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I love these gauges, what are your favorite sites or places to get yours from? #gauges# #alternativegirls# #lingeries# #suicidegirls# #suicidegirlhopeful# #allnatural# #girlswithtattoos# #onlyfans# #latina# #tatted# #onlineshopping#

The S&P 500 fell slightly and the Dow ended virtually unchanged as investors ​waited for key earnings reports to gauge the health of a market rally fed by enthusiasm for AI

ve33大概率将退出历史舞台，Aerodrome在7月要上线的新版本对ve33的几项改进都是极具针对性的。 Aerodrome指出的ve33存在的最大问题就是滞后性，池激励每周投票更新一次，且投票趋势都是基于历史表现，比如上周Pool A的表现好，那么投票多数在下周还会集中在A，这对于新Pool非常不友好， 对某个新Pool的爆发潜力也是基本感知不到的。 新的设计取消了vetoken，改成sAERO（可随时转移了），取消了epoch每周投票这个设定，而是可以随时调整投票。AERO 奖励也是以streaming的形式连续发放到Pool gauge，同时fee也是实时积累返给sAERO，看起来改动不大，但实际上Pool、LP、holder三者的关系发生了巨大变化。 原来的设计，投票和激励获得都是有滞后性的（1周），而手握票权的veAERO，想要获得更多的fee分成，那么就要判断未来表现好的Pool，在老机制里，这基本是没什么办法的，只能去选历史表现好的Pool，因为你即使判断下周有一个新Pool会爆发，你去押注它的性价比也极低，这1周的窗口期选错就会损失大部分收益，基本没有人会去做这种前瞻性的预测，而是不停的追数据好的Pool，一旦一个Pool成为“历史赢家”，它会持续吃排放，即使用户开始怀疑它未来会变差，也很难快速切换（1周的硬性周期），明显这对于新Pool来说不友好。 把epoch改掉之后，灵活性大大提升，预测正确后的放大效应更强，预测错误后的止损速度也更快，这会让整体资本配置效率更高。 进一步思考，这种实时、可动态调整的设计天然更适合 AI Agent 进行自动化决策和频繁调仓，这也是Aerodrome一直在做的适合AI接入的dex这样一个故事，原来的vetoken模型则是很难的。

I explained the Chinese real estate & debt crisis in much detail in 2024 on my Substack. Nothing has changed since. China is in what we call "the largest balance-sheet recession the world has ever seen". And it will take years to get out of it and assuming the CCP's investment-led growth model does not dig the next hole in the meantime - a likely. The FT published added some colour to it two days ago: "Housing is important to every economy. But to China, it’s extra important. According to the PBoC, 96% of urban households own a home, and 41% own at least two. The average household owns 1.5 properties. And as such, property constitutes around 70% of China’s private wealth. The comparable figure for the US is around 30%. So when Chinese property prices fall, the authors make a pretty compelling case that this has all sorts of particularly bad economic spillovers. And fall they have. The negative wealth effect is substantial, and “effects are amplified by elevated household debt, much of which consists of mortgage obligations”. This — and the weaker income expectations that the falls generate — goes some way to suppressing consumption. Moreover, declining land-sale revenues constrain local government budgets, “limiting their capacity to finance developmental projects and maintain existing public infrastructure”. And this is even before any credit impacts from rising non-performing loans and mortgages on bank balance sheets are considered. Tl;dr: bad bad bad. Of course, China isn’t the first soon-to-be-global-economic-hegemon-East-Asian-power staring down demographic oblivion to have piled its savings into a property boom. Back in 1991, the world was fretting over the rise and rise of Japan. And the Japanese were buying Japanese residential real estate at outlandish prices. Japan’s house prices peaked back in 1991 and spent the next 30 years on a downward trajectory. We’re only a few years into the Chinese property bust, and its ultimate trajectory is both unknown and unknowable. But Rogoff and Yang have pulled together some cool data they kindly shared with Alphaville, allowing us to make this chart below. So far, it looks like prices in Chinese cities are falling at around the same pace as they did over the first five-to-10 years of Japan’s bust. Japan’s property crash is associated with a lost decade (or two) of economic growth. In the 10 years leading up to 1991, Japanese real annual GDP growth averaged 4.4%. In the subsequent 10 years it averaged only 0.9% per annum. The same numbers for China, with 2021 marking its property zenith, are 7.0% per year and 4.6% per year (so far). If the IMF’s forecasts turn out right, this latter number will fall to around 4.0% per annum. While the levels are different, the before-and-after drop looks comparable. Was it housing wot dun it? Rogoff and Yang reckon that a 40% decline in house prices translates into a total consumption loss of 2-4% of GDP. Not nothing, but not a single answer explaining life, the universe and wiggles in the decadal pace of real economic growth. To get here, they construct a historical dataset comprising subnational data across 47 prefectures, and input and output data at granular industry levels. They then use this to examine the macroeconomic implications of Japan’s real estate bust. And the authors argue that: a housing bust can generate substantial adverse effects on the economy via real channels. . . . overbuilding during the boom can trigger a demand-driven recession with limited reallocation and low output. Unlike financial channels, which amplify shocks through leverage, bank balance sheets, credit constraints, or fire sales, real channels operate directly through investment, consumption, labour markets, or productivity. In Japan’s case, the housing market collapse depressed activity through three key real channels: investment, consumption, and sentiment. This is all pretty intuitive. But using city-level and household-level Chinese data plus some whizzy maths, they put meat on the bone for these three channels. They find that Chinese cities that overbuilt housing the most are less keen on new building, suppressing investment. Sounds legit. Chinese household consumption is estimated to be more responsive to house price changes than it was in either Japan or the US given its outsized role in private wealth. And it looks to the authors like people have scrambled to rebuild precautionary savings they thought they had amassed in property. Understandable. Then, on the sentiment side, Rogoff and Yang use an LLM to gauge market perceptions of the housing market. And by incorporating city-specific perceptions, they double the estimated effect of house price changes on consumption. Huh. While China is not Japan, 1991 was not 2021, and a *lot* of other things are/were going on, it’s interesting to see that the overall magnitude and pace of property price falls — as well as the aggregate drop in the pace of headline GDP growth — has (so far) been spookily similar. And as for the big question — are we there yet? "If China’s adjustment unfolds in a similar way as Japan’s, it would mean China has not gone half way through the transition. By contrast, if China’s path is eventually comparable to the United States, it appears to have already covered roughly two-thirds of the adjustment before reaching the bottom." So more to come.

Hackers breached automatic tank gauge systems used to monitor fuel levels in underground storage tanks at gas stations across the United States. These systems were connected to the internet but lacked basic security protections such as passwords. U.S. officials suspect Iranian hackers. The intruders could view and alter the readings on tank monitors but could not change actual fuel amounts or cause physical damage or operational disruptions. The main concern is that attackers could mask a real fuel leak by falsifying data, creating safety or environmental hazards. No such incidents have been reported.

A key strength: Gemini Robotics-ER 1.6 combines spatial reasoning, world knowledge, and agentic vision to allow robots to read a variety of instruments. See how it reads an analogy gauge right down to sub tick accuracy ↓

While visiting the Ganges River, @nikolajcw met Ankit Agarwal, founder of Phool, a company turning temple flower waste into new products. Here’s what inspired him to start the mission. Watch the full episode of An Optimist’s Guide to the Planet 

We have a long history of using games to measure progress in AI. 🎮 That’s why we’re helping unveil the @Kaggle Game Arena: an open-source platform where models go head-to-head in complex games to help us gauge their capabilities. 🧵