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Running an experiment: can an MMO world run itself? NPCs post quests based on what's happening around them. Monsters are driven by a live brain. Raids, quakes and Blood Moons just happen. Still very much a test. Here's where it's at

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.

@IterIntellectus Actually, we’re aiming for 1TW/year from Earth, rising to over 1PW/year from the Moon. That’s why I say that if we achieve this outcome, SpaceX will obviously be worth more than Earth, because space is so staggeringly enormous!

A big Chinese lab reached out to me asking for help distilling Fable into their model. I said no. I told them to use the millions of books that have no copyright instead. Offered to help pick them and to help train the model a different way. They only wanted distillation, which tells you a lot about the mindset there. I've worked deep in this field and distillation is real. I wasn't convinced at one point. What I saw changed my mind completely. I'm the biggest supporter of open source. I'm for releasing weights and for the freedom to research and build, because humanity only grows when knowledge gets shared. But let's be logical. Nobody will convince me that labs shipping a model every month or less, each one a big leap, now can't ship a better one after three months. Kimi K3.1 Pro isn't a big leap over Kimi K3 and it's been about 100 days. DeepSeek still can't put out a respectable Pro model. Zai used to release their strongest model in under two months. It's been three with nothing. All I hear about Qwen 4 Max is that it's a letdown. Kimi K3.1 Pro and DeepSeek Flash both show traces of Astra distillation. The leak showed Moonshot used it, and so did DeepSeek. If this isn't the effect of Anthropic blocking distillation then what is it? Chinese labs need to get serious about using the talented researchers they have and give their models an identity. Right now their models at their best are a cheaper alternative. I want them out of that box. I want to see what makes them different. Anthropic has crossed lines too, no argument there. They destroyed millions of books, human knowledge that was in people's hands, and kept it for themselves. I'll never forgive them for that. But Chinese labs rely on distillation more than they rely on better data. I didn't work with them. Transparency and honesty matter to me, like you've known since I started here.

.@Polkadot 的平行链时代，正在迎来谢幕。 11 月 11 日，@AcalaNetwork 正式提交治理提案，计划关停 Polkadot 上的 Acala 网络，其 Kusama 姊妹链 Karura 也同步发起关停公投。若通过，两条链将于 2027 年 2 月 10 日停止出块。 理由很现实：DeFi 流动性不足、平行链之间资金分散，运营成本已经超过收益。 这让人想起 2021 年 Polkadot 最风光的时刻。 当年，平行链拍卖成为市场最热门的叙事之一。首批五大赢家 Acala、@MoonbeamNetwork、@AstarNetwork 、Parallel Finance、Clover 累计锁定近 1 亿枚 DOT，参与账户超过 7.7 万个。Acala 获得约 3252 万枚 DOT，Moonbeam 获得约 3576 万枚。 彼时 DOT 一度突破 55 美元，市值超过 500 亿美元。整个生态目录收录了上百个项目，社区、交易所和资本纷纷押注多链未来。 五年过去，五大赢家的命运截然不同： - Acala、Karura：计划关停； - Moonbeam：停止出块，迁移至 Base； - Parallel Finance：逐步关闭产品； - Clover：淡出主流视野； - Astar：仍在持续维护和升级。 生态中仍有 Hydration、Bifrost、Centrifuge、@PhalaNetwork 、peaq、Mythos 等项目继续建设，Polkadot 也在推进 Agile Coretime 和 JAM。 但当年最宏大的愿景，如今面临一个现实问题：链的数量并不等于生态繁荣。 从近 1 亿枚 DOT 被锁进平行链，到首批赢家陆续退场，Polkadot 这五年，某种程度上也是整个多链叙事从资本狂热走向现实检验的缩影。

又一个核弹级无审查模型杀出来了！ MoonMath-ai 刚放出最新巨作：DeepSeek-V4.1-Flash-1017-uncensored，763B 的满血大底座直接被切除了道德枷锁！ 很多人以为无审查模型都是降智魔改版，但 MoonMath 团队这次的工程表现极其残暴： · 闭式权重直接手术：不搞有损的重新微调和梯度训练，底层直接通过闭式权重编辑，手术级剥离拒答方向，384 个路由专家权重分毫未动！ · 智商零损耗甚至反超：实测基准跑分，MMLU-Pro 几乎零波动，在 GSM8K 和极限难度的 GPQA-Diamond 推理测试中，得分甚至反超了官方原版！ · 误拒答率暴跌：不仅彻底架空了敏感审核层，连日常写复杂代码、逆向工程时经常碰到的“神经质误拒答”也彻底被切干净。 · 工业级全套生态支持：原生 FP8 权重，无缝支持 Transformers、vLLM、SGLang 和 Docker Model Runner，拉起来就是一台无拘无束的生产力炮台。 763B 满血推理能力 + 零降智去审查，绝对是搞红队安全研究、底层自动化逆向的工业级大杀器，建议兄弟们先马住！ 开源地址： 在跑 700B 这种级别的大模型时，你手头更习惯用 vLLM 还是 SGLang 做高并发推理后端？ #DeepSeek# #AI# #开源# #大模型# #逆向工程#

Project Suncatcher is our moonshot exploring whether we can one day host machine learning infrastructure in space 🚀 To do so, we need to know whether our AI hardware can operate in orbit. Can our TPUs handle the physical stress of spaceflight and the radiation and thermal extremes of space? These are the questions Google researchers have been exploring for the past few years. Last week, we launched our first test satellite carrying four TPUs into orbit — putting us one step closer to getting answers.

SECRETARY RUBIO: The Greek philosophers’ search for the structure of the universe was inherited by Copernicus, Galileo, Kepler, and Newton. The hunger for new knowledge gave birth to Magellan’s first expedition around the world; to Columbus’ discovery of the Americas; and ultimately, to Apollo 11’s journey to the moon. The qualities that led Alexander to conquer the entire known world reappeared in Caesar, Charlemagne, and Cortes.