Every so often you meet a founder so sharp that every conversation lights up new pathways in your thinking and quickly deepens your grasp of their business and technology. Working with people like that is a privilege.

Everything Starship has done so far, in 30 seconds. Hot staging. Surviving reentry. Soft splashdowns. The first tower catch. Payloads deployed from orbit. Next up: Flight 14, the first orbital attempt. 🚀

everytime timeline glazes me it ends up becoming 10x more hate a few weeks later just saying this now so i can retweet it later im not your friend, my only goal is to make the most money onchain for myself so i can eventually exit and put the money into things i care about

Everyone wants to know which of the Magnificent Seven AI kills. I think that’s the wrong question. None of them need to disappear. IBM didn’t disappear. Intel didn’t disappear. They just stopped being the scarce asset in the next computing cycle. AI has three layers that matter: Who owns the intelligence: models and data. Who owns the factory: chips, power and cloud. Who owns the last mile: the device, workplace, feed or agent where demand starts. The obvious conclusion is that Apple and Microsoft have more to defend than most people think. Apple owns the device but rents much of the intelligence. Microsoft owns enormous enterprise distribution, but agents are coming directly for the software seat. But there’s another side to this that I think matters even more. The AI infrastructure trade isn’t seven different bets if they all depend on the same customer paying the bill. NVIDIA, Broadcom, TSMC, the clouds and the AI labs can look diversified on a screen while ultimately depending on the same AI capex cycle. Picks and shovels are only safe when the miners can pay. That was one of the lessons of 2000. Equipment demand looked enormous until the financing underneath the customers broke. I don’t think AI is 2000. The demand is real and the technology is real. But the financing still matters. So I’ve stopped asking only which company wins AI. I’m asking which companies still compound if the AI capex cycle slows for two years. That gives me a very different list.

Every year tens of millions of young Indians compete in what are probably the world’s toughest exams. Register for free to learn why the country’s schools fail to set them up for success Photo: Getty Images

every good coin goes through volatility, euphoric highs and corrections/retraces, it's healthy with it, of course comes all kinds of emotions, that's trading it's always good to zoom out and see if you still have conviction long term and if the vision is still in play if yes, continue to hold, buy more if no, take profit/cut of course, we all look at the chart, it's a part of it, but zoom out the game we love

Everyone fails. Anyone you see succeeding is only succeeding at the things you're paying attention to--I guarantee they are also failing at lots of other things. The people I respect most are those who fail well. I respect them even more than those who succeed. #principleoftheday#

Everyone on planet Earth is talking about local AI right now And for good reason Governments are banning models. Hardware prices are 10xing You NEED to be getting into local AI. The number 1 questions everyone has though is which computer to buy? Here's your answer: You basically have 3 options: 1. MAC STUDIO (high intelligence, lower speeds)- Mac Studios are excellent devices for local AI. They can run MASSIVE models. I'm running GLM 5.2 right now on a single Mac Studio. The model is Opus 4.8 level The issue is, Mac Studios are a bit slower at running intelligence (altho the M5 Ultra is promising) Mac Studios are a good choice for you if you want frontier level intelligence, but are fine running the intelligence passively Meaning you get top intelligence, but it runs more in the background rather than on demand As an example, I have GLM 5.2 running security checks on my codebase every hour. It creates a report. I review this later in the day 2. POWERHOUSE NVIDIA CHIPS (RTX 5090, 6000 Pro) Nvidia is the most valuable company in the world, and for good reason They make the world's best GPUs. They have decent VRAM (32gb on the 5090, 96gb on the 6000 Pro) and INSANE bandwidth. Meaning the local models run at unbelievable speeds I'm running Qwen 3.8 locally on a 5090 and it's just as fast as cloud models I'd go this route if you want to run an AI agent like Hermes off a local model, still get decent intelligence, but have it able to work lightning fast 3. AI WORKSTATIONS (DGX Spark type computers) The DGX Spark is an excellent AI computer It has high memory (128gb unified memory) and has decent speeds because of the Nvidia CUDA architecture It is basically the sweet spot between a cutting edge Nvidia chip and a Mac Studio You can run medium sized models, and get usable speeds out of them You're not going to get the same performance as cloud models, but it will allow you to offload small secondary tasks to your local models for them to handle They are also the absolute easiest to get up and running You plug it in, then tell your agent on your main computer to go onto it and set it up. You don't even need it connected to a monitor CONCLUSION Here's what it comes down to: how high intelligence do you need, what speeds do you need, and how plug and play do you want? Want the highest speeds, like you are used to with cloud compute? Build a computer around an RTX 5090 Want to run frontier level intelligence, and don't mind slower speeds, go with a Mac Studio Either way, it's never been more important to get into local AI