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13F 更新 Bitcoin 二季度数据 大型机构并未撤离 根据第二季度的 13F 披露的所有和 Bitcoin 相关的数据，把 Call、Put、BITO 这种期权和期货敞口全部剔掉，只看机构实际持有的 bitcoin:native 现货 ETF 普通份额得出的数据。 Harvard Management 在一季度持有 3,044,612 股 $IBIT ，到了二季度还是 3,044,612 股，一股没动。因为二季度 IBIT 价格下跌，这笔仓位在 13F 里的市值从大约 1.17 亿美元下降到 1.01 亿美元，下降超过 13%，但持仓数量完全没有变化。 阿布扎比主权财富基金 Mubadala 的动作一样。一季度持有 14,721,917 股 IBIT，二季度仍然是 14,721,917 股。仓位市值从大约 5.66 亿美元下降到 4.90 亿美元，同样主要来自 IBIT 本身价格下跌，而不是卖出。 摩根大通反而在增加仓位。第一季度的 13F 中，JPMorgan 持有 8,302,691 股 IBIT，第二季度增加到了 10,407,635 股，一个季度增加大约 210 万股，增幅 25.35%。 另外还有阿布扎比投资委员会 ADIC，一季度持有 8,218,712 股 IBIT，二季度同样没有变化。算上 Mubadala，两家阿布扎比主权资金合计持有大约 2,294 万股 IBIT，截至 6 月 30 日价值大约 7.64 亿美元。 Paul Tudor Jones 旗下的 Tudor Investment 也在增加现货仓位。第一季度持有 579,083 股 IBIT，第二季度增加到 688,529 股，增加 109,446 股，增幅约 18.9%。 当然并不是所有机构都在加仓，13F 里当然也存在减仓和调仓，而且二季度 Bitcoin 的下跌确实让很多机构的账面价值缩水，但至少从已经披露出来的主要长期资金、主权资金和传统金融机构来看，更多看到的是继续持有和增加现货 ETF 仓位，并没有出现大规模撤离。 至少没有看到大型资金在 Bitcoin 下跌期间出现一致性的撤离。 @Gate Crypto、美股、港股、韩股、黄金、CFD、预测市场一站交易

Same watch. Two completely different personalities. The Black Bay 54 in blue can be configured with either the bracelet or rubber strap—which one would you choose? #BlackBay54# #Tudor# #LuxuryWatches# #SwissWatches#

Sometimes one small change transforms an entire watch. The new five-link bracelet gives the Black Bay an entirely different personality—and I think it works beautifully. #BlackBay# #Tudor# #LuxuryWatches# #WatchCollector#

Every person living in a western nation needs to listen to every word of this Katherine Berbalsingh went to the University of Oxford and is Headmaster at Michaela Community School in London, UK She PERFECTLY explains the mass indoctrination into the narrative of oppressor and oppressed, and of hating White People I will write only some of this out because it’s very important, however you should listen to it so you can hear the passion: “The culture shift comes from what children learn at school and online. Ask any young person what history they learned at school, and they’ll tell you, Hitler. Ask them what else? Slavery. Ask them what else? American civil rights. In fact, what little they know of history will be all about Black and brown people fighting for equality against the white man, women fighting men for the vote, gay and trans people fighting for various rights. Our young people have been taught that history is simply one long story about various groups struggling under the oppressive dead white man. — History is taught through an oppressor lens. The triangular slave trade, white men held the power. What about Britain ending the slave trade? More than a quick mention, if at all? Mm, no. What of the Arab slave trade that lasted 3 times as long as the triangular slave trade? Mm, no. Okay, so GCSE history in Britain is often taught as migration through time, so the idea that Britain has always been a land of immigrants is embedded in our children’s heads. Most schools would prefer to concentrate learning about the tiny number of Black people who existed in Tudor England over a thorough analysis of England’s break from Rome. — Not to mention weeks on King Mansa Musa of Mali because he was a Black Muslim. His bearing on British institutions, laws, and faith is nonexistent. And the fact that he is said to have been the richest man in history, thanks in part to his massive slave-owning society, is a detail somehow that teachers rarely ever teach. But it isn’t just our schools. It’s our general culture too. Take your kids to a museum or an art gallery in any Western country, and you’ll find the same narrative. As an example, when learning about aviation in London’s Science Museum and the extraordinary feat that is man making massive machines move in the sky, a write-up on the wall explains that women and Black people were historically barred from aviation schools and the military. Similarly, James Watt, the man who invented the steam engine and is considered the founder of the Industrial Revolution, has a write-up on the wall explaining that his early career involved slave trafficking, with a bonus analysis of the whole of Britain’s complicity in the slave trade. They flatten the entire human story and all of its complexities into the narrative of oppressor and oppressed, leaving young people unable to see the world in any other terms.” We have to end the mass indoctrination

全球数字资产市场在经历长达数周的剧烈波动后，在 60,000 美元关键技术支撑位展现出非标的买盘承接迹象。 尽管此前由于 MicroStrategy（MSTR）及相关实体面临潜在清算抛压的传闻，导致市场悲观情绪在 6 月上旬集中宣泄，但比特币（Bitcoin）作为去中心化、主权无法没收且具备跨境即时结算属性的硬货币，其底层分发逻辑在宏观资金重组中再次获得确信度验证。 在此前的 2024 至 2025 周期中，由于黄金创下 2011 年以来的首次实质性暴涨，传统数字黄金叙事曾遭遇阶段性的资本撤离摩擦。 然而，随着全球大模型推理成本下行与 AI 股票资产沉淀出庞大的实现利润（Realized gains），这批高净值科技资本正在寻求长期的价值存储入口（Store of Value）以对冲法币的持续折旧。 传奇宏观投资人保罗·都铎·琼斯（Paul Tudor Jones）等机构资本在当前负面舆论频发的情形下，依然维持了对数字资产的企业财库防御性配置，这表明科技与传统老钱的利润最终在向稀缺资产复归。 这种对核心支撑位的死守，直接对冲了此前市场对塞勒（Saylor）爆仓危机所预设的极值清算模型。资产价格走势表明，MSTR 与相关杠杆凭证当前的贴水表现，已阶段性消化了未来 6 个月内最恶劣的潜在强制抛售摩擦。 与此同时，地缘冲突导致的供应链溢价正在迎来转折。随着霍尔木兹海峡的重新开放，全球大宗商品运输阻力与航运通胀随之下行，这为美联储从非理性鹰派转向行政降息腾出了关键政策空间，全球万亿法币流动性重组的闸门随时可能打开。 华尔街资管巨头与链上原生多头在第三季度（Q3）起跑线前的密集换手，解释了为什么这场左侧左倾的防御性套利在当前节点获得胜率支撑。 当市场的风险溢价模型被恐慌情绪推向历史极值，价格回归长期历史趋势线的技术收敛往往最具爆发力。 资本的逐利属性决定了，当高带宽的算力泡沫将软件生产力推向边际零成本，具备绝对稀缺性与零物理折旧损耗的数字锚定物，将不可避免地再次接管泛化资本大盘的避险分发权。

North Korean leader Kim Jong-un has frequently appeared in public with his teenage daughter, Kim Ju Ae, involving her in ceremonial parades and missile launches. Daniel Tudor explains why experts believe she is being lined up for leadership.

Mijns inziens is dit een van de beste use cases voor AI-videocontent op dit moment. Een AI-influencer reist terug in de tijd naar 1536 om te vloggen over haar ervaringen in Tudor-Londen.

Stan Druckenmiller made 1 trade - earned $1B destroying the UK economy, Paul Tudor made 1 trade - earned $100M predicting Black Monday on one JPMorgan stage they showed for the first time exactly how they made $1.1B in 24 hours 35-min from the two greatest traders alive - how they think, how they size, how they win bookmark & watch - no course, no book, no podcast comes close to this

Higgsfield is the most untold story in tech. $1BN in ARR in 18 months. Faster than everyone other than OpenAI and Anthropic. They spend $4M a month on models. They expect this to be $100K per person per month. They have 150 people working in a content machine. They will breed more millionaires than any other company in Kazakh history. For the first time, @alexmashrabov on the journey to $1BN in ARR. (below) 1. The Power of the Immigrant Founder Coming from Uzbekistan, Alex was pushed into competitive programming at age eight as his single path to reach the United States. For international founders, placing top in global competitions serves as the ultimate social elevator, instilling the relentless work ethic required to build breakout companies. 2. My Biggest Lessons in the Journey to Finding Product-Market Fit @higgsfield burned over $10 million of its $16 million seed round chasing hype and narrative rather than product quality. With under $5 million left, the team pivoted to product-led growth, solving camera control for creative directors, which immediately triggered organic hypergrowth without paid ads. 3. The 150-Person Content Team Powering Higgsfield's Billion in ARR Nearly half of Higgsfield's workforce consists of 150 in-house creative professionals producing tutorials, ads, and cinematic projects. Generating 90 minutes of TV-quality AI video requires 100 hours of raw output, proving human taste and curation remain the primary drivers of distribution. 4. We Spend $4 Million per Month on Models Higgsfield spends $4 million monthly on internal model usage, averaging $10,000 per employee so teams can freely vibe code and test workflows. Uncapped inference compute acts as a force multiplier, allowing top talent to discover breakthroughs at maximum velocity. 5. Why Chasing Benchmarks Is Bullshit and the Corporate Misalignment Occurring Public benchmarks have devolved into corporate psyops where lab researchers overfit test data to secure bonuses before job-hopping. Text-to-video benchmarks ignore real production workflows requiring 3,000-word prompts, proving direct customer iteration beats artificial leaderboards. 6. Why Team Sizes Won't Be Impacted as Much as People Think While AI handles over 60% of basic support requests, complex B2B environments cannot eliminate human teams. High product velocity constantly shifts rules and context, requiring smart, coordinated operators across legal and customer success. 7. Americans Are Way More Promiscuous When It Comes to Leaving Companies Silicon Valley workers routinely jump jobs every two years, prioritizing short-term trends over deep commitment. This transactional market gives international hubs an advantage, where cultural loyalty and team stability build compounding technical moats. (links in comments)

Researchers proved AI has deleted every reason universities exist. Harvard University ran a controlled experiment pitting a custom AI against their own top-tier classrooms. And the results are going to collapse the higher education bubble. They took 194 undergraduates and split them up. One group learned physics in one of Harvard’s best hands-on, active-learning physical classrooms. Group work. Instructor support. The premium university experience. The other group went home and learned the exact same material with an AI tutor. The AI didn't just win. It embarrassed the institution. Students using the AI learned more than twice as much as the students in the elite Harvard classroom. They scored 30% higher on the final assessment. And they did it in less time. Let that sink in. A piece of software sitting on a laptop outperformed a world-class faculty in one of the most elite learning environments on Earth. Universities have always justified their exorbitant tuition with two things: access to elite knowledge and the physical classroom experience. This study just proved both of those moats are gone. When software can teach you complex physics twice as well as a $60,000-a-year institution, the math of higher education breaks permanently. The AI didn't just give the students answers. It used strict pedagogical guardrails. It guided. It questioned. It forced the students to do the cognitive work. It offered perfect, one-to-one tutoring, personalized to the exact moment a student misunderstood a concept. That level of attention is mathematically impossible to scale in a physical lecture hall. For a thousand years, the university was the only place to get a premium education. Now, it’s the bottleneck. If AI can double your learning speed for a fraction of the cost, what exactly are students taking on decades of debt to pay for?