satofan

he who chases two rabbits catches neither.

[satofan @satofan4]

@_FORAB 失败是成功之母哈，现在有四个妈了，下一个成功的概率很大！

[satofan @satofan4]

#megaETH 策略
主力：关联历史最丰富、交互最活跃的核心钱包及GitHub账户；
打野：多号赌兜底分配。

[satofan @satofan4]

#megaETH checklist
关键信息
公售总量: 5亿 $MEGA (占总供应量5%)
硬顶 (Hard Cap): ~$49.95M
拍卖形式: 有最高限价的英式拍卖
价格区间: $0.0001 (起始) 至 $0.0999 (封顶)
个人限额: $2,650 - $186,282
• • 核心机制: 超额认购后个人分配额度将与出价、链上Profile等因素挂钩。

[satofan @satofan4]

#megaETH 时间节点(UTC+8)&FAQ
投标开始: 10月27日 晚9点，持续72小时。
冷静期: 11月5日 - 19日，期间可选择放弃分配，全额退款。
FAQ:
Q: 投满$186k就能拿满？
A: 不能。 链上Profile是决定额度多少的胜负手。
Q: 投错了或后悔了怎么办？
A: 有保险。 “冷静期”无理由退货。

[satofan @satofan4]

@0x_antifragile Calling him a scam is an insult to scams themselves.

[satofan @satofan4]

@AlexSoh14 @streamsj131142 这个回购会销毁么？如果团队回购了再转手出货那相当于没回购？

[satofan @satofan4]

@V1JeromeLoo 这一条为啥我去gcr的账号历史没搜到？

[satofan @satofan4]

@reisnertobias where to track hype dat’s mNav?

[satofan @satofan4]

@cryptopainter 这里的资料来源是？

[satofan @satofan4]

@Keisan_Crypto What percentage of the fee is used for buyback? Which document confirms this detail? Seems I haven't seen it.

[satofan @satofan4]

@ViNc2453 @ethena 啊，理解了，忘了1usde 1x 一天=1 point 这个设定，一直想岔了。 感谢vinc耐心答疑，0.01%级别的大使！

[satofan @satofan4]

@ehalm_ With this kind of liquidity, getting to a $10M market cap is a piece of cake. You don't need to spend ages finding the perfect market maker. Time is the most critical factor here. Move now before you completely lose the community's attention.

[satofan @satofan4]

@ehalm_ In crypto, the chart is the best marketing. Pump it, and the volume will come. The narrative will then become a self-fulfilling prophecy thanks to reflexivity.

[satofan @satofan4]

@ViNc2453 @ethena 关键是b8？这里不是用的所有空投价值/总sats得到的吗，为啥是一天的积分价值？可能我哪里没理解对？请指正😁

[satofan @satofan4]

@ViNc2453 @ethena 如果是年化，那是不是要 /58*365呢？

[satofan @satofan4]

@ViNc2453 @ethena 所以计算器上的apy是对应points？我是points或者usde apy都行，就是页面上没写清楚，使用起来很怪。从使用者的角度看，我选了usde的资产，那对应的apy肯定以为是usde的future apy。

[satofan @satofan4]

@ViNc2453 @ethena 这里 9.223已经是空投结束对应1M的空投价值了，为什么还要*365呢？

[Jason Wei @_jasonwei]

New blog post about asymmetry of verification and "verifier's law": https://t.co/28Ly8NHJWZ

Asymmetry of verification–the idea that some tasks are much easier to verify than to solve–is becoming an important idea as we have RL that finally works generally.

Great examples of asymmetry of verification are things like sudoku puzzles, writing the code for a website like instagram, and BrowseComp problems (takes ~100 websites to find the answer, but easy to verify once you have the answer).

Other tasks have near-symmetry of verification, like summing two 900-digit numbers or some data processing scripts. Yet other tasks are much easier to propose feasible solutions for than to verify them (e.g., fact-checking a long essay or stating a new diet like "only eat bison").

An important thing to understand about asymmetry of verification is that you can improve the asymmetry by doing some work beforehand. For example, if you have the answer key to a math problem or if you have test cases for a Leetcode problem. This greatly increases the set of problems with desirable verification asymmetry.

"Verifier's law" states that the ease of training AI to solve a task is proportional to how verifiable the task is. All tasks that are possible to solve and easy to verify will be solved by AI. The ability to train AI to solve a task is proportional to whether the task has the following properties:
1. Objective truth: everyone agrees what good solutions are
2. Fast to verify: any given solution can be verified in a few seconds
3. Scalable to verify: many solutions can be verified simultaneously
4. Low noise: verification is as tightly correlated to the solution quality as possible
5. Continuous reward: it’s easy to rank the goodness of many solutions for a single problem

One obvious instantiation of verifier's law is the fact that most benchmarks proposed in AI are easy to verify and so far have been solved. Notice that virtually all popular benchmarks in the past ten years fit criteria #1-4; benchmarks that don’t meet criteria #1-4 would struggle to become popular.

Why is verifiability so important? The amount of learning in AI that occurs is maximized when the above criteria are satisfied; you can take a lot of gradient steps where each step has a lot of signal. Speed of iteration is critical—it’s the reason that progress in the digital world has been so much faster than progress in the physical world.

AlphaEvolve from Google is one of the greatest examples of leveraging asymmetry of verification. It focuses on setups that fit all the above criteria, and has led to a number of advancements in mathematics and other fields. Different from what we've been doing in AI for the last two decades, it's a new paradigm in that all problems are optimized in a setting where the train set is equivalent to the test set.

Asymmetry of verification is everywhere and it's exciting to consider a world of jagged intelligence where anything we can measure will be solved.