers rather than interesting ones.
If you could access the current models as base models I guess you'd still get very interesting hilarious stuff.
Genuine question, is the reasoning chain different from clicking the status bar under a reply and watching it "think"? Or selecting the "Thinking" transcript view in Claude Code? (both on the desktop app). Seems to me that is very out in the open
That's a summarized and filtered view of the actual reasoning.
OpenAI and Anthropic guard the real reasoning closely. Users have never been able to see it and the API returns an encrypted blob instead of legible reasoning.
So nowadays the hardware and hosting providers must be in an optimization race, whoever can make the model just a bit smaller or more efficient (to fit on fewer/less powerful cards) will have a huge advantage and can make a lot of money.
I am curios what's the most profitable thing to "plant" (agriculture analogy) on the land (cards) that you have have: web hosting, vps, llms, image/video generation, etc
Again, their "none" version costs more than "low", and says zero reasoning tokens, makes no sense[1].
As always, the "low" version seems to be the best price/perf ratio for factual answers and tool usage, and high one for creative tasks (coding, generating UIs, etc.)
I have a spare 3090 that I want to use to off-load some tasks from Claude to a local model (probably Qwen 3.6 27b), any success with that? Is it good enough to follow some tasks, coding requirements or browser usage?
Alone, Qwen can close basic bug tickets. Passing up and passing down have not really worked that well for me. In some cases passing Qwen’s output up to Claude can be actively harmful bc it can anchor the solution space to a bad approach. Passing down isn’t worth the time. By the time I have Claude plan something, most of the time it’s 1/3 or 1/4 more tokens to just have Claude do the implementation.
Where it has worked is to have Qwen not just write the proposed solution, but also include all the files it consulted and why it did or didn’t consider them. The documentation is often longer than the patch, but it lets me prompt Claude differently. I tell Claude to challenge the implementation and find holes, edge cases and things not considered. Maybe it’s my imagination, but Claude kicks it into another gear when you tell it to evaluate the results of another model.
Maybe this is a pure preference but I find Qwen to be bad at UI and these edits Claude will often let go.
One thing I haven’t tried, but will at some point is to hand Qwen all my half assed ideas and things I can’t be bothered to spec out. Probably not to take the output, but to help me draft a more complete issue because it’s only costing me electricity.
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