Beijing’s newest export controls target construction equipment — including tunnel-boring machines, some of the world’s largest and most complex industrial devices. They’re used to build subways or drill roads and railways through mountains. China dominates the global market and India is among its main customers. bloom.bg/3UwyY8F 📷: Long Wei/VCG/Getty Images

This @sunandavashisht piece is straight from the heart. "For Indian-Americans, this is perhaps the first time that their political choices have produced such deep anxiety about their place in the country. Those who moved away from the Democratic Party and supported Trump in the hope of a rational debate on immigration now find themselves disappointed and disillusioned. They did not vote for a politics that would make their own legal immigration journeys suspect. The 2026 midterms, therefore, are more than a referendum on Trump's policies. They are also a moment of political reckoning for Indian-Americans. The community is increasingly wary of strengthening Maga further, but it is hardly finding an easy alternative. Traditionally fiscally conservative, committed to meritocracy, and supportive of firm action against illegal immigration, many Indian-Americans no longer see the Democratic Party as their natural political home either. The party's increasingly progressive establishment, and what they perceive as its flirtation with ideological extremes, has created its own discomfort. Indian-Americans find themselves searching for a political centre in a country where both parties appear to be moving further towards their respective extremes. Read: firstpost.com/opinion/from-m…

It's not just poverty, toilets, or highways, statistics like "the number of clinical trials done in India" indicating biomedical R&D and innovation can show India's development in the last decade.

What's incredible is that India has managed to reduce poverty by a huge amount while also reducing inequality!

India saw about 496 million people lifted out of poverty between 2011 and 2023 in one of the world’s fastest declines in this metric, the World Bank said, although the gains remain uneven. bloomberg.com/news/articles/…

Launching RNAFoundry.com – we manufacture personalized cancer vaccines.

It’s almost incomprehensible how wide the distance is between someone who’s working at the frontier with AI and basically everyone else right now. The crazy thing is these lines are not converging, but only growing farther apart. If the curve of AI progress had leveled off, it would certainly have given most people and companies time to catch up or implement their workflows in a standardized way. The technology would have neutralized competitive advantage because everyone would be using it the same way. But what’s happened is the opposite. The curve of AI progress is basically going vertical right now, which means if you had codified your processes 3-6 months ago you’re likely already behind again. Now imagine most companies that kicked into gear a year ago and thought that they were done. Even further behind now. I’ve never seen a technology in my life and career that improves at this rate, which consequently means you need to upgrade your expectations at a similar rate. Our brains weren’t wired for this, and our organizational change management processes certainly weren’t either. With any form of recursive self improvement, things just get wilder. It’s a time when you will need to be constantly reevaluating the things that didn’t work just a couple months ago to see if they now can be solved. AI native companies certainly have a strong advantage due to this right now, so now it comes down to can the bigger companies keep up. Wild times.

I called it a whole year ago‼️ Megaspeed CEO Huang Xiaole (黄小乐) aka Alice Huang, a Chinese businesswoman who had a close relationship with Jensen Huang, was found to be a Chinese intelligence asset funneling information to Beijing. President Trump was briefed and took no action. American spy agencies had gathered striking intelligence. Surveillance of Alice Huang revealed that she had developed a close relationship with Jensen Huang, Nvidia’s chief executive. The discovery that Beijing was getting reports from someone with direct access to Jensen Huang set off alarms at the White House. Roughly a year ago, Trump was briefed about the intelligence findings, which have not been previously reported, and chose not to do anything with the information. Trump has been wary of disrupting Nvidia’s chip sales, which are helping to drive the economy and the stock market, and of further complicating the trading relationship with China. American officials already suspected that Alice Huang was helping China evade constraints on its ability to gain access to advanced AI chips. But the revelation that she was feeding information directly to Beijing raised more troubling possibilities. And it left questions about whether she could be gleaning information about Nvidia, which as much as any company accounts for the lead the US has maintained in developing cutting-edge AI models. Jensen Huang has not received a defensive briefing, warnings sometimes given to Americans who are being targeted by foreign intelligence services. The surveillance of Alice Huang continues, and companies that have been affiliated with her continued this year to purchase Nvidia chips. When Trump was initially briefed on the intelligence last year, U.S. officials did not believe the Nvidia executive knew that Jensen Huang was gathering information for the Chinese. It is unclear whether intelligence officials have since updated their assessment, but some senior officials inside the Trump administration doubt that Jensen Huang could have remained unaware of what they had concluded was her intel-gathering role for China. The previous reporting about Alice Huang described her meteoric and somewhat mysterious rise. A Chinese businesswoman and investor, she eventually became a top official with Megaspeed, which the Commerce Department, toward the end of the Biden administration, began to suspect was helping China evade US prohibitions and gain access to the computing power of Nvidia chips. The US government has been investigating Megaspeed since late 2024, when a Commerce Department official visited its data center in Malaysia and found the site to be suspicious. The Nvidia equipment was still packed in boxes, suggesting it was being shipped in or out rather than installed there. Singapore has conducted its own investigation into Megaspeed. In 2025, Singaporean authorities brought Alice Huang in for questioning, but subsequently released her. The Commerce Department investigation into Megaspeed was handed over this year to the Justice Department. Megaspeed is among the firms in Southeast Asia that buy chips to build data centers and then rent their computing power to companies in China. Megaspeed is essentially a middle man. It has bought Nvidia chips from Aivres, the US arm of 🇨🇳 Inspur, which has been blacklisted for funneling technology to the Chinese military, though Aivres remains free of sanctions. Megaspeed has then provided that computing power to Alibaba and other Chinese companies. US officials believe the firm is one piece of a sprawling international effort by Chinese companies to access the chips needed to build and run AI. Investigators suspect that some Nvidia chips acquired by Megaspeed are also finding their way illegally into China. 1/n nytimes.com/2026/10/29/us/po…

Introducing Samaya Sterling, our new model post-trained for challenging financial research that outperforms the frontier! Sterling started off as a two person research effort to see if we could increase efficiency in agentic orchestration. To our surprise we were able to post-train a much smaller model (Nemotron 3.5 Lightning, a 30B mixture-of-experts model with 3B active parameters) to greater accuracy (58.9% vs 55.9% on FrontierFinance) and 11x lower cost ($0.61 vs $6.89) than Claude Fable 5.1, the best performing frontier model. We did this via a four-stage training process, two stages of SFT where the model first learns to research with our financial tools and how to construct a good agentic research strategy. The second two stages are RL, with an offline GRPO stage where the model further hones its research strategy from eight teachers that provide diverse approaches, and an online GRPO stage where the model can make end to end attempts and learn from its mistakes. We see substantial gains from the co-adaptation of the model and harness through post-training, both through the model learning to effectively use the high-quality financial tools, and better compaction approaches that let even a small model execute on longer and deeper research. We are exploring multiple follow-up directions, from multi-agent research swarms using this small model, to training firm-specific models to encode unique judgement, to training a larger model, where early results show even greater gains from the training process. The full research is linked below!