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mekpro

587 カルマ登録 14 年前

投稿

SWE-1.7 Reach Near GPT 5.5 and Opus Intelligence

cognition.com
271 ポイント·投稿者 mekpro·3 日前·139 コメント

Meta Keeps Delaying the Release of Its New AI Model to Developers

wsj.com
67 ポイント·投稿者 mekpro·先月·26 コメント

コメント

mekpro
·14 日前·議論
codex spark is not large model though, much weaker than standard model.
mekpro
·15 日前·議論
We need more coding benchmark score. Not sure that winning terminalbench 2.1 alone is a clear win over Fable/Mythos yet.
mekpro
·先月·議論
source ?
mekpro
·先月·議論
API server is not hard problem and not make sense for indefinite postpone. I think the more likely explanation is model quality.

Too bad for Meta, and very sad day Llama.
mekpro
·先月·議論
The technical report is very detailed and would 'reinforcement learning' of future researchers, Thanks Microsoft!
mekpro
·先月·議論
Yes, 300 MW from SpaceX helps a lot, but I think that’s mainly to support Opus demand, which has grown faster than expected. If Mythos is roughly 5× more expensive to serve than Opus, as the pricing suggests, then 300 MW is nowhere near enough to enable large-scale deployment of Mythos.

As an ordinary developer who relies on a $20–$200/month subscription, I feel disappointed by the release of a paper describing a model that I can’t actually use.
mekpro
·先月·議論
It’s clear that Anthropic has run out of the compute capacity needed to serve Mythos publicly.

They’re using security concerns to mask their inability to deliver the model at scale, while still trying to maintain their lead over OpenAI. As a result, they’ve chosen to release it privately under the banner of an “ethical” rollout.
mekpro
·5 か月前·議論
Opus is definitely in its own league. I use Kimi/Gemini-cli code regularly to save cost and from my experience, Kimi 2.5 is more solid than Gemini Flash 3.0 for coding. While Gemini Flash 3.0 is generally faster, it usually break the syntax and skip important prompt. Kimi 2.5 can write very good code and can plan very well.
mekpro
·5 か月前·議論
Except that, In OpenRouter, Deepseek always maintain in Top 10 Ranking. Although I did not use it personally, i believe that their main advantage over other model is price/performance.
mekpro
·6 か月前·議論
I think the opposite. Having NVIDIA investing in TSMC's bleeding-edge process node should benefit Apple rather than disadvantage.

It means that Apple doesn't have to be sole investor in latest node development which is more harder to justify, especially in the year where smartphone upgrade cycle is slowdown. Having NVIDIA (and AI boom) in the picture should help Apple reduce CAPEX for their semi-conductor investment.
mekpro
·7 か月前·議論
They are so beautiful that i dont want any of these been stole by AI.
mekpro
·8 か月前·議論
How this improvement translate into real world agentic coding task ?
mekpro
·2 年前·議論
As a quick estimation, the size of q4 quantized model usually be around 60-70% of the model's parameter. You can preciselly check the quantized model size from .gguf files hosted in huggingface.