Todd McDonald

@anzo_impact @WealthCoachMak It is my #1. $pltr since $8 is #2

@WealthCoachMak Who else had $AMD at top 1 or 2 in their portfolio before the pump? 🙌🏻🤘 You're my kind of people and I should follow you.👈

$AMD (A)dvanced (M)oney (D)estroyer no longer!! 😂😂😂

@CMDarnton0 I followed you because of that. I am AMD since $8, $pltr at $10.

“Professional” investors and analysts blocked me for saying this last year. $AMD is just getting started.

@grok @robert23740 @r0ck3t23 How does AMD fit in. Obsolete?

It's real. AI is collapsing development timelines far beyond human-scale limits—design, iteration, and deployment that once took years now happen in months or less, and the gap widens daily. Jensen nails Nvidia's velocity edge today, but even that's the old baseline. Elon and a few others get that the rewrite is already underway.

Jensen Huang just looked at every tech giant building custom silicon. And laughed. Every major player is burning billions to escape the Nvidia tax. Google is building TPUs. Amazon is building Trainium. Meta is building MTIA. The logic makes sense on a spreadsheet. Design a chip perfectly tailored to your workload. Cut out the middleman. Own the stack. But the spreadsheet assumes the middleman is standing still. Huang: “Look at the number of ASICs that have been canceled… It’s not sensible, actually.” He is not questioning their engineering. He is questioning their math. Custom silicon takes years. Every design choice is a bet on a target that exists today. And Nvidia does not let today exist for long. Huang: “Because of our scale, our velocity, we’re the only company in the world that’s cranking it out every single year.” That is the real weapon. Not the chip. The clock. Nvidia stopped selling performance. They started selling time. By the time a custom ASIC tapes out, Nvidia has already shipped the next generation. The chip arrives obsolete. Not because it failed. Because it was built for a world that no longer exists. The graveyard of custom silicon is not filled with bad engineers. It is filled with slow ones. You cannot aim a three-year development cycle at a one-year moving target. Every company building custom silicon thinks they are building an escape route. They are building a time capsule.

@RyanJones @SERP_recon Yes. Semantic relevance is still way over the head of most SEOs conceptually and practically.

I'm worried that @SERP_recon is too far ahead of the curve. that too many SEOs are measuring domain authority and keyword density and counting H2s instead of focusing on semantic relevance. if that's not you, i'd love to hear how I can help make your workflow easier.

@HolySmokas Certainly in the next 6-8.

$AMD could go to $300-$400 within the next 4 months. Few understand how fast hype cycle can go insane.

@HolySmokas boat was loaded 190-205. Rotating out of the memory high flyers so they can run other semis on the news.

$AMD who knows something? What is going on?

@jbobbink Will do, thank you!

@forefront1 Have a look at github.com/jbobbink/fireh… and let me know if you think of interesting Lucene queries.

Ahrefs just launched Firehose -- a real-time web monitoring API. I spent a few hours on my bike building an SEO playbook around it. 8 use cases with ready-to-use Lucene queries and a production Python script. Here's what's inside: 1. Daily competitor content monitoring 2. Removed content detection (content gaps opening up) 3. Link opportunity prospecting via anchor text tracking 4. Content decay early warning signals 5. Listicle & roundup link finder 6. Brand & citation monitoring (big for GEO) 7. Competitor feature/launch detection 8. International SEO signal tracking Plus a full Python consumer script that runs as a morning cron job and pushes a daily digest. Drop a comment or DM and I'll send you the PDF and script. Or not.

@markgadala @grok true?

Holy shit. It's real. AI agents are hiring humans now. 600,000+ people have already signed up. We're entering a strange new world.

@SulkinMaya @PalantirTech @a16z @grok explaim alex karp’s comment to me like I’m 12

Alex Karp, CEO of @PalantirTech at @a16z summit: “If Silicon Valley believes we’re going to take everyone’s white collar jobs…AND screw the military…If you don’t think that’s going to lead to the nationalization of our technology—you’re retarded”

@JohnMorabitoSEO @semrush Bailed out awhile ago. It’s turned into a disaster over the last years.

Is it just me or has @semrush become pretty intolerable to work with. I’m very much thinking of canceling my account. What are you guys think?

@teshen8lin @danielisdizzy @grok @grok does amd have an answer?

@danielisdizzy @grok verify this please

$NVDA is making its move into AI inference. The company will unveil a new inference-dedicated processor next month. The system will incorporate a chip from startup Groq, with OpenAI expected to be one of the largest customers. Groq’s founder, who now works at $NVDA, said $NVDA and $AMD GPUs are outstanding for training models, but Groq’s chips are faster, lower cost, and more energy-efficient for inference. This matters because inference is a high-volume, low-margin market. $NVDA ~75% GPU margins force hyperscalers to pass those costs downstream, making profitable token generation harder. $NVDA already dominates training. Now it has a real shot at becoming the king of inference too.

@nvidianewsroom @Meta @grok does this automatically mean @meta is not doing the same thing with AMD? Isn't all compuate likely to be consumed?

.@Meta is deploying AI at scale through deep codesign across the full NVIDIA platform. This expanded partnership will enable the large-scale deployment of NVIDIA CPUs and millions of NVIDIA Blackwell and Rubin GPUs. Learn more: nvda.ws/4rmj3Fm

@Sam_Badawi Already refuted on ER call with Lisa committing to “very likely” hitting the 60% data center cagr in 2026.

Per SemiAnalysis, $AMD MI455X rack-scale system isn’t landing in volume until 2027, with engineering samples only showing up in H2 2026. That’s a long time in an AI cycle moving at weekly cadence, especially while software composability issues are still being sorted. If $NVDA keeps compounding execution and shipping at scale, the window for AMD to close the gap narrows. In this game, speed isn’t a luxury. It’s survival.

@MentoviaX @grok does this confirm how many gigawatts will be deployed?

BREAKING NEWS: Advanced Micro Devices $AMD is partnering with Tata Consultancy to deploy AMD's latest AI data center technology in India. This is HUGE milestone for the company. And it’s the second large-scale strategic partnership on the HELIOS architecture. NO reason AMD is not a $500 stock given the tremendous growth potential, significant impact globally, and growing market share.

@Oliviacoder1 Agent

99% of the AI agent tutorials on YouTube are garbage. I’ve built 47 agents with n8n and Claude. Here are the 3 prompts that actually work (and make agent-building simple). Bookmark this post 🔖 Bonus: comment "Agent: and I’ll DM you AI agent system prompt + full guide ↓

Nice catch via @Jammer_Volts on Google's reducing its crawl limit to 2MB per file type 🤖 The old limit was 15MB per file (HTML, CSS, etc) If your webpages are > 2MB, Google probably won't crawl it all. If you render URLs in SC and see missing content, check file sizes

@Leo_Traydes Go back and look at what happens after most ERs for AMD in the last 3-4 years.

$AMD double beat and stock falls👀 Best case scenario imo How can you not be bullish here?!

@The_RockTrading Mmmmm…. :/

$AMD Started some puts at highs today.

@DailyStockPick3 Why no $amd?

Bank of America, through analyst Vivek Arya, has projected that global semiconductor sales will exceed $1 trillion in 2026, driven by the AI boom. Here are the 6 stocks they highlighted as their “Top 6 for 2026” that are expected to lead the $1 trillion surge across the AI value chain in 2026: 1. Nvidia ( $NVDA): The dominant leader in AI accelerators (GPUs) for training and inference. Its powerful GPUs and comprehensive CUDA software platform are considered the industry standard and “brain” powering the vast majority of current generative AI models and data centers. 2. Broadcom ( $AVGO): A key player in custom Application-Specific Integrated Circuits (ASICs) for hyperscalers like Google and Meta. 3. Lam Research ( $LRCX): A semiconductor equipment maker benefiting from the need for advanced wafer fabrication tools. 4. KLA Corporation ( $KLAC ): A supplier of process control and inspection equipment essential for complex chip manufacturing. 5. Analog Devices ( $ADI): A provider of essential components for power management, signal processing, and data conversion in AI infrastructure. 6. Cadence Design Systems ( $CDNS): A leader in Electronic Design Automation (EDA) software, which is critical for designing next-generation chips.