That may be the current hubub, But how they built on other open concepts like Cold Start + RL + Rejection Sampling etc... is the big thing. I am not at all c

That may be the current hubub, But how they built on other open concepts like Cold Start + RL + Rejection Sampling etc... is the big thing. I am not at all connected to whomever this is, but as I am on a "confidential project for a confidential client" it seems to simplify what I would overcomplicate anyway. https://youtu.be/Pabqg33sUrg https://youtu.be/Pabqg33sUrg Some of us need to improve domain specificity, and are more interested in targeted capabilities. DeepSeek-R1-Distill-Qwen-32B itself is pretty good to be honest, but it is more aspirational how easy it is to add reasoning to any base model IMHO. Those of us who remember Simon’s satisficing principle, realize that often complex problems sub-optimal solutions. We don't need, want or expect some central omniscient and omnipotent AGI, we want tools that are sufficient to solve real world problems. Obviously having good reliable domain specific CoT for cold start etc... is hard. But not nearly as hard as hitching the companies success to MS/OpenAI. While there is a lot of noise...I think some people are missing the forrest for the trees here. Deepseek is more about moving the decision to use OpenAI for some needs from a single source vendor to one of convenience for us. For those of us who are in the camp that OpenAI was never going to reach their "AGI" claims because of fundamental issues like the frame problem, this is huge. Ya...we won't be training our own models for some silly budget of ~$5M like the press is claiming. But we won't have to for some use cases... I think it will take time and someone on a project with not such a strict NDA to demonstrate just how much this 'completes' things in ways that RAG, functions etc... didn't. Or I just possibly got lucky with my needs? who knows.