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🚨 WARNING: TOMORROW WILL BE THE WORST DAY OF 2026!! In 24 hours, SpaceX goes public at a $1.75 TRILLION valuation - the biggest IPO in history. I've been trading for 10+ years, and I've never seen the financial system bend its own rules for ONE private company. Nasdaq. MSCI. America's largest brokerages. All changing long-standing rules for a single company. That doesn't happen by coincidence. Let me tell you what's really going on: First, Fidelity slashed its minimum account requirement from $500,000 to just $2,000. A 99.6% reduction. Think about that for a second. One of Wall Street's most exclusive gates was suddenly opened to millions of everyday investors - right before the biggest market debut of all time. Why do they suddenly want YOU involved? Because someone needs buyers. SpaceX set aside 30% of the offering for retail investors. That's THREE TIMES the typical allocation. And despite that, many investors still received only partial allocations. Which means anyone wanting more shares will be chasing them when trading begins. To do that, they're selling other positions TODAY to raise cash. That's one side of the selling pressure you're seeing. The other? Institutional money positioning ahead of July. Here's the part most people are missing: SpaceX won't enter the Nasdaq 100 immediately. It gets added 15 days later. Why? Because Nasdaq shortened its own waiting period from 3 months to just 15 days. Specifically for this event. The second SpaceX joins the index, every fund is REQUIRED to buy shares. That's an estimated $22–27 billion of automatic demand. The big funds are selling assets now to build cash reserves. Retail is selling. Institutions are selling. Both at the same time. THAT is what's driving this selloff. Now for the part nobody wants to say publicly: When the most powerful money managers in the world create a $1.75 trillion liquidity event and invite the smallest investors to participate at the last minute... That's not generosity. That's distribution. We've watched this play out before: → Dot-com bubble (2000) → COVID crash (2020) Insiders exit at extreme valuations. The crowd rushes in chasing momentum. Something doesn't add up. So over the next 24 hours, you have two options: Buy into the most expensive IPO ever at the opening bell... Or dig into the prospectus and consider the possibility that YOU are the liquidity event. The next few days are going to be wild. I've publicly called some of the biggest tops and bottoms of the past decade. And I'll call this one too. I’ve spent decades studying markets, and I’ve called most major tops and bottoms along the way. And I’ll call it again in 2026. Follow me and turn notifications on before it’s too late. Don’t become exit liquidity.

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0:13

A narcissist can fake quite a lot. They can fake empathy. They can imitate affection. They can even put on such a convincing performance of being supportive that, at first glance, you’d swear they meant it. What they struggle to conceal is how they react when something goes well for someone else. Pay attention the next time you have a genuine win. Not the grand victories. The promotion. The successful project. The new relationship. The small moments are often more revealing. The achievement you’re proud of. The piece of good news you’d naturally expect another person to be pleased about. Watch closely. The slight eye roll. The forced smile. The curious inability to simply say, “Well done.” Or the immediate qualification. “That’s nice, but…” An impressive phrase, really. Two words capable of draining the joy from almost any accomplishment. Then there’s the remarkable talent for making your success about them. You share something you’ve achieved and, within moments, you’re listening to a story about something they did years ago that was apparently bigger, harder, better, or more important. Your moment lasts roughly thirty seconds before being requisitioned for their use. My personal favourite is the forensic examination of your achievement. You receive praise; they begin a search for defects. Not because the flaws matter, but because your confidence does. And then, of course, there’s silence. No congratulations. No enthusiasm. No acknowledgment. Just a noticeable drop in temperature, as though your success has somehow inconvenienced them. The lesson is simple. People who are genuinely comfortable in themselves do not find your success threatening. They are capable of applauding without competing, congratulating without comparing, and celebrating without keeping score. Those who cannot manage that are telling you something useful about themselves. Believe them… BELIEVE THEM!!! A narcissist often views another person’s success as a reduction in their own importance. It’s an odd way to move through life, but it does have one advantage for the rest of us: It makes them surprisingly easy to spot.😉💙🎩

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I think that fast strides are the most powerful training stimulus for runners. For a glimpse at the physiology, I want to talk about my all-time favorite study. If we were playing FMK, I’d F this study, M this study, and K any study that is not this study. Published in 2018 in Physiological Reports, it had 20 trained athletes (14 men and 6 women) complete a 40-day intervention with 10 sessions involving 5-10 x 30 seconds with 3.5 minutes recovery, in conjunction with a 36% reduction in weekly volume. Before and after the intervention, participants did a time-to-exhaustion test, two 10km tests (one with glycogen depletion) preceded by 2 x 6 minute intervals at 60% of VO2 max, and a series of muscle biopsy and blood tests. Running economy during the 10k improved by 2.1% and during the intervals at 60% of VO2 max by 1.7%. In other words, the athletes improved at both hard efforts and easy ones. Meanwhile, there was no running economy change in the glycogen-depletion 10k, leading the authors to theorize that improvements were primarily related to changes in slow-twitch muscle fibers. Expression of protein dystrophin increased, which may enhance the ST-fiber structural integrity via the actin-myosin cross-bridge, improving running economy. In addition, the expression of UCP3 decreased, which may improve mitochondrial efficiency. In fast-twitch fibers, expression of the muscle protein SERCA1 decreased by 22%, which could reduce the energy turnover during exercise. Enzymatic activity and testosterone:cortisol ratio also went up. Don’t be discouraged, I only kinda know what a few of those words mean. The point is that as researchers introduce stride-like speed sessions and measure more variables, they often see major changes that may explain why Olympians are always doing strides. So when we use the term “running economy,” we’re actually incorporating dozens of these physiological variables, some of which we can measure and some of which we can’t, from the cells to the muscles to the circulatory and cardiovascular systems. Put it together, and getting faster is a wonderfully complex algorithm. Strides are one way to help solve it 💪
0:11

I think that fast strides are the most powerful training stimulus for runners. For a glimpse at the physiology, I want to talk about my all-time favorite study. If we were playing FMK, I’d F this study, M this study, and K any study that is not this study. Published in 2018 in Physiological Reports, it had 20 trained athletes (14 men and 6 women) complete a 40-day intervention with 10 sessions involving 5-10 x 30 seconds with 3.5 minutes recovery, in conjunction with a 36% reduction in weekly volume. Before and after the intervention, participants did a time-to-exhaustion test, two 10km tests (one with glycogen depletion) preceded by 2 x 6 minute intervals at 60% of VO2 max, and a series of muscle biopsy and blood tests. Running economy during the 10k improved by 2.1% and during the intervals at 60% of VO2 max by 1.7%. In other words, the athletes improved at both hard efforts and easy ones. Meanwhile, there was no running economy change in the glycogen-depletion 10k, leading the authors to theorize that improvements were primarily related to changes in slow-twitch muscle fibers. Expression of protein dystrophin increased, which may enhance the ST-fiber structural integrity via the actin-myosin cross-bridge, improving running economy. In addition, the expression of UCP3 decreased, which may improve mitochondrial efficiency. In fast-twitch fibers, expression of the muscle protein SERCA1 decreased by 22%, which could reduce the energy turnover during exercise. Enzymatic activity and testosterone:cortisol ratio also went up. Don’t be discouraged, I only kinda know what a few of those words mean. The point is that as researchers introduce stride-like speed sessions and measure more variables, they often see major changes that may explain why Olympians are always doing strides. So when we use the term “running economy,” we’re actually incorporating dozens of these physiological variables, some of which we can measure and some of which we can’t, from the cells to the muscles to the circulatory and cardiovascular systems. Put it together, and getting faster is a wonderfully complex algorithm. Strides are one way to help solve it 💪

David Roche

73,626 görüntüleme • 2 yıl önce

🚨 Anthropic committed up to 1M TPU chips for Claude. Openai is leasing TPUs for chatgpt inference. Here's How kernels work on TPUs (deep dive 2/6 by emilio andere) pallas is Google's answer to kernel writing. a python kernel SDK built on JAX. still very experimental (jax.experimental.pallas). on TPU it compiles through mosaic; on GPU it lowers to triton. if you know CUDA, the syntax will feel familiar but the execution model is completely different. in CUDA, grid=(4,4) launches 16 blocks running simultaneously across SMs. in pallas, those 16 iterations run one after another in lexicographic order. no threads. no warps. no blocks. no occupancy tuning. a TPU is a sequential machine with a very wide vector register — more like a CPU than a GPU. performance comes from width: a 128x128 systolic array doing matmul and an 8x128 SIMD vector unit doing everything else. maximum parallelism on chip: 2, one per TensorCore in megacore mode. three concepts replace CUDA's thread/block/grid hierarchy. Refs are mutable memory references. because execution is sequential, each iteration safely accumulates without atomics. in CUDA you'd need atomics or a separate reduction pass. the memory model is also very different from NVIDIA's. zero hardware caches. VMEM is 32-128 MiB of software-managed scratchpad — 500-1000x larger than GPU shared memory per SM. all data must be explicitly DMA'd from HBM to VMEM before any computation touches it. four levels: HBM → VMEM → VREGs → MXU/VPU, plus SMEM for scalar control data. every byte of data movement is your responsibility. this is like CUDA shared memory except it's 500x bigger and there's no cache fallback. pipelining is mandatory. without double-buffering HBM→VMEM transfers, the MXU just stalls waiting for data. this is the single most important optimization on TPU. and because grid execution is sequential and deterministic, consecutive iterations that need the same input block skip the redundant HBM transfer automatically, impossible on GPU where block execution order is undefined. the compilation pipeline is unlike anything in this series: python → jaxpr → stableHLO → XLA HLO (71+ optimization passes) → LLO (78+ passes) → 322-bit VLIW bundles. the compiler packs instructions for scalar, vector, matrix, and DMA units into a single 322-bit word. everything in that bundle executes in parallel, with no runtime scheduling.
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🚨 Anthropic committed up to 1M TPU chips for Claude. Openai is leasing TPUs for chatgpt inference. Here's How kernels work on TPUs (deep dive 2/6 by emilio andere) pallas is Google's answer to kernel writing. a python kernel SDK built on JAX. still very experimental (jax.experimental.pallas). on TPU it compiles through mosaic; on GPU it lowers to triton. if you know CUDA, the syntax will feel familiar but the execution model is completely different. in CUDA, grid=(4,4) launches 16 blocks running simultaneously across SMs. in pallas, those 16 iterations run one after another in lexicographic order. no threads. no warps. no blocks. no occupancy tuning. a TPU is a sequential machine with a very wide vector register — more like a CPU than a GPU. performance comes from width: a 128x128 systolic array doing matmul and an 8x128 SIMD vector unit doing everything else. maximum parallelism on chip: 2, one per TensorCore in megacore mode. three concepts replace CUDA's thread/block/grid hierarchy. Refs are mutable memory references. because execution is sequential, each iteration safely accumulates without atomics. in CUDA you'd need atomics or a separate reduction pass. the memory model is also very different from NVIDIA's. zero hardware caches. VMEM is 32-128 MiB of software-managed scratchpad — 500-1000x larger than GPU shared memory per SM. all data must be explicitly DMA'd from HBM to VMEM before any computation touches it. four levels: HBM → VMEM → VREGs → MXU/VPU, plus SMEM for scalar control data. every byte of data movement is your responsibility. this is like CUDA shared memory except it's 500x bigger and there's no cache fallback. pipelining is mandatory. without double-buffering HBM→VMEM transfers, the MXU just stalls waiting for data. this is the single most important optimization on TPU. and because grid execution is sequential and deterministic, consecutive iterations that need the same input block skip the redundant HBM transfer automatically, impossible on GPU where block execution order is undefined. the compilation pipeline is unlike anything in this series: python → jaxpr → stableHLO → XLA HLO (71+ optimization passes) → LLO (78+ passes) → 322-bit VLIW bundles. the compiler packs instructions for scalar, vector, matrix, and DMA units into a single 322-bit word. everything in that bundle executes in parallel, with no runtime scheduling.

wafer

33,655 görüntüleme • 2 ay önce

InterLink’s Journey to the World’s Top 10 Most Accurate AI Models Artificial Intelligence has rapidly become the defining force of this decade powering breakthroughs across every industry. But while most projects chase trends, InterLink Labs 👤 + 🌐 has been quietly building something deeper: an AI ecosystem grounded in verified human intelligence. Long before AI captured global headlines, InterLink Labs 👤 + 🌐 had already begun its research and engineering efforts back in 2019, assembling a world-class team of engineers and researchers from Big Tech companies and top QS-ranked universities. Their vision was clear - to build a model that truly understands humans, not just data. Unlike traditional AI systems trained purely on digital information, InterLink Labs 👤 + 🌐’s Human-AI Model learns from verified human behavior across millions of Human Nodes. This unique layer of authentic, real-world human input gives InterLink’s AI an unprecedented advantage in trustworthiness, bias reduction, and contextual understanding. Beyond algorithmic optimization, InterLink Labs 👤 + 🌐’s R&D efforts are being scaled to an unprecedented level. The team operates over 100 NVIDIA H100 servers, processing massive volumes of verified behavioral data contributed by real Human Nodes across the world. This data - diverse, decentralized, and human-validated forms the foundation of a next-generation intelligence system designed to mirror real human reasoning patterns. At the same time, InterLink Labs 👤 + 🌐’s AI-powered Human Credit Score applies advanced machine learning to analyze authenticity, contribution, and reliability. Creating an ethical model of digital reputation and fairness. Aligned with National Institute of Standards and Technology (National Institute of Standards and Technology) evaluation standards, InterLink Labs 👤 + 🌐 now aims to achieve Top 10 accuracy globally among AI models. Competing with research teams from Samsung Electronics, Kakao, キヤノン株式会社 / Canon Inc., and other global giants, InterLink Labs 👤 + 🌐’s engineers continue to train, benchmark, and refine their architecture daily to reach world-class precision and consistency. But this is more than a technical race. It’s a human mission. Every verified user contributes to the world’s first Human-Powered Intelligence Network, where real people fuel the evolution of trustworthy AI. As InterLink Labs 👤 + 🌐 advances toward global National Institute of Standards and Technology recognition, one truth becomes clear: The future of intelligence won’t be artificial. It will be human-powered. #InterLink #ITLG #ITL
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InterLink’s Journey to the World’s Top 10 Most Accurate AI Models Artificial Intelligence has rapidly become the defining force of this decade powering breakthroughs across every industry. But while most projects chase trends, InterLink Labs 👤 + 🌐 has been quietly building something deeper: an AI ecosystem grounded in verified human intelligence. Long before AI captured global headlines, InterLink Labs 👤 + 🌐 had already begun its research and engineering efforts back in 2019, assembling a world-class team of engineers and researchers from Big Tech companies and top QS-ranked universities. Their vision was clear - to build a model that truly understands humans, not just data. Unlike traditional AI systems trained purely on digital information, InterLink Labs 👤 + 🌐’s Human-AI Model learns from verified human behavior across millions of Human Nodes. This unique layer of authentic, real-world human input gives InterLink’s AI an unprecedented advantage in trustworthiness, bias reduction, and contextual understanding. Beyond algorithmic optimization, InterLink Labs 👤 + 🌐’s R&D efforts are being scaled to an unprecedented level. The team operates over 100 NVIDIA H100 servers, processing massive volumes of verified behavioral data contributed by real Human Nodes across the world. This data - diverse, decentralized, and human-validated forms the foundation of a next-generation intelligence system designed to mirror real human reasoning patterns. At the same time, InterLink Labs 👤 + 🌐’s AI-powered Human Credit Score applies advanced machine learning to analyze authenticity, contribution, and reliability. Creating an ethical model of digital reputation and fairness. Aligned with National Institute of Standards and Technology (National Institute of Standards and Technology) evaluation standards, InterLink Labs 👤 + 🌐 now aims to achieve Top 10 accuracy globally among AI models. Competing with research teams from Samsung Electronics, Kakao, キヤノン株式会社 / Canon Inc., and other global giants, InterLink Labs 👤 + 🌐’s engineers continue to train, benchmark, and refine their architecture daily to reach world-class precision and consistency. But this is more than a technical race. It’s a human mission. Every verified user contributes to the world’s first Human-Powered Intelligence Network, where real people fuel the evolution of trustworthy AI. As InterLink Labs 👤 + 🌐 advances toward global National Institute of Standards and Technology recognition, one truth becomes clear: The future of intelligence won’t be artificial. It will be human-powered. #InterLink #ITLG #ITL

InterLink Labs 👤 + 🌐

51,575 görüntüleme • 11 ay önce