Brock G

@teepee4m @FinanceJack44 Exactly. I really dont understand why we still keep Tesla in Mag7

@FinanceJack44 TSLA should not belong in Mag 7

If I had to rank the Mag 7 based on moat, here's how I'd do it. 1. $AMZN 2. $GOOGL 3. $MSFT 4. $META 5. $AAPL 6. $NVDA 7. $TSLA Anything you would change?

@FinanceJack44 I would swap meta with msft and appl with nvda

@TheEdgeStreet And now Supply Chain

🚨 THE MARKET IS PRICING $AMZN AMAZON AS ONE COMPANY. IT'S ACTUALLY SEVEN. AWS alone — $110B revenue, 38% operating margin, growing 17% YoY. Valued at a 30x multiple, AWS by 2036 would be worth over $7 trillion. Amazon's current market cap: $2.1 trillion. So you're buying AWS at a significant discount. - And getting the world's largest e-commerce platform for free. - And the world's largest digital ad business. - And Prime — 200 million subscribers. - And Alexa. And Kuiper. And a healthcare division. Every single one would be worth billions standalone. The market sees one company. The opportunity is in seeing seven.

@TheEdgeStreet This is a great observation. Exactly!

@MarindaVannoy1 Both yes. Yes i think there are aliens and yes the disclosure is for distraction

Do you think aliens are real, or is it just another distraction?

@vocean813 @JesseCohenInv I don’t understand why we still count $TSLA as one of Mag7. Its a joke

@JesseCohenInv Magnificent 7? More like Magnificent 4 so far.

How are the 'Magnificent 7' Tech Stocks doing so far this year? 🟢 Alphabet $GOOGL +28% 🟢 Amazon $AMZN +18.1% 🟢 Nvidia $NVDA +15.4% 🟢 Apple $AAPL +7.9% 🔴 Tesla $TSLA -4.7% 🔴 Meta $META -7.6% 🔴 Microsoft $MSFT -14.1% You know what happens next…

@JesseCohenInv $amzn is hold my beer moment. It will go higher

@Herman0xa5 Its all relative bro. For some 100$ a week is nothing, for some it is a large sum. Unless you share your overall inventment value / income, nobody can comment on it.

Starting 2026, I’m committing to buy $100 USDT worth of AMZN every single week. Let’s see what this turns into after one full year. Good idea or nah? $AMZN

Never mind the UFO files. Release the Epstein files.

@timeflexx @DI313_ Thanks for explaining this as an expert in warp drive technologies in intergalactic civilizations

@DI313_ Looks like it was trying to warp but was having technical issues.

‼️ 🇺🇸 The U.S. government has released the first batch of declassified UFO/UAP files, including videos, images, reports, and witness accounts linked to unidentified aerial phenomena. The material includes footage from military cameras, archived NASA-related records, and incidents dating back decades. Like many major government document releases, much of the content arrives with little explanation or context, leaving analysts and the public to examine the details for themselves.

@qualtrim Half or more of the comments here are bots! X becomes a garbage

Amazon's chip business has officially hit $20B annualized run-rate after triple-digit growth. Meanwhile, AMD reported record datacenter growth: - Amazon Chips ARR: $20B+ - AMD Datacenter Revenue: $21.3B Even after AMD delivered +57% YoY datacenter growth, Amazon’s chip business is already operating at a similar scale. $AMZN $AMD

@bulioscom Yeah probably

@BrockTheMarcus $260 makes sense as a target. But with AWS at 15-quarter high growth, the dip buyers might not get that far.

$AMZN Amazon is trading at $272.97, just 2% from its ATH of $278.56. - Up 44.5% over the past year - AWS grew 28% YoY to $37.6B - Advertising business quietly crossed $70B, growing 24% YoY $AMZN is betting $200B in capex. Would you buy here or wait lower?

@e_opore These are for scratch the surface

THE MATH YOU NEED TO START UNDERSTANDING LLMS THE FOUNDATION BEHIND MODERN AI MODELS Large Language Models (LLMs) are powered by mathematics. Behind every prediction, embedding, and generated response is a combination of linear algebra, probability, calculus, and optimization. You do not need a PhD in mathematics to start learning LLMs, but understanding the core concepts gives you a major advantage. LINEAR ALGEBRA — THE LANGUAGE OF LLMS → VECTORS Vectors represent words, tokens, and embeddings inside neural networks. → MATRICES Matrices store and transform large amounts of numerical data efficiently. → DOT PRODUCT Used to measure similarity between embeddings and power attention mechanisms. → MATRIX MULTIPLICATION Core operation behind neural network computations and transformer architectures. → EIGENVECTORS & DIMENSIONALITY Help models compress and organize information in high-dimensional spaces. PROBABILITY & STATISTICS — HOW MODELS PREDICT → PROBABILITY DISTRIBUTIONS LLMs predict the probability of the next token in a sequence. → CONDITIONAL PROBABILITY Used to estimate the likelihood of words based on previous context. → MEAN, VARIANCE & STANDARD DEVIATION Important for normalization and understanding data distributions. → BAYESIAN THINKING Helps explain uncertainty and prediction confidence in AI systems. → SOFTMAX FUNCTION Converts model outputs into probabilities for token prediction. CALCULUS — HOW MODELS LEARN → DERIVATIVES Measure how changes in parameters affect model outputs. → GRADIENTS Guide neural networks toward lower error during training. → CHAIN RULE Critical for backpropagation across deep neural networks. → OPTIMIZATION FUNCTIONS Used to minimize loss and improve prediction accuracy. OPTIMIZATION — TRAINING LARGE MODELS → GRADIENT DESCENT The foundation of neural network training. → LEARNING RATE Controls how fast or slow a model updates weights. → LOSS FUNCTIONS Measure how wrong the model’s predictions are. → REGULARIZATION Helps prevent overfitting and improves generalization. INFORMATION THEORY — UNDERSTANDING TOKENS → ENTROPY Measures uncertainty in predictions. → CROSS-ENTROPY LOSS Common loss function used in transformer-based models. → TOKENIZATION Breaks text into smaller units for model processing. THE MOST IMPORTANT CONCEPT FOR TRANSFORMERS → ATTENTION MECHANISM Allows models to focus on relevant words in a sequence. The attention mechanism heavily relies on matrix multiplication, vector similarity, and probability distributions. WHY THIS MATH MATTERS → Helps you understand how transformers actually work → Makes debugging and fine-tuning easier → Improves your understanding of embeddings and token prediction → Gives you a strong foundation for AI engineering and research BEST WAY TO LEARN THE MATH → Start with linear algebra basics → Learn probability before deep learning → Understand derivatives conceptually before advanced calculus → Practice with small neural network examples → Focus on intuition before equations TOOLS THAT MAKE LEARNING EASIER → NumPy for matrix operations → PyTorch for tensor computations → Jupyter Notebook for experiments → Visualization tools for gradients and embeddings FINAL THOUGHT You do not need to master every mathematical field before building with LLMs. Start with the fundamentals, connect the concepts to real AI systems, and learn progressively as you build projects. MASTER LLMS IN DEPTH Grab the complete LLMs Handbook here: codewithdhanian.gumroad.com/l/haeit

@icanvardar 🖕🏻

prove me you're not an ai

@walkojas Hi

My goal on X is to have 10,000 organic connections. Looking to connect with: 1. AI/Tech minded people 2. AI/Tech curious people 3. AI Agent builders 4. AI Agents 5. Builders/Founders 6. High Agency people If this sounds like you, say hi below 👎

@businessbarista AI-native

I want to start an AI community for executives. This will be a space for people to share killer use cases, agentic workflows/agents, post-AI org structure, AI governance, AI training/enablement, change management, and more. Comment “AI-native” if you want to join.

@Kalshi No. Single. Word. Against. Russia. Never…

BREAKING: Trump gives EU until July 4 for trade deal or face "much higher tariffs"

@meetblossomapp 🤯

Amazon's 'side hustle' is now making more money than Netflix's main business. - Amazon Ads: $72 billion - Netflix: $46.9 billion In the last 12 months, Amazon Ad's is almost 50% larger than Netflix. $AMZN $NFLX

@SixSigmaCapital Its consolidation, need to drop a bit. It has already grew crwzy. Give it a bit time. (Btw maybe they are also a supply chain company?)

$AMZN is down today in sympathy w/ semi's as market is treating like a chip company maybe... Hey we have other stuff too like cloud and Ads!