其实个人更喜欢这种风格,定价简单直接,个股看看营收看看筹码,大盘看看拥挤度看看ETF资金流,美股因为大规模的衍生品市场以及复杂参与主体就很难这么纯粹,很难从庞杂的因子判断方向(或者说很累)
大A的特性就是,要么疯,要么死,
政策也几乎总是矫枉过正,
上半年,给疯牛降温,国家队撤退、约束杠杆。
7月,监管逐渐警惕大A科技股的极端泡沫,继国家队撤退、约束杠杆之后,又通过主管媒体明确放话。
10月,出台新质生产力指导文件提出:“不能脱实向虚、贪大求全、搞泡沫化...”
同月,总书记调研提到对新质生产力最担心的事:
“我现在对发展新质生产力,包括对现在的几大领先产业,最担心的还是无序发展,一哄而起,最后一哄而散,那样更挫伤积极性”。
矫正极端泡沫没问题,想要慢牛也没问题,
但问题是金融市场从来都难以驯服,尤其是大A,动辄矫枉过正,要么疯、要么死,
一哄而起、一哄而散,现在已经是“更挫伤积极性”了
欢迎来到 AI 加持下的新时代
🚨🚨🚨Cyber Alert ‼️
🇺🇸USA - 𝗙𝘂𝗹𝗰𝗿𝘂𝗺𝗦𝗲𝗰 𝗖𝗹𝗮𝗶𝗺𝘀 𝟭𝟬.𝟲 𝗧𝗕 𝗗𝗮𝘁𝗮 𝗕𝗿𝗲𝗮𝗰𝗵 𝗮𝘁 𝗣𝗶𝗻𝗴𝗖𝗔𝗣 𝗜𝗻𝗰. (𝗧𝗶𝗗𝗕 𝗖𝗹𝗼𝘂𝗱), 𝗣𝗼𝘁𝗲𝗻𝘁𝗶𝗮𝗹𝗹𝘆 𝗘𝘅𝗽𝗼𝘀𝗶𝗻𝗴 𝗠𝗮𝗷𝗼𝗿 𝗚𝗹𝗼𝗯𝗮𝗹 𝗖𝗼𝗺𝗽𝗮𝗻𝗶𝗲𝘀
The FulcrumSec hacker group claims to have breached PingCAP (TiDB Cloud), allegedly exfiltrating 10.6 TB of data comprising approximately 4.9 million files.
According to the threat actor, the compromised data includes production databases, source code, customer records, credentials and API keys, potentially affecting over 180,000 organisations.
Companies named in the allegedly stolen records include Booking, Shopee, CAPCOM, Colopl, TOPPAN, Catalyst Software, SB Payment Service, Mercari, PayPay, Fujitsu, SoftBank, DMM, AEON, Square Enix, Konami, BANDAI NAMCO, Delhivery, Flipkart, Quincus, Nylas, Airbnb, Pinterest, Databricks, Bitget, Pionex, DigiPlus, NFTscan, Baidu, SES-imagotag, Persona, Transsion, ZOZO, Gumi, Iret, Classmethod, Wetech, Atlassian, Cygames, SBI, LINE, Chainbase, and PatSnap. Additional companies referenced in other allegedly compromised records include Accenture, Red Bull, Klook, Tencent, Huawei, ByteDance, Mastercard, AIG, and Amazon.
The group also claims to have released an initial 200 GB dataset and threatens further disclosures.
Threat actor: FulcrumSec
Sector: ICT
Data exposure (claimed): 10.6 TB (4.9 million files)
Data type: Production databases, source code, customer records, credentials and API keys
Observed: Oct 19, 2026
Status: Pending verification
ESIX©: 7.70
Full details and impact assessment on HackRisk.io
Flrande retweeted
习近平总书记乌兰察布考察期间有关新质生产力的最新指示:
“我现在对发展新质生产力,包括对现在的几大领先产业,最担心的还是无序发展,一哄而起,最后一哄而散,那样更挫伤积极性。还有就是‘内卷’问题,盲目竞争,不顾自身条件地发展。”
唯一能拉开差距的只有算力,体现出来的就是国内憋不出大参数模型,但现实世界里最多人喜欢和需要的还是小参数
Hope to see more evaluation from the real world use case instead of just benchmarks.
In addition to the “OAI $70B ARR”, another key thesis the bulls holding was “active anti-distillation measures plus much shorter thinking chain from the model architecture improvement, will help to enlarge the gap between models and open models”.
If DS’s latest release is indeed this powerful, this thesis would be invalidated as well 🥹


























