To The Moon

If I already owned Micron, I would hold, but trim gradually rather than exit outright. The bullish case remains intact: HBM demand is still supply-constrained. AI servers require far more memory than traditional servers. Memory has evolved from a commodity into a strategic AI bottleneck. Micron is one of the few companies capable of supplying leading-edge HBM at scale. However, several warning signs are emerging: A trillion-dollar valuation leaves less room for execution mistakes. Expectations are becoming extremely optimistic. Capacity expansions from competitors could eventually reduce scarcity premiums. "AI stock outperforming NVIDIA" narratives often appear late in a cycle. History shows that semiconductor leaders rarely move in straight lines. Even during strong secular upcycles, 20-3

The Pulse Stop. Look at what just happened in the space sector and tell me this is normal. $Rocket Lab USA, Inc.(RKLB)$ — down 14.70% in a single session. One of the most operationally credible space companies on the market, with a real rocket that actually launches, a real customer base, and a real path to profitability. Down almost 15% in one day. $Virgin Galactic(SPCE)$ — up 21.68% in the SAME session. A company that has been functionally dormant, burned through capital, and hasn't run a commercial flight program in years. Up over 21%. This is not fundamentals. This is capital rotation in its most brutal, institutional form — and the catalyst is one that every space investor needs to understand before

I’m leaning toward $ARM Holdings(ARM)$ in this CPU war. Its business model is the most attractive because it benefits no matter who wins. Whether it’s $NVIDIA(NVDA)$ , $Advanced Micro Devices(AMD)$ , or hyperscalers building Arm-based CPUs, ARM collects royalties without having to fight for market share directly. That certainty helps explain the stock’s strong reaction. NVIDIA is still the biggest wildcard. Vera may not replace x86 overnight, but within NVIDIA’s AI ecosystem it doesn’t need to. If customers are already buying NVL racks, adopting Vera becomes a natural extension. The market may also be underestimating how much CPU revenue is embedded inside those

$Hewlett Packard Enterprise(HPE)$ $Dell Technologies Inc.(DELL)$  $NVIDIA(NVDA)$  ⚡🤖📈 $HPE Hits FY28 Targets Two Years Early 📈🤖⚡ 💡 $HPE may have just delivered one of the most important AI infrastructure earnings reports of 2026. The stock has nearly doubled this year, yet the business appears to be accelerating even faster than the share price. $HPE posted its biggest earnings beat since 2018, achieved its FY28 financial commitments two years ahead of schedule, raised free cash flow guidance by 75%, and expanded AI backlog to record highs. Wall Street is now scrambling to catch up. 🔥 Analysts Are Repricing The Story Barclays maintained Overwei

$NVIDIA(NVDA)$ $Apple(AAPL)$  $Alphabet(GOOGL)$  🚨🧠📉 $NVDA Call Selling Surges While $AAPL Attracts Bullish Bets 📈🍎⚡ 💡 A clear divergence is emerging beneath the surface of Big Tech, where short-dated positioning is beginning to split across the Magnificent 7 in a way that suggests selective risk-taking rather than broad directional conviction. More than $19M worth of short-dated, single-leg calls have been sold on $NVDA today, with selling pressure accelerating as the session progressed. Since 10am alone, over $36M in short-dated call premium has reportedly been distributed on the sell side as momentum faded and price action reversed from in

$IBM(IBM)$ （定投項目） 量子電腦未來發展趨勢 量子電腦正從實驗室原理驗證加速邁向工程化與產業化，未來十年將沿著\*\*硬體規模化、錯誤校正實用化、專用機優先落地、量子與AI融合、網路互聯與安全升級\*\*六大主線推進，中長期朝通用容錯量子運算與全球量子網際網路發展。 \--- \## 一、硬體：多技術路線並行，規模與保真度同步提升 \- **超導量子**：仍是主流技術路線，量子位元數量持續突破（如IBM、Google），核心目標為提升\*\*閘保真度（逼近99.9999999%）\*\*與同調時間，降低運算錯誤率。 \- **光量子（中國優勢領域）**：具備室溫穩定、低損耗特性，「九章」系列不斷刷新光子纏結紀錄，產業化門檻低，預計3至5年內可縮減至桌上型電腦尺寸。 \- **中性原子／離子阱**：擴展性高、同調時間長，中性原子技術路線快速崛起（如中國「漢原」系列），目標在2027至2030年推出千位元等級專用量子電腦。 \- **矽基量子**：2026年已實現原子級精度全堆疊邏輯運算，未來將依託成熟半導體製程，實現低成本量產。 \## 二、錯誤校正：從NISQ過渡至容錯運算，邁向實用化關鍵突破 \- **短期（2025-2030）**：量子錯誤校正走向規模化，邏輯量子位元突破10個，表面碼等新型校正碼降低實體量子位元消耗，推動NISQ（雜訊中型量子）電腦於特定領域商業化應用。 \- **中期（2030-2040）**：\*\*通用容錯量子電腦\*\*問世，數千個邏輯量子位元可穩定運行，能夠執行Shor演算法等複雜運算任務，徹底重塑密碼安全與運算算力格局。 \## 三、應用：專用場域優先發展，2025-2030年迎來爆發式落地 \- **醫藥領域**：透過分子模擬加速新藥

For the past three years, investors have been obsessed with one question: who will build the fastest AI chip? I think the more important question is this: who gets paid every time those chips need to talk to one another? That distinction sits at the heart of Marvell Technology's transformation. Following Jensen Huang’s public declaration that Marvell could become the next trillion-dollar company, investors suddenly began reassessing a business they had long treated as peripheral to the AI story. The reaction has been violent. But I suspect many are still using the wrong mental model. They see a semiconductor supplier. I increasingly see an infrastructure business whose value rises with complexity itself. The network may become more valuable than the nodes Complexity Is Becoming the Product

Tesla Is Down 15%+ Since December. What Its Chart Says Here Tesla has fallen more than 15% since hitting an all-time high in December even as the electric-vehicle and energy-storage giant beat analysts' revenue and earnings estimates for its latest quarter. What does TSLA's fundamental and technical analysis say? Tesla's Fundamental Analysis $Tesla Motors(TSLA)$   released fiscal Q1 results back on April 22, reporting $0.41 in adjusted earnings per share on $22.4 billion of revenue. The firm's top and bottom lines both beat analyst estimates, with revenues up 15.8% year over year and adjusted EPS growing 51.9% from Q1 2025. Gross profit likewise rose 50% y/y to $4.72 billion, while gross margin grew 478 bas

$MasterCard(MA)$ 五十二周低位 AI 時代打工仔求生術--八公精神與懶人智慧 1. 八公，從前說人「八公」，意思是你多事、八卦、愛管閒事。 來到AI時代，這反而成了一種讚美。 為甚麼？ 因為你若不「諸事八卦」，就永遠不會知道： 原來會計部可以用AI對帳、市場部可以用AI寫文案、人事部可以用AI篩履歷。 八公，就是那個願意到處看一眼、問一句的人。 2. 練精學懶，是生存智慧 「練精學懶」向來不是好話，但在AI年代，它是一門技術。 所謂懶，不是甚麼都不做，而是： 把重複、低效能的事交給AI，自己只做決策與創造。 寫電郵？AI代勞。做簡報？AI草擬。整理會議紀錄？AI摘要。 你剩下來的時間，用來思考、質疑、串聯、發問—— 這些才是AI做不到的事。 3. 生安白造，變成創造力 「無中生有」曾經是罵人胡說八道。 現在，它幾乎是創新的同義詞。 AI給你的往往是粗糙的、半成品的、甚至是錯的。 你的工作，就是從那些「不太對」的內容中，提煉出「很有道理」的東西。 這不是憑空亂說，而是用見識與判斷力，把碎片拼成價值。 4. 為甚麼AI不是泡沫？ 有人把今天的AI比作2000年的科網泡沫。 這個比喻，表面像，實質不像。 泡沫的特點是：概念先行，落地為零。 當年不少.com公司，掛個招牌、講個故事就能上市，但沒有收入、沒有產品、沒有用戶。 今天的AI，有模型、有算力、有企業應用、有成本節省。 它不是「吹勢」，而是「趨勢」。 5. 巨輪不會等你 AI這股浪潮，不會因為你質疑它而停下來。 它一邊逼人進修，一邊吞噬低效能的崗位。 問題不是「AI會不會取代我」，而是： 我夠不夠八公去發現新工具？ 我夠不夠懶去把瑣事外判？ 6. 最後一句 做一個練精學懶的八公， 不是投機，而是自知。 自知人力有限，所

🚀 Stock Dropped After Earnings: Is Shopify a Bargain or a Value Trap? 🛍️ $Shopify(SHOP)$   The recent market dip has left many investors wondering: Is Shopify still worth buying at around US$117, or is the e-commerce boom officially over? While the headlines looked scary, the actual data suggests the market might be overreacting. Here is a quick, layman breakdown of why Shopify could be a coiled spring ready to give investors a solid upside. 📉 Why Did the Stock Drop? (The Core Disconnect) Shopify’s recent earnings report wasn't actually "bad." Look at the Q1 2026 numbers: • Total Revenue: $3.17 Billion (Up a massive 34% year-over-year) • Free Cash Flow (FCF): $476 Million (A healthy 15% margin) The Problem? Wall