satofan

@_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！

问了几个这波投 USDX 巨亏的老哥，主要看好创始人的连续创业经历，都觉得肯定能做出下一个贝宝金融。 看了下领英，创始人确实是连续创业者。 虽然，结局都不太好吧。。

#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。

#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。

#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。

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

Maybe the most shameless stuff I have seen this year, deposit cap essentially filled 15minutes before any public announcement with top 6 wallets (accounting for 400M+) all funded from BTSE 14 hours ago 💀 CRAZY WORK

@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？

@streamsj131142 不是。新加坡华籍。CAKE的回购若持续下去，价值回归是迟早的事

Bought the dip again. 801k $CAKE worth $2.1m atm.

@V1JeromeLoo 这一条为啥我去gcr的账号历史没搜到？

GCR名言：（翻译加强版） 在我研究 meme 代币周期后得到的通用交易原则： 当趋势来临时，你应该直接大仓位干进去 随着时间推移逐步止盈 大多数人亏损是因为他们做了完全相反的事： 早期行动缓慢，随着时间推移越来越贪婪 带入到现在的bsc meme supercycle来说 先知先觉的上来就猛干的 现在都是利润在玩了 如果你属于后知后觉 可能结局已经注定🐶

@reisnertobias where to track hype dat’s mNav?

$HYPE is at All Time High but HYPE DATs are underperforming mostly because deals are just closing slowly and stock dilution. Will be interesting to watch how those play out over the next months. Personally I hold some $HYPD in my company mainly because it's easier to buy stocks within a company vs. crypto. $HYPD $LGHL $SONN $HYLQ

@cryptopainter 这里的资料来源是？

这是他最近20年的经历... 曾经担任北欧化工的CEO及创始人，在2012年的时候就已经名义上破产了... 后来的流浪与狱中生活彻底摧毁了他...

就很唏嘘啊... 《海龟交易法则》的作者——柯蒂斯·迈克尔·费思，就是那个曾经跟着理查德·丹尼斯在海龟计划中坚决执行交易规则而在股市赚钱的人... 最近几年被发现已经成为了美国街头的一名流浪汉... 他最后的已知住所是波士顿一家流浪汉收容所... 我很喜欢《海龟交易法则》，虽然其中的交易策略早就 过时，但那本书所宣扬的交易观依旧非常有价值。 1. 价格不可预测；（白糖期货的案例） 2. 坚决执行交易计划。（海龟计划的结局） 这两点是对我帮助最大的启示！ 即使书中的传奇交易员“理查德·丹尼斯”最终也因为股市爆亏60%以上而彻底离开市场，作者因为这本书走红但也最终沦为流浪汉... 但书中的有些观点依旧非常值得学习。 这两个人足够聪明，但最终依旧没有在市场中活下去，可能原因还是出在执行上了。 有句话说得好，聪明人看执行，执行力不够，就会聪明反被聪明误...

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

(2/5) Financials Hyperliquid put in its best fee month since inception -- during August -- historically one of the slowest trading volume months in the entire year Not only did Hyperliquid put in its best month, but this was 22% higher than July (the previous record month), and was Hyperliquid's third record month in a row To get an idea of Q3, I conservatively assume September will be 10% higher than August, which would put Q3 at a staggering $332M in fees, a 88.4% QoQ growth from Q2 Using August-annualized cash flows, I have $HYPE trading at ~14x P/E, while it just grew 88.4% QoQ and 121% in 7 months since January. If $HYPE traded in public markets, I'd venture to say it would be trading at a P/E of ~5x what it is currently trading at Many continue to try to price Hyperliquid at its current fee rate, instead of paying attention to what is one of the most incredible growth stories in financial history. The data is right in front of you, have a look

(1/5) Hyperliquid Fee Growth YTD 2025 🧵 - 15.4% avg MoM growth - 121.2% growth from January --> August - 290.1% annualized CAGR - $3.67M avg daily fees in August - 3 record fee months in a row No signs of slowing down Some more stats / thoughts below 👇

@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！

@satofan4 @ethena 如果 1M 分值 $10 1分值 $0.00001 那1 USDe 1x 倍数 挖1天就是 $0.00001 的利息 $0.00001 x 365 就是 1x 积分的 APR 了 这是也不是什么高深数学

Ethena 第4季积分收益的预期、附积分年化表 @ethena 空投积分规则玩法已很成熟、不可控的因素已很少：S4已确定到9月24日、最少分配3.5%代币，什么最低持仓要求自S3起也已取消了，特殊规定只剩下top 2000地址有33％奖励需解锁 (Pendle上的USDe池子挖的Sats可豁免)… Sats积分排放量也相当稳定（目前推算最后总积分量~34T）所以，积分价值从 $ENA 币价已差不多能估算出来 💡于是，现在Ethena系列的 @pendle_fi YT基本上就是「一种 PT」＝ 折价买入三个月后的 $ENA 例如，参考以下表格中积分倍数60x一列，按 $ENA=$0.27 估值的话，积分收益(三个月APY)就是 9.13%左右。把9.13%填写到 Pendle介面上的计算器(见附图二)，可以算出按现价大约7.5%左右成本买的9月份USDe YT，最后可以获利(ROI) 22％ － 或大约等效按18.5％折价买进三个月后的 $ENA 注意：大部份人其实还有至少10%积分加乘 (例如忠诚加乘)，所以表格同时附止积分倍数增益10%的年化供参考 （上例中如果60x提升至66x，积分收益约有10.05%，相当于35％ROI／折价26%买币) 换个话说，目前参与7.5%成本的YT-USDe，币价「容跌空间」有~18.5-26%，这当然越多越好，所以就取决于你的买点了。例如7%的YT成本，同样按 $ENA=$0.27 估值，ROI将升至33-46%、或相当于25-31%折价买币 我自己目前操作是，挂好限价单，等接一时跌下来的便宜YT 〔LP〕 没兴趣买YT的话，其实 Pendle 上的 Ethena 稳定币LP 收益也非常不错：由于LP内目前普遍有85％内容为带积分的"SY"，所以我们看LP年化时，在Pendle介面显示的数字之上，还应该加上预期的LP积分年化。表格中同时附上LP的积分收益，例如七月份的USDe LP池的max年化UI上显示为13%，加上积分预期后就是～20.8% Pendle LP 只要持有至到期，就保证最低限度币本位(USDe本位)保本，所有年化／排放／积分都是白嫖赚的 最后，以上仅为分享个人分析Ethena积分的思路，NFA & DYOR 如果想我定期更新这个表格，请多赞好、留言、转发分享，谢谢！

@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.

@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.

We just burned 8,000,000 $YAPPER for all the YAPPER video credit purchases and creator payments made in the past 2 weeks. Keep yapping 🗣️

@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😁

@satofan4 @ethena 本身已经是一天化了的积分 你自己去算一次用你的计法会变成什么样子😆

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

@satofan4 @ethena 年化的定义….

@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。

@satofan4 @ethena 3. 因为Ethena的 Points 本来就是 USDe 本位 一个 50x 积分倍数的池子，1 USDe 一天就是 50 Sats

@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？

@satofan4 @ethena 1。基本除数

New blog post about asymmetry of verification and "verifier's law": jasonwei.net/blog/asymmetry… 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.