塔仔roll了又roll

All in Polymarket，发掘概率洼地，

[塔仔roll了又roll @bony0002]

https://t.co/jHo8G4gaUH

[塔仔roll了又roll @bony0002]

数据模型分析是买PSG90分钟小胜比分盘口或者阿森纳拿冠军，这俩正EV，人类专家分析是建议跟单PSG 90分钟小胜。最后决定跟单PSG90分钟小胜

[塔仔roll了又roll @bony0002]

SpaceX IPO 我下的单：基于HyperLiquid的价格区间，在polymarket上的下单 $14k 在 >$2.4T YES @ $0.35 + $4k 在 >$2.6T YES @ $0.26 + $2k + 在 >$2.0T YES @ $0.68 。Total $20k stake, expected +$10k EV (50% return), 个人估算大约20% 概率全亏 -$20k
https://t.co/ZrZ2dxLVYP

[塔仔roll了又roll @bony0002]

还好，只是内部地址

[Bubblemaps @bubblemaps]

ALERT: 🚨 Polymarket contract exploited

Attackers are removing 5,000 $POL every 30 seconds – $600k stolen so far

Pause all Polymarket activity for now

[塔仔roll了又roll @bony0002]

https://t.co/qFZATO7JMi
访华期间应该不会签协议

[塔仔roll了又roll @bony0002]

@aoke_quant 没有英伟达啊

[塔仔roll了又roll @bony0002]

这个对opus 4.7尤其有用啊，太tm会偷懒了

[Garry Tan @garrytan]

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.

[塔仔roll了又roll @bony0002]

claude buddy保佑我别被封号！！

[塔仔roll了又roll @bony0002]

@cqkten on travel, bad network so just market sell

[塔仔roll了又roll @bony0002]

claude 5.7 - 63.7，因为gemini 3.1出了就跑了
外加一个之前gemini 第一的no，22 - 78.3

[塔仔roll了又roll @bony0002]

https://t.co/L5UYxXonK9
配合https://t.co/387jiAVAwg
压下一个大更新是ds v4和claude领先

[塔仔roll了又roll @bony0002]

@0xLuckyPig 好的，我现在是每天看，但是很少发东西，看到肯定回的

[塔仔roll了又roll @bony0002]

https://t.co/L5UYxXonK9
配合https://t.co/387jiAVAwg
压下一个大更新是ds v4和claude领先

[塔仔roll了又roll @bony0002]

Ralph Wiggum 插件真挺好用 https://t.co/tiGN8A5ZoS

[塔仔roll了又roll @bony0002]

@_TobiasLee @LotusDecoder 同意，上下文管理，合理的skills，定期回归产品，演进，bug文档，这些都很重要

[塔仔roll了又roll @bony0002]

@LotusDecoder @zhiyebanzhuan 确实

[塔仔roll了又roll @bony0002]

让LLM编写代码递归分解和调用自身，实现上下文规模扩大两个数量级，且保持性能稳定，代码能力大大提高。现有的大模型其实智商和知识底座绝对足够了，今年更多应该是针对不同的工作内容来优化方法论，工具调用和不断总结优化具体工作的最佳实施方式。

日常写代码也有体会，不管是人工还是vibe coding第一遍出来的从产品文档到代码都有很多问题，我自己也是尽量做分解模块，每个小模块都走两三遍，然后逐级递归整合优化，产品文档和实施方案也都要多review几遍再开始真正实施，前期花的时间越多，写代码出现的问题越少，有问题时参考文档或者修改文档，保持一致性，免得最后写出来的东西都不知道是为啥了。

[alex zhang @a1zhang]

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!

https://t.co/x47pIfIkTb

[塔仔roll了又roll @bony0002]

准备去搞一套

[塔仔roll了又roll @bony0002]

好饭不怕晚，分数凯利下注总体还是赚了一点点。当时买的时候看不上三月的那个一倍赔率，买少了😂😂😂https://t.co/ctxOcQUIHS

[塔仔roll了又roll @bony0002]

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。

[Dr.Hash“Wesley” @CryptoApprenti1]

https://t.co/C5CzmbA1Dw