OreChou
261 posts
OreChou
@OreoChou
程序员 🧑💻 老Model Y 车主 🚗 数字爱好者 📹 喜好：Reading 📖 Coding 💻 Playing 🎮 目前专注于 AI 以及小而美的产品创造
长征十号乙运载火箭今日成功实现一子级可控回收。后续，长征十号乙运载火箭研制团队将持续优化火箭性能，加快重复使用火箭技术的迭代升级，预计将在今年年底前完成一子级火箭复用飞行。（央视） #行情 卫星ETF广发、卫星ETF易方达双双涨停。
(1) Today we're releasing Muse Spark 1.1 -- a strong agentic and coding model at a very low price. It's available through our new Meta Model API and in Meta AI.
Grok 4.5 is looking like a success with help from Cursor data but underneath the surface we expect future Grok/Cursor model training is likely to speed up in the coming months. We've spent several months getting up to speed on the SpaceXAI business, especially the tech underneath it. The C rewrite was under appreciated by the investor community so we dug in to quantify its impact. including building a physics first model that functions as a stopwatch for the SpaceXAI model factory. Bottomline: C-rewrite gets SpaceXAI faster model cycles, leveraging 33% more tokens/second/GPU against SOTA competition resulting in the potential to shipping new models every ~3.5 weeks. two core learnings from this modeling exercise: 1: the training cycle speed up is primarily coming from RL (not pretraining) where the increased tokens/second/GPU advantage can shave up 2+ weeks off full model training cycle. 2: the rewrite itself should compound the time savings as model sizes grow. at ~2T shaving off 2-3 weeks, ~8 weeks at 6T, and 15 weeks at 10T. Note: a 20T parameter model likely runs into a data bottleneck prior to a training speed bottleneck but the directional advantage stands. Also we assume tokens/second/gpu advantage will melt over time as competitors try to match it. when you do the math, in true SpaceX and Elon fashion, it looks like they are attempting to build a SOTA model factory that can pump out bigger models faster than anyone else. Full analysis here for the public: research.33fg.com/analysis/what-…