Dr. A

不一样的视野：来自社会最底层，关注社会公平正义。“为人民服务”是中共婊子裆树立的牌坊。灭共救中国！没有共产党，才有新中国！支持台湾中华民国民主自由统一大陆，抵制大陆纳粹中共独裁侵吞台湾！#杀pig头灭CCP救China

[Dr. A @agostic]

新中华千古罪人：#习近平 和 #彭丽媛—祸国殃民！

[Milk Road AI @MilkRoadAI]

This is WILD!

One week before SpaceX's historic IPO, Google signed a deal to pay SpaceX $920 million per month from October 2026 through June 2029 for access to 110,000 Nvidia GPUs, CPUs, and related infrastructure (Save this).

That is $11 billion per year and up to $30 billion over the life of the contract.

This comes less than a month after Anthropic committed $1.25 billion per month for full access to the Colossus 1 data center in Memphis, 200,000+ GPUs, 300+ megawatts of power capacity, through 2029.

Two of the most consequential AI labs in the world combined committed value over $70 billion.

The question that haunted SpaceX's IPO roadshow was why did Elon keep spending billions constructing Colossus, Macro Hard and Macro Harder, three facilities totaling nearly 2 gigawatts of AI compute when xAI's revenue wasn't yet on the same trajectory as OpenAI or Anthropic?

Wall Street was pricing in a risk that Elon was building capacity ahead of revenue which would mean sustained cash burn without a clear payback timeline.

That concern was legitimate on its face, because xAI had been aggressive on model development but had not yet demonstrated the enterprise revenue numbers to justify the infrastructure cost.

The answer is that the compute itself was always the product.

Amazon has AWS, Microsoft has Azure, Google has Google Cloud, Elon just confirmed that he has been quietly building the fourth major hyperscale AI cloud and his first two paying customers are Google and Anthropic, the very companies most aggressively competing in the AI race.

xAI's Colossus facility in Memphis was built at a speed that no traditional data center developer could match, it went from groundbreaking to operational in roughly 122 days.

That is what happens when you have direct Nvidia relationships, a construction operation built around SpaceX-style execution, and a founder who treats infrastructure buildout the same way he treats rocket launches: compress every timeline and eliminate every bottleneck.

The result is that SpaceX now has three operational facilities, Colossus, Macro Hard, and Macro Harder with Macro Hard and Macro Harder in Blackwell architecture running 1.2 gigawatts combined.

Colossus 1, built on H100s and optimized for inference, is the facility that went to Anthropic first.

The Blackwell-era facilities are where the next-generation training workloads happen and Google's deal suggests they are renting into that capacity as it comes online through the second half of 2026.

Elon's compute leasing business would generate approximately $45 billion in incremental annual revenue on top of the mid-$20 billion range analysts had been modeling for SpaceX more than enough to fully subsidize the infrastructure investment and take the financial pressure off xAI delivering immediate AI product revenue.

That changes the entire valuation conversation of SpaceX completely!

Milk road remains bullish on Space and come join Milk Road Pro and get our full SpaceX IPO breakdown, how we're thinking about the $1.75 trillion valuation and our entire AI thesis. Link below!

[Gavin Baker @GavinSBaker]

Really fun to interview my old friend Bret Johnsen in Mission Control.

Three parts of the @SpaceX story that I wish were more widely discussed:

SpaceX has created thousands of good blue-collar jobs: welders, machinists, electricians. Everyone talks about the need to bring high-paying, blue-collar jobs back to America. SpaceX and Tesla are making that happen. To the best of my knowledge, they have created more manufacturing jobs in the US than just about any other American company over the last ten years. It’s hard to imagine our nascent industrial renaissance succeeding without these companies.

SpaceX was started with the goal of putting humans on Mars. And along the way, they have massively improved life for many humans on Earth. Mars may be a starter planet, but Earth is our planet, and the technologies developed at SpaceX are already in use today connecting and safeguarding the people of Earth. Starlink is a really efficient way to bring internet to low-income countries. In Kenya’s remote Murang’a County, Starlink has made it possible for patients in rural villages to consult with medical specialists via telemedicine. In the rainforests of Brazil, Starlink has connected schools to reliable high-speed internet that will provide more educational opportunities to students. Here in America, Starlink has proven vital to emergency teams responding to natural disasters. During Hurricane Helene, the Starlink hubs dropped into North Carolina and East Tennessee were often the only contact point between cut-off towns and the outside world. Literally life-saving.

This IPO will be a big milestone for the company. It’s important to celebrate this, while also remembering that making humanity multi-planetary is the ultimate goal. Going to Mars is really hard. There have been many setbacks thus far, ranging from fiery explosions to failed landings. There will be many more. Ad Astra Per Aspera. But SpaceX is at its best *after* a setback imo. Their first 3 launches were “failures”. Had the 4th not succeeded, there might not be a SpaceX today. The company’s success in the face of such daunting odds is a testament to the resilience of the culture and absolute commitment to the mission shared by every employee I’ve ever spoken with. Some of the world’s most talented engineers have chosen to live in Airstreams at Starbase away from their families for weeks on end in service of this goal. I will never forget the welders who told me they signed every weld because they wanted to be accountable if they were responsible for a failure. True missionaries, all of them.

I am grateful to every single person at SpaceX for helping to make the future as inspirational as possible. And I will be even more grateful if I get to see a blue sunset on Mars!

More info on https://t.co/dLOPlKr0Un

[Jaynit @jaynitx]

3 days ago, Elon Musk sat in front of JP Morgan’s 3,500 wealthiest investors and explained why the AI economy is moving to space:

1. Starship is the first rocket in history designed to be fully reusable. Every other mode of transport... planes, cars, ships... you take reusability for granted. Rockets have always been thrown away after one use. That ends with Starship. Once you achieve full reusability, the only cost is fuel. Starship runs on liquid oxygen and methane. Both are cheaper than jet fuel.

2. Sending cargo to orbit will soon cost less than international air freight. This is not a distant projection. It is the direct mathematical outcome of reusable rockets plus cheap propellant. The economics of space change entirely.

3. Starlink V3 is 10 to 20 times more capable than what's currently in orbit. The satellite is so large it can only launch on Starship. It cannot fit on any other rocket on Earth. 100 times more bandwidth. Half the latency. It may become the highest bandwidth, lowest latency communication system that exists.

4. AI and robots will consume bandwidth at a scale humans cannot picture. Peak human bandwidth is a few hundred bits per second. A computer runs at a trillion. The appetite of AI for data infrastructure will be unlike anything built for human use. Starlink V3 is being built for that world... not this one.

5. Data centers are moving to space. Not as an experiment. As the primary way to scale AI compute going forward. It is increasingly hard to build power plants on the ground. Nobody wants one near their home. Space removes that constraint entirely.

6. From the moon, you can scale to 1,000 terawatts of compute per year. From Earth... maybe 1. The moon has no atmosphere and one-sixth Earth's gravity. You can manufacture solar panels from moon materials and launch data centers with a railgun. No rockets needed. The math on this is not close.

7. Current human civilization uses less than one trillionth of the sun's energy output. You could scale to a million times Earth's entire economy and still be using less than one millionth of what the sun produces. The ceiling on what's possible is so far above us it barely registers as a ceiling.

8. There is not a single high-volume computer memory fab in America right now. Zero. The chips needed to build the AI future do not exist in sufficient quantity anywhere in the Western world. That is why SpaceX is building one. Not to compete. Because there is no other option.

9. SpaceX has been cash flow positive since around 2014. The IPO is not a distress move. Past funding rounds were not even fundraising... they were liquidity events for employees. The company bought back its own stock. The IPO is happening now because the next phase requires capital private markets cannot absorb.

10. The senior team has barely changed in over a decade. The CFO has been there 15 years. Musk joined as the seventh employee in 2002. He says people who believe in the mission don't leave. And above technical skill, he now looks for one thing... whether someone is genuinely a good person.

[Dr. A @agostic]

@jacksonhinkle 中国有句话：金玉其外，败絮其中。中共已经烂透了。

[Dr. A @agostic]

@jacksonhinkle On this day of June 4, 1989, the Chinese college students fought for freedom and lost their lives on Tiananmen Square. Now, there is no freedom at all that they can hold the 37th memorial anniversary in China. Even worse, rarely do new generations of Chinese know the disaster.

[Dr. A @agostic]

@ChineseEmbinUS On this day of June 4, 1989, the Chinese college students fought for freedom and lost their lives on Tiananmen Square. Now, there is no freedom at all that they can hold the 37th memorial anniversary in China. Even worse, rarely do new generations of Chinese know the disaster.

[Dr. A @agostic]

@ChnEmbassy_jp On this day of June 4, 1989, the Chinese college students fought for freedom and lost their lives on Tiananmen Square. Now, there is no freedom at all that they can hold the 37th memorial anniversary in China. Even worse, rarely do new generations of Chinese know the disaster.

[Dr. A @agostic]

@SpoxCHN_MaoNing On this day of June 4, 1989, the Chinese college students fought for freedom and lost their lives on Tiananmen Square. Now, there is no freedom at all that they can hold the 37th memorial anniversary in China. Even worse, rarely do new generations of Chinese know the disaster.

[Dr. A @agostic]

On this day of June 4, 1989, the Chinese college students fought for freedom and lost their lives on Tiananmen Square. Now, there is no freedom at all that they can hold the 37th memorial anniversary in China. Even worse, rarely do new generations of Chinese know the disaster.

[硅谷投资君 @guigu2029]

最近很火的“韬定律”，我觉得需要冷静看。

它不是一个能直接取代摩尔定律的“新物理定律”，更像是华为把后摩尔时代的一整套技术路线做了系统化表达：chiplet、3D 集成、逻辑折叠、先进封装、互连优化、软硬件协同、系统级架构优化。

过去摩尔定律主要靠“几何缩微”——晶体管越做越小。
韬定律强调的是“时间缩微”——让信号走得更短、更快，减少数据搬运和互连延迟，从系统层面继续提升性能。

这个方向本身是对的。

现在 AI 芯片的瓶颈早就不只是几纳米制程，而是 HBM、封装、互连、功耗、内存带宽、compiler/runtime、scale-out fabric，以及整机柜级系统效率。

所以“韬定律”有真实工程意义，尤其是在先进制程受限的情况下，通过系统设计和架构优化去弥补差距。

但问题是，市场上把它宣传成“颠覆半导体格局”“绕过先进制程限制”“挑战台积电和英伟达”，这个就有点过热了。

台积电本身就在做 CoWoS、SoIC、3D 封装、chiplet、背面供电、DTCO/STCO。
英伟达的壁垒也不是单颗 GPU，而是 CUDA、NVLink、NVSwitch、HBM 供应、CoWoS 产能、整机柜设计、networking 和 AI factory 交付能力。

所以我对“韬定律”的判断是：

有技术含量，但不是魔法。
有工程价值，但不是新物理定律。
对中国国产半导体是重要路线，但短期不会改变 TSMC / NVIDIA 的核心优势。

更准确地说，它代表的是后摩尔时代的一个趋势：

未来半导体竞争，不再只是“谁的制程更先进”，而是：

谁能把芯片、封装、内存、互连、软件、系统架构整合得更好。

这也是为什么 AI 芯片竞争会越来越像系统工程，而不只是芯片设计。
从这个角度看，“韬定律”值得关注，但不宜神化

[qinbafrank @qinbafrank]

细读华为何庭波的署名论文来理解“韬（τ）定律”，从这篇论文能看到“韬（τ）定律”在缩放时间理论上的五个核心要点，但是越细看你越会发现华为、英伟达、台积电几家在未来的演化迭代路径上的底层逻辑是一致的。详细聊聊我的理解。
1、先说“韬（τ）定律”的五个核心要点
1）LogicFolding / 逻辑折叠。
文中给出的定义是：LogicFolding 是一种设计方法，把数字电路、模拟电路和存储电路划分到垂直堆叠的有源层中，通过缩短关键路径走线来优化性能、功耗和面积。它不是简单把封装做厚，也不是普通 2.5D Chiplet，而是更接近“把原本二维平铺的逻辑电路折到垂直方向

2）Unified Bus统一总线，
传统 AI 集群需要��层协议栈：PCIe、NVLink 或专有互联、以太网或 InfiniBand、RDMA、软件消息传递等。每一层协议转换都会增加序列化、DMA 缓冲、握手和延迟。

Unified Bus 的目标是用一个在机箱内和机箱间都运行的单一协议，替代这些协议栈，并在整个系统中暴露原生内存语义。论文称，这能把远程访问延迟从传统协议栈的几十微秒级降到约 100 纳秒，沿主要通信轴线的系统 τ 降低约 500 倍；在机架规模上，使系统更接近一台结构一致的单一机器，内部称为 System-as-One-Chip / 系统即芯片。这其实就是芯片—服务器—机柜—数据中心级别的系统协同。

3）Hi-ONE近封装光引擎
何庭波的论文认为，当单个 AI 芯片带宽进入 Tb/s 级别时，铜缆布线会遇到体���、SerDes、散热、供电和可靠性限制。华为提出的Hi-ONE是一种近封装光互连节点引擎。文中称单 Hi-ONE 模块可提供 8 Tb/s 带宽，把 SerDes 传输距离从约 100 厘米缩短到约 5 厘米，同时把面板间传输距离从不足 1 米扩展到 100 米。

这说明华为的系统级路线也把光互连纳入核心架构，不只是外部光模块，而是更接近 near-package optical I/O / 近封装光 I/O。

4）3D Folding
论文认为，传统 2.5D AI 芯片存在一个几何矛盾：逻辑芯片面积按 N² 增长，所以计算能力按面积增长；但 HBM、SerDes、供电等资源主要沿封装边缘进入，带宽、I/O 和电源能力只按周长 N 增长。计算能力按 N² 扩，边缘资源按 N 扩，二者差距会越来越大。

3D Folding的作用是把原来位于边缘的资源转移到表面或垂直方向：电源通过背面供电和集成电压调节器，高速存储通过与逻辑混合键合，光 I/O 通过近封装 Hi-ONE，��而让内存、互连、电源和逻辑同步扩展。

华为把3D折叠视为 AI 加速器在 2030 年后继续扩展的核心拓扑。

5）逻辑与内存重新融合
论文还有一个重要产业判断：过去几十年，CPU 和内存是解耦发展的；但 AI 时代正在逆转这种解耦。

原因是AI工作负载对内存带宽、延迟、功耗和封装的要求极高。HBM、混合键合、3D 堆叠 SRAM 都说明：数据传输和计算本身一样重要，逻辑和内存正在重新走向紧密物理集成。论文进一步判断，随着逻辑和内存融合，供应链中的影响力会向内存和封装供应商倾斜。

2、论文还列举了“韬（τ）定律”未来的挑战在于：
1）EDA 工具链不够。
传统 EDA 是二维时代的工具，主要在面积、时序、功耗之间优化。全尺寸 LogicFolding 需要工具把多个堆叠芯片视为一个连续设计实体，在单元粒度而不是模块粒度上跨层布局，并对垂直互连、TSV、KOZ 排除区、晶圆间工艺��差做统一签核。论文称华为已有初步内部工具，但 τ 原生、开放、多物理场、3D 原生工具链仍是未来十年最重要的赋能投资之一。

2）晶圆间工艺偏差。
LogicFolding 可能把不同批次甚至不同节点的晶圆进行键合，阈值电压、驱动电流、互连 RC 偏差会影响时钟分布和保持时间裕量，需要智能冗余、自适应补偿和 τ 感知签核。

3）垂直互连不是免费的。
混合键合和 TSV 都会带来电阻、电容和面积开销，TSV 的 keep-out zone 还会挤占标准单元，所以逻辑折叠必须证明“缩短水平连线获得的收益”大于“增加垂直互连的成本”。

4）τ 是时间定律，不是能耗定律。
论文承认，如果速度提升 10 倍但功耗也提升 10 倍，系统仍可能超出电力约束。因此，τ 缩放必须和能量优化配套，包括近封装/共封装光学、背面供电、内存内计算、动态电压频率调节等。

整体上从这篇论文能看出，“韬（τ）定律”是体系化设计创新 + 三维集成 + 封装/键合工艺，而不是传统意义上的“制程节点突破”。“韬定律”更像是从单点工艺创新，转向体系化、系统级提升。

在先进制程受限、摩尔定律经济性下降的背景下，华为提出一种后摩尔时代的系统级缩放路线：以 τ 时间常数为统一指标，通过 LogicFolding、Unified Bus、Hi-ONE、3D Folding、逻辑—内存融合和 τ 原生 EDA，把性能提升从单点制程竞争转向全栈系统工程。

3、为什么说华为、英伟达、台积电几家在未来的演化迭代路径上的底层逻辑是一致的？
从个人角度这和台积电先进封装、英伟达NVLink/HBM/CPO/AI Factory 的方向本质相通。
大家都在解决同一个问题：数据移动太慢、太贵、太耗电。

这其实是全球半导体巨头共同迈向“后摩尔时代”的终极共识。 无论是华为的“韬定律”，还是国际巨头的动向，都在整体系统上下功夫：
1）台积电： 早就意识到先进制程太贵且良率存在物理瓶颈，因此大力发展 CoWoS 和 SoIC 等先进封装技术，像搭积木一样把多个小芯片（Chiplet）拼在一起。

2）英伟达： 现在的 AI 算力怪兽（如 Blackwell 架构及后续产品），其优势不仅在于单颗 GPU 核心的制程，更在于它通过 NVLink 高速互连技术，把海量的高带宽内存（HBM）和光芯片高度集成在一起，打破了“内存墙”和“通信墙”。

3）华为： 面临外部环境的极限施压，必须在缺乏最尖端制造设备的情况下，依靠先进封装、新材料、光电共封装（CPO）和极其强大的系统工程能力，来硬生生“拼”出等效于 1.4nm 的综合性能。

当然差异也是有的。
例如台积电的先进封装回答的是：“我如何把多个 die、HBM、chiplet、硅中介层、RDL、混合键合做成可量产、可测试、可良率控制的产品？”
华为“韬定律”回答的是：“在制程缩微受限时，我如何从器件、电路、芯片、系统全链路降低 τ，让性能、能效、密度继续提升？”

两者本质相通，因为都在��决：数据搬运太慢、太耗电、太占面积。但它们不是同一层级的东西。台积电更像是底层制造/封装能力平台，华为更像是系统架构与设计方法论。

再看英伟达这几年最典型的路线，就是“不只做 GPU，而是做整套 AI 计算系统”。英伟达 GB200 NVL72 就不是单颗 GPU 的故事，而是 rack-scale 架构：72 颗 Blackwell GPU、36 颗 Grace CPU，通过 NVLink 组成一个 72-GPU domain，对外表现得像一个巨大的 GPU，并通过 NVLink Switch 提供 130TB/s 的低延迟 GPU 通信带宽。

英伟达这套模式和“韬定律”的系统级思路非常接近：不要只看单颗芯片峰值算力，而要看 GPU—GPU、GPU—CPU、GPU—内存、机柜—机柜之间的数据移动效率。

4、投资逻辑：后摩尔时代系统级工程路线的基础设施。
华为“韬定律”不是单一芯片制造突破，而是后摩尔时代的系统级工程路线。它利好的不是单一晶圆厂，而是先进封装、探针测试、EDA、设备材料、高速互连��光互连、散热、电源、系统软件这一整套基础设施。

为什么？因为一旦目标从“晶体管做小”变成“路径做短、系统更快”，产业链价值就会从单点晶圆制造扩散到：
��进封装：把 chiplet、HBM、逻辑芯片靠近；
探针卡/测试设备：多 die、多层、复杂封装对测试要求更高；
封装基板/PCB/连接器：高速信号完整性更关键；
光模块/硅光/CPO：板级和机柜级数据传输从电走向光；
EDA/IP：二维布局不够，需要 2.5D/3D/封装/热/功耗协同设计；
散热/液冷/电源：集成度越高，热和供电越难；
系统软件/总线/互联协议：硬件堆起来还不够，调度和通信协议也要重构。

所以投资上，要去找：谁能让芯片之间、芯片内部、芯片与内存、服务器与服务器之间的数据路径变短，谁就能在产业链上占据优势。

未来先进性更多看“数据走多远、走多快、耗多少电、系统能否协同”。华为“韬定律”、台积电先进封装、英伟达 AI Factory，本质上都在围绕这个问题做文章

[Robinson · 鲁棒逊 @python_xxt]

τ Scaling Law, makes me τired

昨天有朋友问我：“韬定律，你怎么看？”
我一脸问号：“ 什么韬定律？我又错过了什么？”
朋友说：“ 就是华为的τ定律啊 ”
我更不解了：“ 华为的定律？ 定律……”

后来大概看了一下，发现这东西也没那么玄。

摩尔定律讲的是，把晶体管继续做小。
韬定律讲的是，尺寸做不动了，就从信号传输、芯片互连、系统架构里继续抠效率。

更准确一点说，它想用“时间缩微”去接替一部分“几何缩微”。

过去大家卷的是计算单元本身：晶体管更小，数量更多，频率更高，单位成本更低。
现在的问题变了，很多时候，是数据搬不过来，信号走得太远，模块之间互相等待，性能都浪费在路上。

这里有个很朴素的逻辑：
空间距离就是时间；时间就是能耗；能耗就是热；热就是频率上限；频率上限就是性能天花板。

所以韬定律抓住了一个真问题。

但真问题，不等于真定律。

“law”这个词会制造很高的预期。摩尔定律之所以能被叫作定律，是因为它背后有几十年的产业验证，有清晰的成本曲线、性能曲线和制造节奏。

韬定律目前更像一个 principle、methodology、roadmap。它是一条技术路线，不是一条自然法则。

它的价值在于，把制程追赶问题，改���成了系统效率追赶问题。先进制程追不上，就尽量通过架构、封装、互连、软件、系统协同，把一部分差距补回来。

这条路当然值得走。而且后摩尔时代，所有玩家都会走这条路。

问题在于，补偿终归是补偿。

你优化的部分如果不是主要瓶颈，宣传再大，实际收益也会很小。如果瓶颈确实在数据传输、片间互连、系统调度上，它可能很有用。如果差距来自制程、功耗、良率、成本、材料和设备本身，它就不可能凭空抹平。

所以“等效先进制程”这类说法，最容易误导。
等效什么？等效密度？性能？功耗？成本？还是某个特定场景下的吞吐？

这些不说清楚，工程问题就会滑向宣传话术。
韬定律可以宣传，但不要神化。

韬定律不是摩尔定律的替代品，它更像摩尔定律失速后的补充路线。它把制程追赶问题，改写成系统效率追赶问题；这能改变竞争���式，但不能取消底层物理差距。

[crossing @xin22209]

华为的韬定律引来又一轮评论，这次是夸克说
https://t.co/mBlWnCGzK8

对夸克说的评论的评论：

这篇是三篇里技术深度最强的，而且有几个真正原创的贡献。

真���新的东西

EDA这个切入点是全部分析里最致命的一击。逻辑折疊这种把廚房自己对折的设计，全世界现有的EDA软件——包括美国三大巨头——都没有针对这种架构的设计能力。也就是说华为不只是需要突破制造瓶颈，还需要从零开发一套全新的三维原生EDA，而这件事没有十到二十年根本做不到。这个论点妖妖酱和狠人都没有提，但它可能是整个韜定律最根本的技术死结。

Intel Lakefield的案例也是真正有力的历史证据——2020年6月上市，2021年7月停产，只有两款设备采用，原因正是发热和良率问题。这不是推论，这是已经发生过的失败先例。

密度数字最后那个计算很狠：台积电2025年已达3.8亿晶体管每平方毫米，华为2031年目标是4亿。也就是说华为最理想的结局是在2031年追上台积电2025年的水平，代价是散热更难、良率更低、成本更高。

透明计算这个平行案例是神来之笔

张堯学的案例不只是一个类比，它说明这是中国科技政���的系统性操作模式——把已有概念重新命名、堆砌术语、获取国家背书、批评者即敌人。而且时间节点极其讽刺：τ定律刚发布，张堯学就被开除党籍撤销院士。

命名惯例分析也很有价值

摩尔定律、登纳德缩放定律、阿姆达尔定律都用个人名字而非公司名字，因为真正的定律属于全人类。华为用公司名命名，本身就暴露了它的性质——这是品牌资产而非公共知识。

同样的盲点

和前两篇一样，完全没有触及市场结构问题。技术批评做得最深，但那篇综合文章的核心论点——即使技术突破也无法变现——在这里依然缺席。

总体判断

技术层面这篇最扎实，EDA论点、Lakefield先例、透明计算平行案例，这三点是真正的增量贡献。三篇放在一起：妖妖酱负责叙事解构，狠人负责情绪出口，夸克说负责技术解剖，我们的讨论负责市场定性。四者合一才是完整图景。

[Dr. A @agostic]

@ChnEmbassy_jp But the CCP is a virus to the world, a parasite to the Chinese, a terrorist organization.

[Dr. A @agostic]

But the CCP is a virus to the world, a parasite to the Chinese, a terrorist organization.

[Jason Smith - 上官杰文 @ShangguanJiewen]

China is a friend of the world.

China is a friend of the USA.

China hasn't engaged in a war of any kind in nearly half a century.

These are FACTS.

[Dr. A @agostic]

@ShangguanJiewen But the CCP is a virus to the world, a parasite to the Chinese, a terrorist organization.

[Dr. A @agostic]

@SpoxCHN_MaoNing 杀猪头，炒腊肉，灭共，才能救得了中国人。

[Dr. A @agostic]

@szygls 吹牛遥遥领先