One pattern I find useful for working with LLMs is a nice long ramble session. Sometimes the LLM needs more bits to understand what you're trying to achieve, but you're too lazy to type them. In these cases I like to lean back, switch to /voice and just ramble for like 10 minutes, total mess, anything goes, full stream of consciousness. Sometimes I declare it up top, something like "switching to speech recognition sorry for any typos...". Sometimes I turn it into a small interview of a few turns. But I find that the LLMs are somehow very good at reconstructing long incoherent rambles and often their echo of your own tangle of thoughts comes out quite a bit cleaner than what you started with. The result is that you improve the mind meld and have to correct things less from that point on.
The setup that has made me the most money over my trading career
Is the the VCP (Volatility contraction pattern)
Searching for coiled price action has not only given me more success when the market is strong...
BUT
Its also allowed me to know when to do less in the markets
You're looking for price to get tighter and tighter, almost like a coiled spring or a pennant...
If theres not many setups that fit the bill, its telling you the market is wide and loose = weaker conditions
Here's what I want to see:
• Higher lows
• Tightening price ranges
• Volume drying up
• Price holding above key moving averages
That tells me sellers are getting exhausted while demand continues to absorb supply.
The tighter the stock coils, the more explosive the move can be once buyers step in.
Train you eyes to searched for these tight spots and you will see how much more follow through your trades will get...
I'd always rather wait for compression...
Because
that's where the best risk-to-reward opportunities are often created.
I made a graphic below to help you visualize this
“In all ages, whatever the form and name of government, be it monarchy, republic, or a democracy, an oligarchy lurks behind the facade. And Roman history, republican or imperial, is the history of the governing class. “
信息市场有一个著名的悖论:“购买者在获得信息之前,无法知晓它的真正价值;然而一旦他知道了,实际上就已经免费获取了该信息。”在诺贝尔经济学奖得主肯尼斯·阿罗的“信息悖论”(Information Paradox)中,卖家面临的风险是:为了把知识卖出去,就得承担“白送”知识的风险👀
微软 CEO Satya 最新的这篇「逆信息悖论」,提到 AI 制造了一个恰恰相反的难题。在 AI 时代,买家面临着泄露知识的风险——而这仅仅是为了使用他们花钱买来的服务。
从本质上讲,你是在为“智能”买两次单:一次是用真金白银,另一次则是用更值钱的东西——即为了让 AI 发挥作用而不得不透露的“私有知识”。而且,你越是希望模型表现出色,就越需要用更多的私有知识去喂养它!
久而久之,这种信息不对称会变得愈发严重。在你使用所购服务的过程中,卖家对你的了解越来越深;而对于卖家究竟从你这里学到了什么,你却知之甚少。
这就是“逆向信息悖论”(Reverse Information Paradox)。那么在智能时代,企业应该如何保护其核心知识产权?除了知识产权之外,AI 时代我们必须将学习基础设施分发给每家公司,让他们能够掌控自己的学习循环。
在云计算时代,企业积累的是数据;在 AI 时代,企业积累的是学习成果。信任边界也必须随之演变,从保护信息本身转变为保护组织学习、适应和积累智能的机制。
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