SuSu_酥酥👅 (@NFT_Chen)

黄仁勋辞职当天就请了顶级律所Paul Weiss！ 不是普通律师，是专门打政府调查、高管危机的诉讼大佬Melinda Haag和Walter Brown。 公司已经请Wachtell查他，他个人再单独请Paul Weiss…… 这不是在处理家事，是在准备应对FBI、国会、出口管制和股东诉讼。 老黄这波，是在主动切割，还是在提前站好位置？ 这场戏，才刚开始。 #黄仁勋# #NVDA# #PaulWeiss#

I have a major result to share. I’ve found conclusive forensic evidence that the top 4 Chinese AI labs—DeepSeek, Moonshot, MiniMax and Ant Group—have built their newest models on NVIDIA’s most advanced Blackwell chips, which are barred from export to China. This sounds insane, but I discovered that almost every AI model carries a hidden signature of the GPU that trained it. When a model is first set up, the starting values of its weights are laid out in a pattern that depends on the exact GPU. Each combination of GPU and seed number produces a unique pattern—a sort of fingerprint of the hardware used to create the model. Traces of that pattern survive training into the published model. Again, this evidence isn’t based on any insider information or leaking. It’s based on a forensic analysis of the model weights released by the companies themselves. I searched more than six trillion combinations of chip layout and seed number and matched them to open-weights models. In doing so, I found conclusive evidence that six Chinese AI models—including DeepSeek-V4-Pro, Kimi K3, and MiniMax-M3—were initialized on NVIDIA Blackwell systems. Four of the models match Grace Blackwell, the GB200 and GB300 parts in NVIDIA’s flagship liquid-cooled rack-scale systems. I then verified these results on real hardware. Running unmodified PyTorch with the seeds I’d recovered, a Blackwell B300 reproduced DeepSeek-V4-Flash’s untouched starting weights bit for bit, and a rented GB200 reproduced Kimi K3’s to a correlation of 0.999995. A Hopper H100 reproduced none of DeepSeek-V4-Flash’s, and changing the seed by one broke the match. (DeepSeek’s seed is 953049583 by the way.) The method also was verified to match 24 known open-weights models to the hardware that developers said they used. It is exceedingly unlikely that these results are a coincidence. Across six trillion combinations, the best accidental match should reach only about 8 sigma (i.e., eight standard deviations above what random chance would produce). The real matches range from 49 to 1,385 sigma, and even the weakest single test hits 20. These were data-center-scale training runs. With some analysis of release schedules and model details, I estimate that the largest, Kimi K3 and DeepSeek-V4-Pro, used 2,000 to 3,000 Blackwell GPUs each. Clusters of this size cost on the order of $150 to $200 million each to buy, and across these labs alone would add up to more than half a billion dollars of export-controlled GPUs, not counting experimental compute or otherwise. It’s quite likely that we see multi-billion dollar expenditure on export-controlled GPUs. I am highly confident in this result, and it has been independently verified by senior researchers at OpenAI and Anthropic. You can read the full report at There is much more I’ve identified about these models through other forms of checkpoint forensics like this, and will have more to share soon.

😱震惊！未来Hy模型可能走向闭源！姚顺雨对未来是否继续开源含糊其辞！ 腾讯混元AI负责人姚顺雨被当场问：如果Hy成为世界第一，还会继续开源吗？ 他居然没有直接回答“是”！ 姚顺雨的回应如下： 1️⃣只有还没领先的模型，开源才能吸引开发者、扩大影响力 2️⃣开源可以分享知识、推动技术社区发展 3️⃣未来模型的网络攻击和生物相关能力可能强大到造成真实伤害 4️⃣网络攻防无法完全分离，更强的推理+编程能力本身就会带来攻击能力 这是不是意味着一旦混元登顶，开源就没了？！ #腾讯混元# #姚顺雨# #开源AI# #AI闭源# #Hy4# #马化腾#

🚨OpenAI 个人 Agent 名字疑似曝光：不叫 o，叫 Dots ！ 今晚 DevDay 前，构建字符串流出： 🔹短信 / 电话 / Slack / 邮件都能找它 🔹可代下单，先问你同不同意 🔹每个 Dot 有专属 3D 角色，还能按它对你的了解自己长样子 🔹自带虚拟机「Your dot’s computer」，能存密码帮你登录网页 🔹可自己干活、可随时暂停 企业侧 4 月的 Workspace Agents（内部常称 Aeon）像是前身。 那之前传的 “o” 会不会是总控，Dots 才是一个个小人？ 今晚 10am PT 看 Sam Altman 会不会亲手点亮这些点。 #OpenAI# #Dots# #AIAgent# #DevDay# #ChatGPT# #OpenAIDevDay# #SamAltman# #Tibo# #Codex#