合法技术移民的路子正在被堵上。 留学生的OPT在被废除中。 切香肠一段段来。 工作的H1B到绿卡的路径，也在慢慢被切断。 即便是绿卡和公民们也不要太高兴。 传媒学中有个叫标签理论，Stereotyping /labeling，指媒介或社会群体用高度简化、概括化甚至符号化的方式，把某些特征强加给特定的人群、事件或现象，从而用“群体属性”代替“个体特性”。 现在，印度人的标签是H1b scammer， 中国人的标签是CCP spy。 很多sb们在拍手叫好，但是实际上就是以后别人一看到你这张脸，第一反应就是spy。 现在很多安全相关的岗位，在慢慢把华裔背景的清理出去。 然后是各种关键岗位，也会排斥华裔背景。 合法移民，技术移民给美国人讲他们的贡献，比如昨天有个amazon aws的 founder员工说当初他们90%都是h1b的员工在非常差的环境下把aws做出来了。 而下面白人们的回复是，有你们，没你们一样能够做出来，美国登月的时候，也没有H1B，现在H1B们现在应该打包回去了。 sb们的共识也是共识啊。 所以，集中营是能选上下铺吗？

其实，对于真正搞投资的人来说，最重要的不是你的工具有多少功能；而是你的核心策略是否能够转化为组合，也就是Portfolio；所以，如何从策略模型到资产配置，到行业配置，到选股选券，再到数据回测，组合业绩跟踪，交易下单构成闭环，才有生命力 提供数据服务，运营，托管，估值，交易的平台非常多；但是真正拥有策略的很少，具备稀缺性；所以，你这样的平台要走出来，必须往投资的前端再走一走

💥【AI&观察：英伟达支持的澳大利亚AI数据中心及浸没式液冷算力公司 Firmus IPO失败】 英伟达支持的澳大利亚AI数据中心及浸没式液冷算力公司 Firmus撤回在澳大利亚证券交易所（ASX）的上市申请，是全球AI资本市场极具标志性的一次“滑铁卢”，本周在向机构开放IPO的募资申请后，询价及募资情况不容乐观，买方机构们对估值表达了不满；最后公司不得不撤回IPO上市申请，转而向私募市场融资 Firmus 成立于2019年，起初主要从事比特币挖矿业务，后转型为以浸没式液冷为核心的AI高密度数据中心与AI云服务商, 它获得了英伟达（战略伙伴与早期支持者，提供 DGX Cloud 和高端芯片支持）、黑石、Coatue Management、Jane Street 等全球顶级资本的背书 这次Firmus在ASX IPO的估值为440亿澳元，融资规模70亿澳元；而该估值在IPO前的几个月翻了3倍；引发了这次市场询价过程中买方机构的不满 当然，Firmus 的折戟不仅仅是一次个案挫败，它精准刺破了当下 AI 算力基建领域的多重结构性矛盾： 市场从“狂热买铲”转向苛求“真实的自由现金流与 ROI”：此前，市场认为“只要买到英伟达 GPU、建起数据中心就能躺着印钱”。但现在二级市场投资者已从情绪亢奋期进入理性核算期：巨额 CapEx 何时才能产生匹配的现金流？ 市场不再愿意单凭“远期算力规划 PPT”买单。 英伟达生态中存在一种模式：英伟达入股或扶持新兴云厂商/算力中心 算力中心大量采购英伟达芯片 推高英伟达营收并推高算力公司估值 这种“左手倒右手”的生态闭环在私募轮和行业上行期运作顺畅，但公开市场（二级市场）的公共资本要求清晰透明的客户真实需求与盈利能力，不再愿为这一同盟的溢价买单

喜欢喝酒的，欢迎到Woodinville，是Columbia Valley的酒庄核心区， 基本上可以对标Napa Valley吧。 这边有几百个大大小小的酒庄。 每到夏天，有很多音乐会。 有很多品酒的点。 各种口味的酒都有，红酒，威士忌，朗姆酒等等都有。 @WineWoodinville you should pay for this promotion. lol 🤣

BREAKING: OpenAI’s solution to Navier–Stokes does not match its Lean verification. The most important article to read today is not one of OpenAI’s 700 AI-generated math papers. It is this other paper, making a deep and worrying point: A Lean-verified proof does not automatically validate the proof written in natural language, nor does it mean that the formal statement captures the intended theorem. During translation, an AI can change an assumption, weaken a statement, or replace the argument entirely. It can hallucinate another theorem. Lean correctly verifies the result. But the proved result may no longer be what the paper claims. This is a general problem. Things get spicy when the authors examine OpenAI’s proposed Navier–Stokes solution. They identify at least two mismatches between the written intermediate results and their Lean counterparts: One estimate claims that four additional input derivatives suffice. The Lean version requires five: a weaker result. A pressure-flux estimate is obtained through a different bound, and proved through a different argument. It is not clear whether these mismatches invalidate the entire proof. But they raise an important issue. OpenAI is flooding us with claimed revolutionary breakthroughs. Yet nobody knows whether the proofs are correct or whether they prove what they claim to be proving. Epistemia at scale. * Paper in the first reply

I don't recommend anyone scramble to move their funds to new wallets today. But we should take the risks to cryptography from AI-accelerated math seriously, and minimize our exposure to not just quantum-vulnerable cryptography, but also potentially AI-vulnerable cryptography. The core new area of risk from this viewpoint is, unfortunately, ML-DSA / FHE / lattices. (and it's also another reason, along with quantum, why ECDSA might fall even faster than expected, hence the "fresh address" recommendation) So far most people have been in the mode of thinking "elliptic curves broken, hashes safe, lattices safe". But there is a good chance that the concrete security of lattices will take serious hits from the next two years of AI math. The basic threat model is: factoring is something that naively takes 2^(n/2) time, but over decades smart people have found and optimized number field sieves, and degraded that to 2^O(n^(1/3)), which is why RSA keys and signatures need to be ~400 bytes (and not 64 bytes). What if there are skeletons in the closet like that, both for elliptic curves and lattices, that we are simply not smart enough to discover - but bots soon will be? This is a major part of the reason why for the past year ethereum's lean roadmap has been going in the "hash-only" direction: no lattices, no ML-DSA, no Falcon, no lattice-based commitments inside ZK proofs, etc. Signatures in lean ethereum are all hash-based, either WOTS or SPHINCS-. For signatures and proofs, we already know how to go hash-only. The bigger challenge is for *public-key encryption* - and this goes far beyond blockchains. Secure communication, anonymizing protocols, lots of things need public-key encryption. And unfortunately there are long-standing mathematical theorems showing why public-key encryption cannot be done with hashes alone. You have to have some kind of trapdoor object that has at least one form of usable "structure" - either group theory (incl. isogenies) or lattices or code-based or potentially in the future even more newfangled and spooky things (local mixing?). But for anything that has structure, you should assume that AI will make at least some progress in breaking that structure. Here, one reasonable inference is that if you want to make something plausibly long-term secure, multiply the key sizes by 10. To me that's a very plausible world and something not at all extreme to predict. If AI will bring us 50 years of math in 2 years, then that 50 years of math may very plausibly include a "naive factoring -> GNFS" level of improvement to our ability to break lattices. In that world, lattices will still exist, but they will have to be significantly bigger to guarantee the same level of safety. And at those new larger sizes, hash-based constructions will beat lattice-based constructions on concrete efficiency in every use case where hash-based constructions are possible at all. Theoretically, of course it's possible that hashes are broken too (eg. P = NP would imply that). But I think P = NP is very unlikely. And intuitively, it's much more likely that a mathematical object has exactly no exploitable structure (like hashes are intended to), than that a mathematical object has exactly ~3 forms of exploitable structure (for elliptic curves: associativity, Schoof, pairings) and not some secret fourth form of structure we have not yet discovered that greatly degrades its security (for elliptic curves, ECDLP and pairing security). Similar for LWE, SVP, RLWE and the zoo of lattice problems. For this reason, we do not yet see any reason to worry and start padding the byte size of hashes (if we start to worry more, we would pad the round count first before doing anything to the byte size). Concrete TLDR, my own personal views: * Hash-based > lattice-based, in those situations where hash-based is possible at all * For anything lattice-based, be much more paranoid on param sizes. Remember that blockchains are only a small portion of the cryptography story; this point goes far beyond blockchains and applies to eg. access to websites, secure messaging, Tor / VPNs ... * For privacy protocols, strongly favor NOT putting encrypted notes onchain. Instead, send them offchain through some third-party mechanism. * If it's not difficult for you, keeping your funds in addresses which have not yet been used to make a transaction is a good idea. If it's easy for you, do it. **But be careful about migrations; I personally have lost more money in botched migrations than I have lost in all hacks combined**. * For multisig wallets, doing confirmations offchain is better than onchain, because this way the signatures of signer wallets do not get exposed to the public, so if ECDSA falls to AI much faster than expected, at least the multisig "gracefully degrades" to a 1-of-1 where the 1 is whoever was gathering the signatures - a much better place to be than "anyone can take the money"

作为知行合一的人，我持有 $ONON 的股票，占组合的比例大约5%左右；核心的原因是，我自己最近这几年都穿它的鞋，穿旧了换新还是买它 并不是因为有了股票持仓才买它的鞋，而是，因为自己的这个行为已经持续了5年，并且我开始慢慢向周围的朋友推荐；是因为它的鞋底真的特别好，耐磨，而且舒服；它一双鞋的穿着时间要远长于别的鞋；因为自己的行为和我观察到的其他佐证，最近加上它估值一杀再杀，个人认为它有比较高的安全边际 此外，今年夏天，我买了一双耐克的运动鞋，价格也不菲，还是新款，但是鞋的后跟磨脚，磨出了血；穿了2次，最后我把它留在了酒店 还有，今年我去上海，特意去昂跑在新天地的专卖店看，非常火爆；而且，这个鞋基本上目前还只能在中国核心大都市的核心地段开了店；在亚太和新兴市场的渗透率很低 虽然，从定价来看，一双1000多人民币的鞋是贵的，不过根据我对中国消费市场的了解；这个鞋还有很大的市场增长空间 接下来，我会在Substack写一篇深度的公司分析文章，告诉大家它的前世今生，与当前的战略布局与发展趋势