It’s heartbreaking for those of us living inside Iran to see that people in the rest of the world don’t see the injustice being inflicted on the Iranian people. Human rights have become selective. Some of the most serious human rights violations have been happening inside Iran for years, yet the world has closed its eyes.

1983年，女子因在家组织舞会、与多名男性交往而犯“流氓罪”判处死刑：26岁的胡贤华，她原本是江苏徐州云龙区医院的勤杂工。因性格外向、社交活跃，她在家组织舞会、与多名男性交往，被邻居举报。在当年“从重从快”的严打政策下，她的行为被认定为“流氓犯罪活动”，一审判处死刑，剥夺政治权利终身。画面里，她听到判决后神情呆滞，反复哀求“判我死缓也行，我能改”，但最终还是被押赴刑场执行枪决，年仅26岁。

The contagion from housing collapse to the rest of financials starting to spillover.

China’s “Sinicization” of Tibetan Buddhism is the "second Cultural Revolution".

Streamer has gone viral for making her boobs into a workable controller

#ChinaMustAnswer | “You spent the last month in Islamabad…”: Arnab catches Victor Gao, Vice President, Centre For China & Globalisation, off-guard as the debate heats up. Watch #DebateWithArnab now, on-air, and online. Tune in and fire in your comments: piped.video/live/fBVFFM7YfxM… #NepalFloods #BharatWithNepal #NepalDisaster #ChinaNepalBorder #ChinaMadeDisaster #HimalayanDisaster #ChinaWarning #TibetFloods #BarrierLake #GlacierCollapse #NepalFloodUpdate #NepalChinaNews #ArnabGoswami #RepublicWorld

Population is a separate metric from deaths, are you really that retarded?

Sorry you struggle with reality. The good news is that China abandoned communism and adopted capitalism, and far fewer people have been killed by the state since.

Last week I posted a new paper with Patrick Norrick: “Terra Incognita: The Economics of a Shrinking World.” We chose the title deliberately. No society in history has experienced the fertility levels now seen in South Korea, China, Thailand, Colombia, Chile, and many other countries. Our knowledge of the causes (and of the economic consequences) remains far more limited than most public discussion acknowledges. Much of what we write is, at best, educated conjecture. The paper also struggles against a space limit. We wrote 20,000 words, far from the 250,000 or so we would need to address some issues in more detail (if I had the time and resources to hide away for a year, I could do that, but not now). That means some ideas are only sketched. Nonetheless, we emphasize several important points. First, fertility has fallen very fast everywhere: rich and poor countries, east and west, north and south, conservative and liberal societies, religious and non-religious societies (with the exception of the Jewish population of Israel; fertility has also collapsed among the Muslim population within the pre-1967 borders), you name it. Even in sub-Saharan Africa we see fast and unprecedented drops in fertility (alas, from a high initial level). Second, and this is really interesting, the fertility collapse has been concentrated in poor and lower-middle-income countries much more than in rich countries. By now, income per capita and fertility are positively correlated within OECD countries. We conjecture this will hold globally in a few decades. Third, and related to the second point, the fertility collapse has been concentrated among poor and lower-middle-income women within countries. In countries such as the U.S., the rich and highly educated now have more children than the poor and less educated. Fourth, we document why we do not understand the data from the U.N. World Population Prospects. See, for example, Tables A.1 and A.2. Fifth, we explain why some proposed mechanisms struggle when confronted with the data. A more subtle point is at work here. Many commentators do not seem to understand the difference between proximate causes and ultimate causes. Yes, births might have gone down because fewer women are in long-run relationships. That is the proximate cause. But you need to explain why fewer women are in long-run relationships (the ultimate cause), and saying that they spend more years in school, to take one example, does not get us very far. Why do they spend more years in school? Once you start down the whole chain of reasoning, things become much harder than they seem. The paper can be found here: sas.upenn.edu/~jesusfv/Demog… Comments always welcome!

The 2026 Shanghai Ranking of universities has been released. The Anglo-American giants still dominate the Top 15, and Paris-Saclay retains its 13th-place in the ranking. The top Chinese university, Tsinghua (in Beijing), ranks 18th. China is making progress, with 16 universities (18 if Hong Kong is included) in the Top 100, up from 13 (15) in 2025. Future editions are expected to show further significant progress by Chinese universities, thanks to the growing number of Chinese researchers returning home—choosing to leave the United States for China, where they do not face suspicion and discrimination based on their ethnicity.

一起来领教一下印度人的诡辩： 中国很成功，但中国的模式没有用，别人都学不来，人们都说印度和我们一样那么乱，印度模式合适我们 ……………… 其他专家像看傻子一样看着这位印度砖家😑

Blackhat has uploaded the full presentation on the OpenAI Hugging Face incident, about which much ink has been spilled. piped.video/87DyyMV0kCY?si=wwYt…

This speech on the state of the Chinese economy by Li Daokui is a pretty accurate representation of how the actual economics profession *in China* views the current situation. In short, it’s not optimistic. In fact there’s very little resemblance between their views and the portrayal of the Chinese economy commonly seen in Western policy circles or on Western social media.

The DeepSeek Leak: What Liang Wenfeng Really Thinks About China’s AI Race There’s a leaked transcript of Liang of DeepSeek’s May investor meeting. The meeting lasted almost four hours and contains a huge amount of information. He reportedly cancelled the fundraising and may delay the IPO because of the leak. It’s also rumoured Beijing is unhappy because of the sensitivity of what was disclosed. Here’s the full transcript: aiproem.substack.com/p/must-… Here are some points I found particularly interesting. 1. Model differences between China and the U.S. Liang repeatedly argues that the only fundamental gap is compute resources, not talent or algorithms. • Scale / parameter size: Leading U.S. models have around 800B activated parameters. Chinese models, including DeepSeek’s, remain at the tens-of-billions scale. Training an 800B-class model would require roughly 50,000 NVIDIA GB300 cards or 200,000 Huawei 950 cards. DeepSeek’s current compute only supports serious work at the tens-of-billions scale, with plans to reach 150B–250B as more hardware arrives. • Capability gap: Liang says differences in capability, talent development and downstream applications all stem from the compute gap. China is around 12–24 months behind but achieves its results using only about one-twentieth of U.S. compute. The goal is to reduce that lag to 3–6 months while remaining competitive in areas such as cost and user experience. • Other characteristics: DeepSeek believes Chinese models will become the world leader in low-cost, high-quality AI services, much as Chinese manufacturers dominate many industries today. Liang sees no permanent structural advantage for either country once compute becomes more equal. 2. Technology differences Again, Liang sees compute as the key differentiator. • The main bottleneck is GPUs and the capital required to buy them, not algorithms or engineers. • The U.S. benefits from an earlier start, vastly larger GPU clusters and much greater capital investment. • China’s advantages are lower costs, greater efficiency, better product optimisation and a willingness to maximise smaller models. • Both countries follow the same roadmap: LLMs → Chain-of-Thought → AI agents → continuous learning → self-improving systems → embodied intelligence. • Huawei 950 supernodes can substitute for NVIDIA GB200/GB300 systems, though at roughly four Huawei cards per NVIDIA card. Liang believes the software ecosystem can catch up within a year, while manufacturing capacity will take much longer. 3. How DeepSeek sources chips This was one of the most sensitive parts of the discussion. • Current holdings total around 20,000 H100-equivalent cards, most delivered in the previous one to two months. The majority are NVIDIA. • NVIDIA strategy: “Buy as many as possible at a reasonable price.” After the recent financing, the plan is to convert cash into GPUs as quickly as possible because supply—not money—is the real constraint. Overall posture: aggressively acquire every available NVIDIA card (including non-compliant ones where possible - grey market smuggles) while simultaneously investing in Huawei cards to accelerate domestic substitution. Self-built clusters only; no plan to vertically integrate into chip manufacturing.

🇸🇬 Incredible anti-communist speech by Lee Kuan Yew who was the first prime minister of Singapore. He turned Singapore from a failed third world country into the richest state in Asia. His polices proved that socialism fails and capitalism works.

Data centers barely use any water, barely use any land, and they lower electric bills.

You said they never did that. I promptly gave a dozen examples where they did. You got frustrated and started name calling, as you knew you were defeated. Now you’ve tried to shift the goalpost to avoid your embarrassment, but it’s only making you look worse. Just be honest.