February is one of those months... - Moonshot AI's Kimi K2.5 (Feb 2) - z. AI GLM 5 (Feb 12) - MiniMax M2.5 (Feb 12) - ByteDance Seed-2.0 (Feb 13) - Nanbeige 4.1 3B (Feb 13) - Qwen 3.5 (Feb 15) - Cohere's Tiny Aya (Feb 17) (+Hopefully DeepSeek V4 soon) Anything I forgot?
Yeah, in an ideal world, we would use AI to enable experts to do higher-quality work. But in the real world, they are also expected take on a wide range of additional responsibilities that detract (& distract) from their core work.

> "Less than half the tokens of 5.2-Codex for same tasks" That one line already says a lot. There is no assumption anymore that compute or budget is infinite in 2026. But if you can get better modeling performance while using...
Yes, Moltbook (by clawdbot) is still next-token prediction combined with some looping, orchestration, and recursion. And that is exactly what makes this so fascinating. (It is also why understanding how LLMs actually work really does pay off. Lets us see through the...
Had a fun chat with @mattturck the other day where we talked about a bunch of interesting LLM stuff... Basically everything from the future of the transformer architecture to inference-time scaling as a recent MVP of LLM performance:

It's been a while since I did an LLM architecture post. Just stumbled upon the Arcee AI Trinity Large release + technical report released yesterday and couldn't resist: - 400B param MoE (13B active params) - Base model performance similar to GLM...

Another really interesting paper from my 2025 bookmarked papers: On the Interplay of Pre-Training, Mid-Training, and RL on Reasoning Language Models (https://t.co/UjhiJW643U). In short, RL is most effective when applied to data that is neither too close to nor too far...