81–90 of 151 posts
Okay, let's give software engineers a break for a bit and focus on obsoleting other high-linguistic context occupations.
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id rather iterate multiple times than wait 15 minutes to notice it made a mistake.
Again, my point is exactly the opposite. Higher quality implies a mistake isn't made in a significant % of cases.
While I am skeptical of the results here, I am very excited for this new trend of making models faster. Running capable models at 1k TPS is more valuable for me than running better models at 30 TPS. I can only imagine the trend continues to move from "let's only make models smarter" to just incremental intelligence gains but with step improvements in speed.
Why? I'm personally on the opposite end. Less babysitting/higher quality means more time goes back to me/the user. 1000tps of bad code means you have to keep validating the output and circling back.
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It's based on an open weight model (Kimi 2.7) so shouldn't it also be open weight?
There is no obligation to do that. I think the landscape would be very different now if one of the big labs had released an earlier “frontier” model under copyleft that requires sharing fine tunes. I hope it still happens.
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There is no obligation to do that. I think the landscape would be very different now if one of the big labs had released an earlier “frontier” model under copyleft that requires sharing fine tunes. I hope it still happens.
Dario is convinced that will create SkyNet, and so no, it will never happen. Only the blessed members of the True Church Of Effective Altruism can approach the Ark of the Covenant. The unwashed cannot be trusted.
Would have been worth a consideration if it could have been used beyond it's own harness. Unfortunately, doesn't seem to be the case. https://x.com/theodormarcu/status/2074896486047834380
On artificialanalysis.ai, Kimi 2.7 Code is way worse than GLM 5.2 at everything (general intelligence, coding, agentic tasks). But here, both Kimi 2.7 and its derivative SWE-1.7 are ahead of GLM 5.2. This tells me the benchmarks they use are cherry-picked.
Which benchmarks would you have chosen instead, and why?
We need more models that optimize for coding and that can be cheaper than frontier models, like what SWE 1.7 and composer 2.5 are trying to do. I don't think there's an effort to make something GLM-5.2 level but focused only on coding.
Defining what "coding" means now, and how quickly we fall off the capability cliff seems increasingly important. Today my "coding" sessions often enough begin with real life problems, where I discuss domain or inter-domain things, ranging from business, economics, psychology, etc. Being able to do all of that with one model is something I am willing to pay a premium for. Of course not having to pay the premium, becau…
intuition is that your sessions consists of 10% of domain related reasoning, and 90% of code plumbing. Those 90% could be moved to cheap and efficient specialized and focused model.
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Qwen was doing something like this with their coder models. But alas, they seem not to be releasing those anymore. Last one was Qwen3-coder-next.
Its crazy that OpenAI and Anthropic themselves aren't doing that. No attempts at reducing inference cost for code as far as I know from them.