塔仔roll了又roll

赚取100万美元的几率： 彩票：0.00000013% 职业运动员：0.03% 联姻致富：3.82% 股市：5.92% 房地产：8.94% 加密货币：10% 企业主：12.2% 预测市场：69.69%

ALERT: 🚨 Polymarket contract exploited Attackers are removing 5,000 $POL every 30 seconds – $600k stolen so far Pause all Polymarket activity for now

Boil the Oceans You know the phrase: “don’t boil the ocean.” Everyone’s said it in some overly ambitious meeting. It’s good advice in normal times. It keeps teams focused. It prevents scope creep. But we are no longer in normal times, and I think it’s time to retire saying it. Artificial Superintelligence means it’s time to boil the ocean. We’ll start with a few lakes first. I was recently with a university endowment’s head of private investing who told me their engineers were terrified for their jobs after seeing what Claude Code could do. And I get it — that’s the natural first reaction. But it’s the wrong one. It’s a zero-sum reaction to a positive-sum moment. Instead of worrying about doing the same thing we’ve been doing for cheaper, why not focus on doing the thing we never even dreamed of doing? Why can’t that endowment achieve 50% net IRR instead of 10%? Why can’t a startup deliver a service that is 100x better than the incumbent? Why can’t we have fusion energy? Why can’t we talk to every single user and have a perfect understanding of every bug in our product? These aren’t rhetorical questions anymore. They’re engineering problems with paths to solutions. Here is what I think is actually going on with the fear: our fear of the future is directly proportional to how small our ambitions are. If your plan is to keep doing exactly what you’re doing, then yes, a machine that can do it faster and cheaper is terrifying. But if your plan is to do something dramatically bigger, then the machine is the best news you’ve ever gotten. If you’re a worker — someone who trades labor for a living — this is the moment to become a builder. Start a business. And if you’re already management or capital, it’s time to go 10x more hardcore on what your aspirations could be. Not eking out 5% efficiency gains. Not increasing profit margins 2% by lowering cost and firing people. Those are the old games. The new question is: what would it look like to build a product or service so good that people would happily pay 10x what they pay now? The net result of this is more jobs, not fewer. As Ryan Petersen likes to say, the human desire for more things is absolutely limitless. We can actually fulfill that desire now — if we have the agency to prompt it for ourselves. Buckminster Fuller coined the term “ephemeralization” in 1938: doing more and more with less and less until eventually you can do everything with nothing. His entire vision of progress was about technology enabling radical expansion of human capability through dematerialization. He traced this from stone bridges to iron trusses to steel cables — each iteration stronger, longer, lighter, cheaper. He wasn’t describing job destruction. He was describing civilization getting better at being civilization. This is Jevons Paradox for everything. When you make a resource dramatically more efficient, you don’t use less of it — you use vastly more. Steam engines didn’t reduce coal consumption. They made coal so useful that demand exploded. The same thing is about to happen with intelligence, with labor, with every service and product we can imagine. But Jevons Paradox doesn’t activate on its own. It requires capital and management to actually raise their ambitions — to boil lakes and oceans instead of drowning them in committee That’s what startups have always been good at: moving fast in the face of radical uncertainty, building for the 10x future while everyone else is optimizing for the 1.05x present. Time to start.

polymarket.com/event/deepsee… 配合polymarket.com/event/which-c… 压下一个大更新是ds v4和claude领先

Much like the switch in 2025 from language models to reasoning models, we think 2026 will be all about the switch to Recursive Language Models (RLMs). It turns out that models can be far more powerful if you allow them to treat *their own prompts* as an object in an external environment, which they understand and manipulate by writing code that invokes LLMs! Our full paper on RLMs is now available—with much more expansive experiments compared to our initial blogpost from October 2025! arxiv.org/pdf/2512.24601

1. 六号左右入场，航母在摩洛哥停驻，大家觉得是准备打尼日利亚，Yes概率低估了 2. 川普没有向国会提交相关议案，yes概率降低，最多的时候亏一半 3. 十号二次入场，思路是通过grok查找公开信源OSINT，以及相关分析各种航船信息，飞机位置信息，目击信息等等的大v，确认大概率是航母还在朝加勒比海挺近，并且补给船在全速靠拢。 4. 过了一两天，概率有小变化，但是不明显，甚至航母已经到加勒比海附近了还是没变化，直到阿利伯克级靠近，消息发酵，补仓后还是略亏。 5. 今天终于涨起来了！！！grok第三次分析，一般航母打击群到位后七天内发生military engagement的概率是很高了，看看政府开门红是不是落在委内瑞拉。 总结：grok是目前收集x上高质量推特最快和最及时的，对信息和规则的分析能力稍差一点问题不大，毕竟很多信息是已经过专业分析过滤。