X Ye

[Dogan Ural @doganuraldesign]

Introducing: Tesla RoboPod

First Full Self-Driving Stroller

Starts from $2499

[TrendSpider @TrendSpider]

We had 24 years to accumulate Apple for less than the cost of a Costco hot dog combo...
$AAPL

[JasonZ @Jason_WealthAI]

暴跌夜，美股吓哭一票散户，连夜割肉。

同一晚，木头姐却反手干了一件「撕毁十年信仰」的大事：几乎清空 AMD、百度、Trade Desk，

把子弹全压在 3 只「AI 下半场保命底牌」

[Mark Sterling @MarkSterling0]

The market is crashing, panic is spreading.
At this very moment, Trump’s team suddenly announced: they may acquire U.S. shares of artificial intelligence companies!
This is not a coincidence, but a signal.
History has repeatedly proven:
Whenever you follow President Trump in buying stocks, it often marks the beginning of great wealth.

I’ll say this once. These 8 stocks are going to make generational wealth for many by year end…

1. Nvidia~ $NVDA
2. Microsoft ~ $MSFT
3. Alphabet ~ $GOOG
4. Amazon ~ $AMZN
5. Marvell Technology ~ $MRVL
6. UMeta Platforms ~ $META
7. IonQ ~ $IONQ
8. Palantir ~ $PLTR

Many people ask me: why don’t you charge?
The answer is simple: I’ve already made enough.
Sharing is my passion — that’s why I insist on publishing for free.

[Mulight 沐光🌟 @0xMulight]

不会还有很多人搞美股还在手动查找数据做分析吧？
其实开源社区已经给出了几套非常成熟的美股 Skill 工具。

今天帮大家盘点目前 GitHub 上最火的 5 大美股Skill神器，帮你快速找到最适合自己的那一款：

1. 数据获取神器 ranaroussi/yfinance （ 24.1k）
这是美股数据获取的入门神器。几乎��有量化项目都在用它，一键拉取历史，实时行情、财报、期权、新闻、分红，支持批量Ticker，AAPL、NVDA、特朗普概念股随便搞。新手5分钟出第一个K线图，闭眼选这个。

仓库直达：https://t.co/Pg0nQLkrPi

2. 全能研究平台 OpenBB （ 68.7k）
目前量化美股研究领域星标最高的平台之一。Python终端 和数据看板，支持股权历史、财务筛选器、新闻、宏观指标，一站式拆解特朗普AI/芯片/能源概念股。想做深度调研的玩家，首推这个。

仓库直达：https://t.co/Xo1nzvP9Cc

3. 基本面分析利器 JerBouma/FinanceToolkit （ 4.9k）专注透明计算的财务工具箱。150+财务比率（PE、ROE、杜邦分析、WACC、风险评分），估值模型全部公开。搭配yfinance拉财报，直接理性评估美股基本面，避免盲目跟风。追求硬核拆解的必备。

仓库直达：https://t.co/eXLmsqqKmd

4. 回测速度怪兽 polakowo/vectorbt （ 7.8k）
向量化和Rust加速的回测引擎，能同时跑几千个策略，比传统工具快几十倍。支持参数扫描、信号生成、回撤分析、TA指标，直接拿Yahoo美股数据验证“跟着特朗普买 vs 定投标普500”哪个更稳。中高级玩家专属。

仓库直达：https://t.co/cGxvPvDqWL

5. 量化资源导航 awesome-quant （ 26k+）
最全的量化金融资源大全，还在持续更新。里面美股相关神器一网打尽：yfinance、回测框架、数据源、AI量化、风险管理全都有。当作技能���用，想学哪个方向直接点进去。

仓库直达：https://t.co/ThjQS9pi48

小建议，结合特朗普持仓实战：
新手快速上手 ——直接用yfinance + OpenBB
想做基本面拆解 ——FinanceToolkit
要验证策略实盘 ——vectorbt
想系统性进阶 —— awesome-quant当导航

这些项目都免费开源、社区活跃，随便挑一个就能把美股分析升级成现代量化体验。

投资有风险，仅分享工具，DYOR，玩得开心～

[Patrick Crawford @patrickonx]

Longtime TSLA holder since 2013, never sold a share. Requested 3700 SpaceX IPO shares via Fidelity. Strategy: keep 20% and flip the rest. Expecting IPO rocket but a sell-off after several months, might dip below $1.75T valuation. What’s y’all’s SpaceX strategy

[Puru Saxena @saxena_puru]

The stock market is STILL overbought

Decline towards the 40-week ma is likely outcome, time for caution over the next several weeks.

$NDX $SPX

[TrendSpider @TrendSpider]

"If all you ever did was buy high-quality stocks on the 200-week moving average, you would beat the S&P 500 by a large margin over time. The problem is, few human beings have that kind of discipline."

- Charlie Munger
$MA

[Sunil Gurjar, CFTe @sunilgurjar01]

GOLD Is About to Repeat 1979. Last Time This Happened, It Pumped Hard.

1979: Iran war → oil price 2x → crisis → Dump → Pump
2026: Iran war → oil price 2x → crisis → Dump →

[Massimo @Rainmaker1973]

30,000 hours of footage, equivalent to 3 years and 7 months, were filmed to capture the blooming of 77 types of flowers, and the result is spectacular.

[Douglas Swift @SwiftMacro]

The biggest takeaway isn’t that AI demand is at an all-time high.

It’s that we’re discovering compute isn’t the scarce asset.

Power is.

When the richest companies in the world are waiting on grid connections, you’re no longer in a technology race.

You’re in an infrastructure race.

[静曼婉儿 @BGlayds30098]

🔥 轉：这是在湖北省建始县花坪拍到的非常珍贵、难得一见的“九头鸟”。
十几位摄影家，用了62天才拍到此视频。九头鸟至少可以变九种头脸，像极了川剧的绝活变脸。极其珍贵，请大家欣赏。
😂 这就是“天上九头鸟，地上湖北佬”的来源。

[無名先生 @Wuming_Mr_]

🚨 2026-2027 周期完整路线图

六月 → 💀 强制清算
七月 → 🔄 市场重置
八月 → 📦 吸筹
九月 → 📈 复苏
十月 → 🚀 BTC 历史新高
十一月 → 🎯 牛市陷阱
十二月 → 📉 市场抛售
一月 → 😱 恐慌
二月 → 🏳️ 投降式抛售
三月 → 🐻 周期底部

👑 过去 10+ 年，每一次重大顶底全部命中

✅ 2022 年 17K底部
✅2025年17K底部
✅2025年126K 顶部

🔔 错过之前？

别担心，今年我还会 提前喊。
还没关注？现在正合适。

[Puru Saxena @saxena_puru]

South Korea's $KOSPI is VERY over-extended

Pullback to the 10-week ema = ~20% decline
Pullback to the 40-week ema = ~42% decline

Pullback in semis + memory stocks will be painful

[Mister Crypto @misterrcrypto]

Charlie Munger, one of the greatest investors of all time, said:

"Buy high quality assets on the 200 week moving average and you'll beat the S&P 500 by a large margin."

The best buy signal in Bitcoin isn't a pattern or an indicator.

It's the 200 week moving average.

Every time $BTC has touched it, it marked a generational bottom.

2015. 2019. 2020. 2022.

We just touched it again at $61,800.

[Jason @aimee_ericksen]

My favourite stocks in each sector right now:

Nuclear: $SMR
Robotics: $OUST
Rare Earth: $UAMY
Copper: $COPX
ETF Memory: $DRAM
ETF Space: $SATL & $VELO
Solar: $CSIQ , $TE & $ENPH
Drones: $ONDS & $KRKNF (UUVs)
Neoclouds: $IREN $NUAI & $KEEL
Health/Bio Tech: $HIMS , $IBRX ,$PRME

[Rocky @Rocky_Bitcoin]

今天去华强北，跟朋友聊天，发现AI真的太疯狂了，他们最近这些囤货的倒爷，2个月里面，诞生了6个亿万富翁，主要是屯内存芯片和倒卖服务器的！

前两个月，B300服务器，深圳保税区报价为370万RMB，现在已经炒作到1000万+了，还要当天结算现钱现货，真的好夸张！

他们这些屯硬件的，就跟年初那些在水贝屯黄金白银的商户一样，一天一个价，亢奋到了极点！

从华强北逛完一圈回来，总感觉哪里有点不对，市场貌似有点太疯狂上头了！

回来赶紧查了一下最近上市公司高管卖出的情况，如👇图，基本也在疯狂抛售！

让AI读了一下图片的数据📊，汇总如下：

总减持金额： 样本数据内累计减持/拟减持总额约为 $67.45 亿美元。

AI 核心企业套现： 累计金额达 $32.59 亿美元，占据总减持规模的 48.3%。

传统/非AI企业套现： 累计金额约为 $34.86 亿美元，占比 51.7%。

🧐其中这几家AI公司，卖出最为严重：

1️⃣ #DELL (戴尔科技) —— 绝对抛售主力（$20.81 亿美元）

DELL 是核心 AI 服务器供应商，受益于英伟达 Blackwell/H100 服务器订单暴增。

卖出详情： 创始人迈克尔·戴尔旗下的基金会（Michael & Susan Dell Foundation）单笔拟卖出即达 $12.48 亿；

主要战投银湖资本（Silver Lake）及关联实体多笔连续抛售，累计套现超 8 亿美元。

2️⃣ #SNOW (Snowflake) —— 云数据 AI 平台（$5.43 亿美元）

企业级 AI 数据仓库与大模型对接平台。

卖出详情： 核心高管 Frank Slootman 密集通过 Form 4 完成多笔减持，总额达 $5.43 亿。

3️⃣ #SYM (Symbotic) —— AI 智能���流机器人（$2.82 亿美元）

卖出详情： 大股东 SVF Sponsor 密集执行了 $2.82 亿美元的直接市场卖出（Sale）。

4️⃣ #MU (美光科技) —— AI 高带宽内存 HBM（$0.74 亿美元）

卖出详情： CEO Sanjay Mehrotra 进行了约 $7,440 万美元的减持（部分为 10b5-1 计划）。

最近6月份还是悠着点，很早就提示了🔔了，一定注意仓位管理！🧐

[Crypto Rover @cryptorover]

SPACEX COULD BECOME THE BIGGEST EXIT LIQUIDITY EVENT IN MARKET HISTORY.

And the data backs it.

Most of the biggest IPOs of the last 15 years eventually crashed hard after listing.

• Robinhood: -90%
• Rivian: -88%
• Lyft: -79%
• Uber: -68%
• Palantir: -53%
• Coinbase: -57%
• Snap: -56%
• Twitter: -58%
• Facebook: -54%

The median max drawdown across major IPOs was around -54% within 1 year.

And these were not small companies.

Most of them were industry leaders, heavily hyped by media, backed by top institutions, and considered “must own” stocks before listing.

Now look at SpaceX.

The company is reportedly targeting a valuation of around $1.75-$2 TRILLION.

That would instantly make it one of the largest companies in the world.

At the same time:
• Retail demand is extremely high
• The IPO float may stay very small
• And early investors are sitting on massive gains from private rounds.

This is the exact setup where IPO prices can disconnect from fundamentals very quickly.

History shows retail investors usually enter during peak excitement while early investors slowly get liquidity after listing.

Facebook became a great business long term. Still, its stock max drawdown within a year was 54%.

Coinbase became one of the largest crypto companies in the world. Still, its stock max drawdown within a year was 57%.

A successful company does not always mean it'll go up only after IPO.

That is the part retail investors usually ignore during highly hyped listings and become the exit liquidity.

[Hedgie @HedgieMarkets]

🦔UC Berkeley's computer science department just posted its worst failure rates in years. 35.3% of CS 10 students got F's in spring 2026, up from under 10% in prior semesters. Professor Dan Garcia says the primary driver is a "vast increase in academic dishonesty" through LLMs. Students use AI to complete assignments, never learn the material, then fail exams. His office hours, once full, are now empty.

My Take
Companies are firing experienced engineers while the pipeline that produces new ones is being gutted by the same technology. Students use AI to bypass the hard part of learning, show up to exams without the understanding, and fail. One professor discovered a student's linear algebra class had an "open AI" policy for homework and exams. That student then couldn't do basic linear algebra in the next course.

Both ends of the workforce are eroding at the same time. Senior engineers are getting cut to fund AI spending. Junior engineers are graduating without the skills because AI did their coursework. And the companies spending trillions on these tools haven't connected those two facts yet.

Hedgie🤗

[Michael | Hypermarkets @itsmichaelluu]

TIER 1 — THE IRREPLACEABLE (Core Bottlenecks)

#1 $NVDA — The entire AI compute stack runs through NVDA. Every hyperscaler, every neocloud. NVDA remains the clearest monetization point for AI infrastructure spending — the GPU, networking, and software ecosystem still pull demand toward one platform. Nothing replaces it.

#2 $MU — The tightest supply constraint in the stack right now. Micron is the only U.S. producer of high-bandwidth memory and the fastest-growing of the three global suppliers. Its 2026 HBM output was already sold out by December 2025. Every AI accelerator built by Nvidia, AMD, or the hyperscalers needs HBM. HBM3E → HBM4 transition is a massive tailwind.

#3 $NOW — The AI software layer that runs on top of all this hardware. Enterprise AI orchestration, agentic workflows, and automation at scale. Premium valuation is justified by best-in-class platform stickiness and expanding AI product suite.

#4 $AVGO (Broadcom) — Custom AI silicon (XPUs) for Google, Meta, and others. Broadcom captures custom AI silicon and networking spend and converts AI infrastructure demand into large, durable cash-flow streams.

#5 $TSM (TSMC) — Manufactures every chip that matters. NVDA, AMD, and the hyperscalers all run through TSMC — it covers the chip manufacturing layer of the full AI stack.