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🚨 India’s Semiconductor Game Just Leveled Up Polymatech Electronics is building India’s first Dark Factory in Tamil Nadu — where robots run 24×7 with minimal human intervention. 🔹 150,000 sq ft automated facility 🔹 25 robots per 100 employees 🔹 1 operator controls 20–35 machines 🔹 Productivity jump: $100K... ➝ $1M per employee/month And this is just the beginning 👇 📈 Revenue Target FY27: ₹4,000–4,500 Cr (6 months window) ₹8,000–9,000 Cr annual projection 🏭 Expansion Plans: ▪ 2 million sq ft SEZ factory in Chhattisgarh ▪ Global presence: Singapore, France, USA ▪ Potential entry into Japan & Korea 🔬 Backward integration into raw materials like silver nitrate 🎯 Target: Full supply chain self-reliance by 2030 This isn’t just automation. This is India entering the global semiconductor race with scale, precision & ambition. IPO could fund the next phase of global dominance. Full Interview of Polymatech MD Eswara Rao Nandam #Polymatech #Semiconductor #MakeInIndia #DarkFactory #IPO #Automation #Robotics #AI #Electronics #Chips #IndiaGrowthStory #ManufacturingRevolution 🚀show more

Manoj Gupta

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16,130 次观看 • 5 个月前 •via X (Twitter)

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$AMD Massive Rotation from $NVDA $INTC🧵 Not Financial Advice! DYOR! 5-10 minutes before the bell today, last trading day of May 2026, massive rotation out of $INTC and $NVDA into $AMD. I wrote this thread this morning on what $TSM said on Energy Efficiency is now TOP Priotity and why AMD is the biggest winner. Of course I did not have influence on this rebalancing, I was just pointing out why Dr. Su saw this coming years ago. (Check the picture to understand more). I been talking about Agentic AI for like 3-4 years now. OpenClaw broke the CPU:GPU Ratio 1:4 narrative to 1:1 to 5:1 in late Jan and Feb 2026. I will link various threads where you can understand the full picture from supply chain, to TSMC expansion, and different Wafer Ratio for EPYC Venice and MI455X. Energy efficiency is a structural, long-term driver behind institutional rotation from $NVDA and $INTC into $AMD (with spillover strength in $AVGO for complementary networking/custom silicon). This isn't just short-term rebalancing, it's a massive bet on the shift from AI training (performance-at-any-cost) to inference, deployment, and embodied/agentic systems (where total cost of ownership, power draw, and scalability dominate). Precisely What I been writing about $AMD for years now, probably at least more than 5,000 threads.This is the FOMO from Institutions to own $AMD. Do know that AMD is the least owned Semi Stock among vs Peers. AI infrastructure is moving beyond massive training clusters to widespread inference for Agentic AI (running models 24/7) and embodied AI (robots, autonomous agents, edge devices). These workloads prioritize: ~Tokens-per-watt and performance-per-watt ~Lower total power consumption for data centers facing grid constraints ~Better economics at scale (cost-per-token, TCO) ~Thermal and power efficiency for on-device/robotics use Hyperscalers are now thinking more about Margin, Profitability, and $/M Tokens At $516/share. AMD Fwd PEG Ratio is still 35/100+= 0.35 AKA very cheap IMO for the growth and potential. A. Why institutions rotated out of $NVDA? Because Agentic AI is going to dominated by CPUs for years to come, moving violently to 5-10-20:1 CPU:GPU Ratio as enterprises are demanding more than 10-20 agents to run tasks. Now, that does not mean training is going away, Inference is just going to grow much faster. B. Why instiutitons rotated out of $INTC? Because AMD x86 unit share is only at 30-31% but Revenue share is already at 46.2% according to Mercury Research. And Dr. Su wants 50-60% market share, and that would mean 60-70%+ Revenue share where the CPUs TAM Is now already at $200B in 2026 and projected to be $500B by 2030. C. Why $AMD? Because AMD secured meaningful 2nm Capacity, Advanced Packaging and Memory through 2027-2028. And TSMC is expanding 2 primary 2nm Fabs toward 60-65k WPM each, and speeding up 5 2nm Fabs in Taiwan. With total up to 12 2nm Fabs through 2027/2028. 2nm Capacity is expected to be 140k+ WPM toward end of 2026, and 220-240k WPM by end of 2027. Apple has secured 35-45k WPM. And AMD does not have to worry about allocation competition until late 2027 from $AVGO for $META and $GOOGL(This may change) D. Agentic AI will evolve to 24/7 Autonomous Agent, and that will become the foundational layer for Robotic or Physical AI. Agentic AI (autonomous systems that plan, reason, use tools, self-correct, pursue long-horizon goals, and adapt) provides the high-level cognitive architecture. It turns raw perception and low-level control into useful, general-purpose behavior in the physical world. Physical AI (or Embodied AI) refers to AI that senses, understands, and acts directly in the real world through robots, actuators, and sensors. Agentic capabilities are what make this scalable and useful beyond narrow, scripted tasks. Reactive/programmed machines → To proactive, goal-oriented autonomous agents. How does this work? Autonomous Agent layer is the brain ~Vision-Language-Action models or robotics foundation models. ~Agentic loops: Planning, chain-of-thought reasoning, reflection, tool use (simulators, APIs), multi-step task decomposition. ~Persistent 24/7 operation with Memory, world modeling, continuous learning. Institutions may not like $AMD from 2022-2025, but they cannot stop this evolution and it is inevitable. Part of my main thesis for AMD to get to $5 Trillion Market Cap Long Term. Conclusion: Institutions are rotating capital toward AMD not merely for tactical rebalancing, but because Dr. Lisa Su and her team anticipated this exact inflection years in advance and have been methodically engineering AMD’s platform to dominate it. Dr. Su has long championed the convergence of Agentic AI as the high-level cognitive foundation for Physical AI and robotics. As far back as her 2023/2024 CES keynote and earlier strategic commentary, she described Physical AI (including humanoid robotics and edge autonomy) as “the next big thing”; a natural extension of agentic workflows moving from digital reasoning to real-world action. She emphasized that enabling persistent, 24/7 autonomous agents requires a full-stack approach: high-performance CPUs for orchestration and motion control, dedicated accelerators for real-time vision and multimodal inference, and open software ecosystems for rapid development. This vision aligns precisely with the structural drivers we’ve discussed. As AI shifts from training to massive-scale inference and embodiment, energy efficiency, total cost of ownership, and heterogeneous compute become first-order advantages. AMD’s Instinct MI350/MI355 series, Ryzen AI Embedded processors, and EPYC platforms deliver superior performance-per-watt and balanced CPU + GPU + NPU integration ideal for power-constrained robots that must run sophisticated agentic reasoning loops without excessive thermal or battery drain. Dr. Su has repeatedly highlighted the rising importance of CPUs in agentic systems (moving toward 1:1 or even CPU-heavy ratios with GPUs), positioning AMD’s strengths in orchestration, memory handling, and efficiency as critical for the next phase of growth. AMD is engineered for the deployment realities of embodied agents: scalable, efficient, and deployable at the edge and in physical systems. The institutional flows out of NVDA and INTC into AMD reflect recognition of this prepared leadership. Dr. Su didn’t just see the future of Agentic AI powering robotics, she has spent years building the silicon, software, and partnerships to make it practical and economically viable. This rotation signals confidence that the companies best positioned for the physical, always-on intelligence layer will capture the highest-volume opportunities in the coming decade. Not Financial Advice! DYOR!

$AMD Massive Rotation from $NVDA $INTC🧵 Not Financial Advice! DYOR! 5-10 minutes before the bell today, last trading day of May 2026, massive rotation out of $INTC and $NVDA into $AMD. I wrote this thread this morning on what $TSM said on Energy Efficiency is now TOP Priotity and why AMD is the biggest winner. Of course I did not have influence on this rebalancing, I was just pointing out why Dr. Su saw this coming years ago. (Check the picture to understand more). I been talking about Agentic AI for like 3-4 years now. OpenClaw broke the CPU:GPU Ratio 1:4 narrative to 1:1 to 5:1 in late Jan and Feb 2026. I will link various threads where you can understand the full picture from supply chain, to TSMC expansion, and different Wafer Ratio for EPYC Venice and MI455X. Energy efficiency is a structural, long-term driver behind institutional rotation from $NVDA and $INTC into $AMD (with spillover strength in $AVGO for complementary networking/custom silicon). This isn't just short-term rebalancing, it's a massive bet on the shift from AI training (performance-at-any-cost) to inference, deployment, and embodied/agentic systems (where total cost of ownership, power draw, and scalability dominate). Precisely What I been writing about $AMD for years now, probably at least more than 5,000 threads.This is the FOMO from Institutions to own $AMD. Do know that AMD is the least owned Semi Stock among vs Peers. AI infrastructure is moving beyond massive training clusters to widespread inference for Agentic AI (running models 24/7) and embodied AI (robots, autonomous agents, edge devices). These workloads prioritize: ~Tokens-per-watt and performance-per-watt ~Lower total power consumption for data centers facing grid constraints ~Better economics at scale (cost-per-token, TCO) ~Thermal and power efficiency for on-device/robotics use Hyperscalers are now thinking more about Margin, Profitability, and $/M Tokens At $516/share. AMD Fwd PEG Ratio is still 35/100+= 0.35 AKA very cheap IMO for the growth and potential. A. Why institutions rotated out of $NVDA? Because Agentic AI is going to dominated by CPUs for years to come, moving violently to 5-10-20:1 CPU:GPU Ratio as enterprises are demanding more than 10-20 agents to run tasks. Now, that does not mean training is going away, Inference is just going to grow much faster. B. Why instiutitons rotated out of $INTC? Because AMD x86 unit share is only at 30-31% but Revenue share is already at 46.2% according to Mercury Research. And Dr. Su wants 50-60% market share, and that would mean 60-70%+ Revenue share where the CPUs TAM Is now already at $200B in 2026 and projected to be $500B by 2030. C. Why $AMD? Because AMD secured meaningful 2nm Capacity, Advanced Packaging and Memory through 2027-2028. And TSMC is expanding 2 primary 2nm Fabs toward 60-65k WPM each, and speeding up 5 2nm Fabs in Taiwan. With total up to 12 2nm Fabs through 2027/2028. 2nm Capacity is expected to be 140k+ WPM toward end of 2026, and 220-240k WPM by end of 2027. Apple has secured 35-45k WPM. And AMD does not have to worry about allocation competition until late 2027 from $AVGO for $META and $GOOGL(This may change) D. Agentic AI will evolve to 24/7 Autonomous Agent, and that will become the foundational layer for Robotic or Physical AI. Agentic AI (autonomous systems that plan, reason, use tools, self-correct, pursue long-horizon goals, and adapt) provides the high-level cognitive architecture. It turns raw perception and low-level control into useful, general-purpose behavior in the physical world. Physical AI (or Embodied AI) refers to AI that senses, understands, and acts directly in the real world through robots, actuators, and sensors. Agentic capabilities are what make this scalable and useful beyond narrow, scripted tasks. Reactive/programmed machines → To proactive, goal-oriented autonomous agents. How does this work? Autonomous Agent layer is the brain ~Vision-Language-Action models or robotics foundation models. ~Agentic loops: Planning, chain-of-thought reasoning, reflection, tool use (simulators, APIs), multi-step task decomposition. ~Persistent 24/7 operation with Memory, world modeling, continuous learning. Institutions may not like $AMD from 2022-2025, but they cannot stop this evolution and it is inevitable. Part of my main thesis for AMD to get to $5 Trillion Market Cap Long Term. Conclusion: Institutions are rotating capital toward AMD not merely for tactical rebalancing, but because Dr. Lisa Su and her team anticipated this exact inflection years in advance and have been methodically engineering AMD’s platform to dominate it. Dr. Su has long championed the convergence of Agentic AI as the high-level cognitive foundation for Physical AI and robotics. As far back as her 2023/2024 CES keynote and earlier strategic commentary, she described Physical AI (including humanoid robotics and edge autonomy) as “the next big thing”; a natural extension of agentic workflows moving from digital reasoning to real-world action. She emphasized that enabling persistent, 24/7 autonomous agents requires a full-stack approach: high-performance CPUs for orchestration and motion control, dedicated accelerators for real-time vision and multimodal inference, and open software ecosystems for rapid development. This vision aligns precisely with the structural drivers we’ve discussed. As AI shifts from training to massive-scale inference and embodiment, energy efficiency, total cost of ownership, and heterogeneous compute become first-order advantages. AMD’s Instinct MI350/MI355 series, Ryzen AI Embedded processors, and EPYC platforms deliver superior performance-per-watt and balanced CPU + GPU + NPU integration ideal for power-constrained robots that must run sophisticated agentic reasoning loops without excessive thermal or battery drain. Dr. Su has repeatedly highlighted the rising importance of CPUs in agentic systems (moving toward 1:1 or even CPU-heavy ratios with GPUs), positioning AMD’s strengths in orchestration, memory handling, and efficiency as critical for the next phase of growth. AMD is engineered for the deployment realities of embodied agents: scalable, efficient, and deployable at the edge and in physical systems. The institutional flows out of NVDA and INTC into AMD reflect recognition of this prepared leadership. Dr. Su didn’t just see the future of Agentic AI powering robotics, she has spent years building the silicon, software, and partnerships to make it practical and economically viable. This rotation signals confidence that the companies best positioned for the physical, always-on intelligence layer will capture the highest-volume opportunities in the coming decade. Not Financial Advice! DYOR!

Mike

104,109 次观看 • 1 个月前

In a newly released technical update, SpaceX's leadership team, which includes communications manager Dan Huot, Director of Satellite Engineering Ian Dahl, and CEO Elon Musk, detailed a highly ambitious infrastructure roadmap to design, manufacture, and operate specialized artificial intelligence computing satellites at scale. Positioned as a major strategic pillar to dramatically elevate civilizational energy and processing capacity on the Kardashev scale, this strategy moves past traditional communications architectures into massive orbital server arrays. Here is the complete breakdown of the core technologies and timelines driving this space-based intelligence revolution: 🛰️ AI1 satellite power and compute capacity Ian Dahl and Elon Musk introduced the baseline performance targets for the first-generation AI1 satellite, explaining how its custom hardware is engineered to operate like an orbital data center server rack. Ian Dahl noted that their direct operational experience with xAI guided them to target a 150-kilowatt peak power capacity. To manage active machine learning workloads continuously, Elon Musk explained that the satellite is optimized to maintain a sustained average compute power envelope of 120 kilowatts, which directly mirrors the real-world performance of a terrestrial NVIDIA server rack. The official presentation slides outline several key operational metrics for this payload configuration: ⚡ The custom architecture delivers a 150 kW peak compute payload. 🔋 The system maintains a 120 kW sustained average compute payload under active workloads. ⚖️ The hardware achieves a highly optimized power-to-weight density of 70 kW per ton. 🔄 The layout features a completely interchangeable compute provider design. "We thought that the right place to start is around the 150 kilowatt peak power level. But as we look at the workloads with our experience with xAI, we see that we can support about 120 kilowatts of average compute. The 150 kilowatt peak power level roughly matches what, say, an NVIDIA GV300 rack would do. A more reasonable operating envelope would be around 120 kilowatts average power, but it can peak up to 150. So it is basically thinking about it as a rack of compute in space." --- 📐 AI1 satellite dimensions and thermal efficiency specs Elon Musk detailed the physical layout of the AI1 satellite, highlighting the massive dimensions required to accommodate its immense power and cooling hardware. He shared specific design criteria, explaining that the engineering relies on a custom 150 kW solar array paired with a high-capacity deployable liquid radiator thermal management system. The technical specifications of this vehicle layout include: 📏 The structural frame features a massive 70-meter wingspan. ↕️ The vehicle spans a total deployed height of 20 meters. ☀️ The onboard solar array delivers an efficiency of 250 W/m² using technology manufactured in Bastrop, Texas. 🌡️ The thermal system utilizes a 110 m² deployable liquid radiator to cleanly dump waste heat. 🔄 The cooling architecture incorporates redundant pumping loops for mission safety. 🛡️ The exterior contains integrated micrometeoroid shielding to protect the fluid lines. 🧭 The double-sided radiators achieve a dissipation rate of 1400 watts per square meter while remaining oriented knife-edge to the sun. "The assumptions here are 250 watts per square meter for the solar array and about 1400 watts per square meter for the radiators. The radiators are double-sided, radiating on both sides, and they're oriented knife-edge to the sun. They have about a 70-meter wingspan, so these are fairly large." --- 🧩 Simplified design architecture built on Starlink V3 tech Elon Musk explained that despite the satellite's imposing size, its internal architecture is fundamentally much simpler than a standard Starlink satellite. Because it lacks heavy phased array and parabolic communications antennas, the entire vehicle layout is completely streamlined around a few essential structural modules: 🎛️ The hardware framework is arranged around a centralized compute module. ☀️ Large deployable solar arrays extend outward to capture orbital energy. 🌡️ A deployable liquid-radiator thermal management system controls active operational temperatures. 🔄 The engineering team heavily leverages the component evolution and manufacturing experience gained from developing the Starlink V3 vehicle platform. "The AI satellite is actually much simpler than a Starlink satellite. A Starlink satellite has gigantic phased array antennas, parabolic antennas, and a lot of laser links, making it much more complicated. An AI satellite is essentially a lot of solar cells, a radiator, and you still need some laser links, but you don't have all of the super complex antennas that you have on a Starlink satellite. A lot of this is technology we've already made for the Starlink V3 satellites." --- 🔌 Interchangeable compute reference designs and high connectivity Elon Musk outlined a modular hardware approach for the satellite's payload, allowing it to house a variety of industry-standard processing units depending on client requirements. This interchangeable compute rack is supported by a high-bandwidth connectivity loop that links separate orbital units together or transmits data directly back to Earth. The core network parameters include: 🧠 Reference designs are fully established to seamlessly accommodate NVIDIA Reuben chips. 💾 The system architecture is built to support alternative setups using NVIDIA GB300 chips. 💻 Custom hardware layouts are explicitly designed to integrate Google TPUs. 🌐 The onboard communications setup delivers roughly 1 terabit of laser link connectivity. ⏱️ The network closes the communication loop directly with the main Starlink constellation at an ultra-low latency of only 3 milliseconds. "Our current reference design is for NVIDIA Reuben chips, or it could be either GB300 or Reuben chips. We'll also have a reference design for TPUs. Essentially, you can put up any existing chips into orbit. There would also be probably something on the order of a terabit of laser link connectivity from the satellite. Then you can connect these racks of compute to each other by the laser links or directly to the Starlink constellations. Light travels 300 kilometers per millisecond, so that's about three milliseconds away." --- 🏭 The "gigasat" AI satellite and solar production hub in Bastrop, Texas Dan Huot highlighted that the primary production hub for this entire hardware ecosystem is anchored at their sprawling complex in Bastrop, Texas, officially designated as the Gigasat factory. Elon Musk verified that construction is already actively underway on the solar manufacturing facility to feed the project's supply line, with plans moving forward to construct the adjacent AI satellite assembly lines. The physical footprint and timeline of this manufacturing hub are defined by the following benchmarks: 🗺️ The company has over 1,000 acres of land currently owned or under contract for the site. 🏢 The manufacturing complex boasts a massive structural building potential exceeding 11 million square feet. ⚙️ The facility will vertically integrate production to manufacture solar ingots, wafers, solar cells, and completed AI satellites. 📅 Both the solar and AI satellite production lines are targeted to be operational at a viable volume by the end of next year. "We're going to be building a lot of satellites and we're going to be building them here in Bastrop. We already have the solar manufacturing facility under construction, and then we will be building out the AI sat production building soon. We expect to have the AI sat production, the solar production, and all of that operating at some reasonable volume by the end of next year." --- 🏢 The 100-million-square-foot "terafab" chip factory Elon Musk revealed a massive, long-term scaling strategy to build an immense chip manufacturing facility dubbed the "terafab" to completely bypass global semiconductor volume constraints. This manufacturing infrastructure is designed to transition the company into next-generation industrial scaling by producing highly specialized computing components at an unprecedented volume. The scale of this infrastructure project is defined by several extraordinary engineering and production benchmarks: 🏭 The colossal factory is projected to span approximately 100 million square feet, making it ten times larger than the current Tesla Gigafactory Texas. ⚡ The facility is structurally engineered to achieve a massive manufacturing output of 1 terawatt per year once fully operational. 📦 This unprecedented physical footprint provides the capacity required to manufacture 1 billion full-reticle equivalent chips annually. 🔌 Each individual chip manufactured by the facility is designed to run at a power capacity of 1 kilowatt. 🇺🇸 The total scaled output of the facility represents an energy footprint that is exactly double the current annual electricity consumption of the entire United States. "In order to get to the next order of magnitude, you need a gigantic chip factory. To give you a sense of scale here, we expect that the terafab is going to be around 100 million square feet, which is 10 times the size of the Tesla Gigafactory Texas. From a logic die standpoint, that's like having a billion chips per year with a kilowatt per reticle, scaling to a terawatt per year. That is twice the current electricity consumption of the United States." --- 📶 Next-generation high-volume Starlink terminals Dan Huot and Elon Musk introduced their next-generation Starlink user terminals, which have been redesigned specifically to achieve massive manufacturing throughput. Elon Musk pointed out that these newer models will be produced in vastly higher volumes than current hardware designs to fulfill their long-term global deployment targets: 📈 The upgraded user hardware is manufactured at a much higher volume capacity than existing units. 🌍 The company's ultimate target is to successfully deploy a few hundred million of these next-generation terminals worldwide. "In fact, these are the new Starlink terminals, which we made in much higher volume than the current terminals. Ultimately, we think there's probably going to be a few hundred million Starlink terminals out there." --- 📈 Aspirational timeline for orbital AI compute scaling Elon Musk laid out an ambitious, multi-year execution timeline detailing how the company plans to progressively scale space-based processing power. The roadmap targets an initial run-rate by the end of next year and sets an aggressive pace to increase total operational capacity sequentially through a structured, multi-phase timeline: 1️⃣ The initial target aims to hit an annualized run-rate of 1 gigawatt of space AI compute by the end of next year. 2️⃣ The capacity scales to an annualized rate of 10 gigawatts within the next two and a half years. 3️⃣ The operational envelope expands to reach 100 gigawatts in three and a half years. 4️⃣ The long-term deployment plan scales directly to a full terawatt capacity per year using the output of the terafab. "The goal is to get to roughly an annualized rate of a gigawatt per year by the end of next year in terms of space AI compute. Then aspirationally, we want to scale that by an order of magnitude per year. In two and a half years, hitting an annualized rate of 10 gigawatts a year in space, and in three and a half years, maybe a hundred gigawatts, going beyond that with the terafab to scale to a terawatt per year." --- 🌕 Ultimate scaling via lunar production and mass drivers Elon Musk explained that scaling three orders of magnitude past a single terawatt forces a transition completely off-planet to avoid the logistical penalty of Earth's deep gravity well. The vision relies on establishing manufacturing infrastructure directly on the moon to leverage localized resource loops and zero-atmosphere physics: 🌙 The company plans to establish localized raw production lines on the moon to fabricate solar panels, photovoltaics, and radiators from lunar materials. ⚡ Manufacturing components locally avoids the massive fuel and mass penalties of transporting heavy structural materials from Earth. 🧲 Because the moon has no atmosphere and only one-sixth of Earth's gravity, the facility will utilize an electromagnetic mass driver to launch completed satellites. 🚀 Operating essentially as a linear electric motor rail gun, this mechanism will shoot fully assembled AI satellites straight into deep space without relying on chemical rockets. "The only way that we can really see that you can achieve that is on the moon with a mass driver, essentially where you do local production of photovoltaics, solar panels, and radiators on the moon. Because the moon has no atmosphere and only one-sixth Earth's gravity, you can accelerate the AI satellites into deep space without a rocket. You can basically shoot them into space using an electromagnetic gun, like a rail gun type—it's basically a linear electric motor."

In a newly released technical update, SpaceX's leadership team, which includes communications manager Dan Huot, Director of Satellite Engineering Ian Dahl, and CEO Elon Musk, detailed a highly ambitious infrastructure roadmap to design, manufacture, and operate specialized artificial intelligence computing satellites at scale. Positioned as a major strategic pillar to dramatically elevate civilizational energy and processing capacity on the Kardashev scale, this strategy moves past traditional communications architectures into massive orbital server arrays. Here is the complete breakdown of the core technologies and timelines driving this space-based intelligence revolution: 🛰️ AI1 satellite power and compute capacity Ian Dahl and Elon Musk introduced the baseline performance targets for the first-generation AI1 satellite, explaining how its custom hardware is engineered to operate like an orbital data center server rack. Ian Dahl noted that their direct operational experience with xAI guided them to target a 150-kilowatt peak power capacity. To manage active machine learning workloads continuously, Elon Musk explained that the satellite is optimized to maintain a sustained average compute power envelope of 120 kilowatts, which directly mirrors the real-world performance of a terrestrial NVIDIA server rack. The official presentation slides outline several key operational metrics for this payload configuration: ⚡ The custom architecture delivers a 150 kW peak compute payload. 🔋 The system maintains a 120 kW sustained average compute payload under active workloads. ⚖️ The hardware achieves a highly optimized power-to-weight density of 70 kW per ton. 🔄 The layout features a completely interchangeable compute provider design. "We thought that the right place to start is around the 150 kilowatt peak power level. But as we look at the workloads with our experience with xAI, we see that we can support about 120 kilowatts of average compute. The 150 kilowatt peak power level roughly matches what, say, an NVIDIA GV300 rack would do. A more reasonable operating envelope would be around 120 kilowatts average power, but it can peak up to 150. So it is basically thinking about it as a rack of compute in space." --- 📐 AI1 satellite dimensions and thermal efficiency specs Elon Musk detailed the physical layout of the AI1 satellite, highlighting the massive dimensions required to accommodate its immense power and cooling hardware. He shared specific design criteria, explaining that the engineering relies on a custom 150 kW solar array paired with a high-capacity deployable liquid radiator thermal management system. The technical specifications of this vehicle layout include: 📏 The structural frame features a massive 70-meter wingspan. ↕️ The vehicle spans a total deployed height of 20 meters. ☀️ The onboard solar array delivers an efficiency of 250 W/m² using technology manufactured in Bastrop, Texas. 🌡️ The thermal system utilizes a 110 m² deployable liquid radiator to cleanly dump waste heat. 🔄 The cooling architecture incorporates redundant pumping loops for mission safety. 🛡️ The exterior contains integrated micrometeoroid shielding to protect the fluid lines. 🧭 The double-sided radiators achieve a dissipation rate of 1400 watts per square meter while remaining oriented knife-edge to the sun. "The assumptions here are 250 watts per square meter for the solar array and about 1400 watts per square meter for the radiators. The radiators are double-sided, radiating on both sides, and they're oriented knife-edge to the sun. They have about a 70-meter wingspan, so these are fairly large." --- 🧩 Simplified design architecture built on Starlink V3 tech Elon Musk explained that despite the satellite's imposing size, its internal architecture is fundamentally much simpler than a standard Starlink satellite. Because it lacks heavy phased array and parabolic communications antennas, the entire vehicle layout is completely streamlined around a few essential structural modules: 🎛️ The hardware framework is arranged around a centralized compute module. ☀️ Large deployable solar arrays extend outward to capture orbital energy. 🌡️ A deployable liquid-radiator thermal management system controls active operational temperatures. 🔄 The engineering team heavily leverages the component evolution and manufacturing experience gained from developing the Starlink V3 vehicle platform. "The AI satellite is actually much simpler than a Starlink satellite. A Starlink satellite has gigantic phased array antennas, parabolic antennas, and a lot of laser links, making it much more complicated. An AI satellite is essentially a lot of solar cells, a radiator, and you still need some laser links, but you don't have all of the super complex antennas that you have on a Starlink satellite. A lot of this is technology we've already made for the Starlink V3 satellites." --- 🔌 Interchangeable compute reference designs and high connectivity Elon Musk outlined a modular hardware approach for the satellite's payload, allowing it to house a variety of industry-standard processing units depending on client requirements. This interchangeable compute rack is supported by a high-bandwidth connectivity loop that links separate orbital units together or transmits data directly back to Earth. The core network parameters include: 🧠 Reference designs are fully established to seamlessly accommodate NVIDIA Reuben chips. 💾 The system architecture is built to support alternative setups using NVIDIA GB300 chips. 💻 Custom hardware layouts are explicitly designed to integrate Google TPUs. 🌐 The onboard communications setup delivers roughly 1 terabit of laser link connectivity. ⏱️ The network closes the communication loop directly with the main Starlink constellation at an ultra-low latency of only 3 milliseconds. "Our current reference design is for NVIDIA Reuben chips, or it could be either GB300 or Reuben chips. We'll also have a reference design for TPUs. Essentially, you can put up any existing chips into orbit. There would also be probably something on the order of a terabit of laser link connectivity from the satellite. Then you can connect these racks of compute to each other by the laser links or directly to the Starlink constellations. Light travels 300 kilometers per millisecond, so that's about three milliseconds away." --- 🏭 The "gigasat" AI satellite and solar production hub in Bastrop, Texas Dan Huot highlighted that the primary production hub for this entire hardware ecosystem is anchored at their sprawling complex in Bastrop, Texas, officially designated as the Gigasat factory. Elon Musk verified that construction is already actively underway on the solar manufacturing facility to feed the project's supply line, with plans moving forward to construct the adjacent AI satellite assembly lines. The physical footprint and timeline of this manufacturing hub are defined by the following benchmarks: 🗺️ The company has over 1,000 acres of land currently owned or under contract for the site. 🏢 The manufacturing complex boasts a massive structural building potential exceeding 11 million square feet. ⚙️ The facility will vertically integrate production to manufacture solar ingots, wafers, solar cells, and completed AI satellites. 📅 Both the solar and AI satellite production lines are targeted to be operational at a viable volume by the end of next year. "We're going to be building a lot of satellites and we're going to be building them here in Bastrop. We already have the solar manufacturing facility under construction, and then we will be building out the AI sat production building soon. We expect to have the AI sat production, the solar production, and all of that operating at some reasonable volume by the end of next year." --- 🏢 The 100-million-square-foot "terafab" chip factory Elon Musk revealed a massive, long-term scaling strategy to build an immense chip manufacturing facility dubbed the "terafab" to completely bypass global semiconductor volume constraints. This manufacturing infrastructure is designed to transition the company into next-generation industrial scaling by producing highly specialized computing components at an unprecedented volume. The scale of this infrastructure project is defined by several extraordinary engineering and production benchmarks: 🏭 The colossal factory is projected to span approximately 100 million square feet, making it ten times larger than the current Tesla Gigafactory Texas. ⚡ The facility is structurally engineered to achieve a massive manufacturing output of 1 terawatt per year once fully operational. 📦 This unprecedented physical footprint provides the capacity required to manufacture 1 billion full-reticle equivalent chips annually. 🔌 Each individual chip manufactured by the facility is designed to run at a power capacity of 1 kilowatt. 🇺🇸 The total scaled output of the facility represents an energy footprint that is exactly double the current annual electricity consumption of the entire United States. "In order to get to the next order of magnitude, you need a gigantic chip factory. To give you a sense of scale here, we expect that the terafab is going to be around 100 million square feet, which is 10 times the size of the Tesla Gigafactory Texas. From a logic die standpoint, that's like having a billion chips per year with a kilowatt per reticle, scaling to a terawatt per year. That is twice the current electricity consumption of the United States." --- 📶 Next-generation high-volume Starlink terminals Dan Huot and Elon Musk introduced their next-generation Starlink user terminals, which have been redesigned specifically to achieve massive manufacturing throughput. Elon Musk pointed out that these newer models will be produced in vastly higher volumes than current hardware designs to fulfill their long-term global deployment targets: 📈 The upgraded user hardware is manufactured at a much higher volume capacity than existing units. 🌍 The company's ultimate target is to successfully deploy a few hundred million of these next-generation terminals worldwide. "In fact, these are the new Starlink terminals, which we made in much higher volume than the current terminals. Ultimately, we think there's probably going to be a few hundred million Starlink terminals out there." --- 📈 Aspirational timeline for orbital AI compute scaling Elon Musk laid out an ambitious, multi-year execution timeline detailing how the company plans to progressively scale space-based processing power. The roadmap targets an initial run-rate by the end of next year and sets an aggressive pace to increase total operational capacity sequentially through a structured, multi-phase timeline: 1️⃣ The initial target aims to hit an annualized run-rate of 1 gigawatt of space AI compute by the end of next year. 2️⃣ The capacity scales to an annualized rate of 10 gigawatts within the next two and a half years. 3️⃣ The operational envelope expands to reach 100 gigawatts in three and a half years. 4️⃣ The long-term deployment plan scales directly to a full terawatt capacity per year using the output of the terafab. "The goal is to get to roughly an annualized rate of a gigawatt per year by the end of next year in terms of space AI compute. Then aspirationally, we want to scale that by an order of magnitude per year. In two and a half years, hitting an annualized rate of 10 gigawatts a year in space, and in three and a half years, maybe a hundred gigawatts, going beyond that with the terafab to scale to a terawatt per year." --- 🌕 Ultimate scaling via lunar production and mass drivers Elon Musk explained that scaling three orders of magnitude past a single terawatt forces a transition completely off-planet to avoid the logistical penalty of Earth's deep gravity well. The vision relies on establishing manufacturing infrastructure directly on the moon to leverage localized resource loops and zero-atmosphere physics: 🌙 The company plans to establish localized raw production lines on the moon to fabricate solar panels, photovoltaics, and radiators from lunar materials. ⚡ Manufacturing components locally avoids the massive fuel and mass penalties of transporting heavy structural materials from Earth. 🧲 Because the moon has no atmosphere and only one-sixth of Earth's gravity, the facility will utilize an electromagnetic mass driver to launch completed satellites. 🚀 Operating essentially as a linear electric motor rail gun, this mechanism will shoot fully assembled AI satellites straight into deep space without relying on chemical rockets. "The only way that we can really see that you can achieve that is on the moon with a mass driver, essentially where you do local production of photovoltaics, solar panels, and radiators on the moon. Because the moon has no atmosphere and only one-sixth Earth's gravity, you can accelerate the AI satellites into deep space without a rocket. You can basically shoot them into space using an electromagnetic gun, like a rail gun type—it's basically a linear electric motor."

Ming

22,203 次观看 • 1 个月前

"India will be invited to join US-led initiative Pax Silica as 'full member' next month"- US embassador Sergio Gor Trying to act smart, Trump? But Bharat under PM .Narendra Modi Ji can see through this 'noble gesture'! Why's US Pushing Bharat into Pax Silica: It's All About American Interests, Not Altruism. Let's cut through diplomatic fluff. US inviting Bharat to join Pax Silica—a coalition for securing supply chains in critical minerals, semiconductors, AI & advanced tech—isn't some benevolent gesture. America doesn't do global alliances without hefty dose of self-interest. Here's real playbook on why Uncle Sam wants us in & crucially, how Bharat must play smart to avoid getting trapped in one-sided deal. 👉US Motives- Securing Dominance, Countering China & Tapping Bharat's Assets 1. Countering China's Monopoly: China controls 90% of global rare earth processing & massive chunk of semiconductor supply chains. Pax Silica is Washington's answer to "de-risk" from Beijing—building a "trusted" network of allies to reroute critical tech flows. Bharat, with world's 3rd-largest rare earth reserves (6.9 million tonnes, mostly in coastal monazite sands), is prime target. US wants access to our raw materials to feed their refineries & fabs, reducing reliance on China without lifting a finger on their own soil. 2. Geopolitical Leverage in Indo-Pacific: Pax Silica pulls Bharat deeper into US orbit as a "strategic counterweight" to China. It's about encircling Beijing economically—Japan, South Korea, UK, Israel are already in; adding Bharat's scale (population, market, geography) makes it powerhouse bloc. Trump-era reciprocity means fair trade on paper, but in reality, it's about locking in allies for tech war. 3. Economic Goldmine-Talent, Market & Cheap Resources: Bharat's engineering prowess, booming digital economy & manufacturing push (via PLI schemes) are irresistible. US sees us as a diversification hub—away from Taiwan's risks—for chips & AI. But dig deeper: Raw rare earths sell for $3,000/tonne, while processed magnets (which we'd import back) go for $25000/tonne. Joining gives US firms access to our resources, talent & $3T+ market, boosting their profits while we play supplier. 4. Tech Sovereignty for US, Not World: This's about controlling "operating system of modern power"—silicon, AI, data centers. By inviting Bharat early, US ensures standards, export controls & investments align with American rules, preventing a multipolar tech world where Bharat could pivot to alternatives. 🔥In short, Pax Silica is economic statecraft: Prosperity for US bloc, dependency for others. It's not charity; it's strategy. 👉How Bharat Must Not Fall into the Trap? JOIN, BUT ON OUR TERMS. Bharat should absolutely join—access to tech, R&D & global chains is a win for our semiconductor ambitions (we're already at <1% global production despite reserves). But NOT Blindly. Here's what Modi Sarkar will demand even as members: —Demand Tech Transfer & Build Domestic Muscle: No deal without enforceable clauses for refining tech & joint fabs in India. —Invest heavily in our own processing plants (e.g., expand IREL's capabilities) to avoid exporting cheap ore & importing pricey chips. –Push for co-ownership in AI/logistics standards—don't let US/Japan hog high-value end. —Maintain Strategic Autonomy by Diversifying Alliances. Will Keep balancing with China (via BRICS/SCO), Russia & EU. Bharat won't burn bridges; but will use Pax Silica to negotiate better terms elsewhere. —Safeguard Data, IP & Sovereignty: Enforce strict data localization in AI deals. Protect our startups from IP theft or forced partnerships. Monitor investments via CFIUS-like screening to block backdoor control. —Focus on Long-Term Resilience: Ramp up R&D funding (aim for 2% GDP), skill 1M+ engineers in semis/AI, & integrate with Quad/iCET for broader gains. IF AMERICA AGREES TO THIS, PAX SILICA IS WELCOME. OR ELSE....THANK YOU, BUT NO THANK YOU!

"India will be invited to join US-led initiative Pax Silica as 'full member' next month"- US embassador Sergio Gor Trying to act smart, Trump? But Bharat under PM .Narendra Modi Ji can see through this 'noble gesture'! Why's US Pushing Bharat into Pax Silica: It's All About American Interests, Not Altruism. Let's cut through diplomatic fluff. US inviting Bharat to join Pax Silica—a coalition for securing supply chains in critical minerals, semiconductors, AI & advanced tech—isn't some benevolent gesture. America doesn't do global alliances without hefty dose of self-interest. Here's real playbook on why Uncle Sam wants us in & crucially, how Bharat must play smart to avoid getting trapped in one-sided deal. 👉US Motives- Securing Dominance, Countering China & Tapping Bharat's Assets 1. Countering China's Monopoly: China controls 90% of global rare earth processing & massive chunk of semiconductor supply chains. Pax Silica is Washington's answer to "de-risk" from Beijing—building a "trusted" network of allies to reroute critical tech flows. Bharat, with world's 3rd-largest rare earth reserves (6.9 million tonnes, mostly in coastal monazite sands), is prime target. US wants access to our raw materials to feed their refineries & fabs, reducing reliance on China without lifting a finger on their own soil. 2. Geopolitical Leverage in Indo-Pacific: Pax Silica pulls Bharat deeper into US orbit as a "strategic counterweight" to China. It's about encircling Beijing economically—Japan, South Korea, UK, Israel are already in; adding Bharat's scale (population, market, geography) makes it powerhouse bloc. Trump-era reciprocity means fair trade on paper, but in reality, it's about locking in allies for tech war. 3. Economic Goldmine-Talent, Market & Cheap Resources: Bharat's engineering prowess, booming digital economy & manufacturing push (via PLI schemes) are irresistible. US sees us as a diversification hub—away from Taiwan's risks—for chips & AI. But dig deeper: Raw rare earths sell for $3,000/tonne, while processed magnets (which we'd import back) go for $25000/tonne. Joining gives US firms access to our resources, talent & $3T+ market, boosting their profits while we play supplier. 4. Tech Sovereignty for US, Not World: This's about controlling "operating system of modern power"—silicon, AI, data centers. By inviting Bharat early, US ensures standards, export controls & investments align with American rules, preventing a multipolar tech world where Bharat could pivot to alternatives. 🔥In short, Pax Silica is economic statecraft: Prosperity for US bloc, dependency for others. It's not charity; it's strategy. 👉How Bharat Must Not Fall into the Trap? JOIN, BUT ON OUR TERMS. Bharat should absolutely join—access to tech, R&D & global chains is a win for our semiconductor ambitions (we're already at <1% global production despite reserves). But NOT Blindly. Here's what Modi Sarkar will demand even as members: —Demand Tech Transfer & Build Domestic Muscle: No deal without enforceable clauses for refining tech & joint fabs in India. —Invest heavily in our own processing plants (e.g., expand IREL's capabilities) to avoid exporting cheap ore & importing pricey chips. –Push for co-ownership in AI/logistics standards—don't let US/Japan hog high-value end. —Maintain Strategic Autonomy by Diversifying Alliances. Will Keep balancing with China (via BRICS/SCO), Russia & EU. Bharat won't burn bridges; but will use Pax Silica to negotiate better terms elsewhere. —Safeguard Data, IP & Sovereignty: Enforce strict data localization in AI deals. Protect our startups from IP theft or forced partnerships. Monitor investments via CFIUS-like screening to block backdoor control. —Focus on Long-Term Resilience: Ramp up R&D funding (aim for 2% GDP), skill 1M+ engineers in semis/AI, & integrate with Quad/iCET for broader gains. IF AMERICA AGREES TO THIS, PAX SILICA IS WELCOME. OR ELSE....THANK YOU, BUT NO THANK YOU!

BhikuMhatre

60,782 次观看 • 6 个月前

$AMD $5 Trillion is Inevitable LT| Agentic AI🧵 Agentic AI is the new $5 Trillion TAM 🚨🚨🚨 This thead will do Comp with $INTC and how to quantify this massive Agentic AI demand spike, and forcing Jensen to rush a CPU design. Global Agentic AI Market size is estimated to be $3-$5Trillion TAM by 2030(McKinsey) Quantifying the demand from agentic AI for AMD involves assessing the broader market growth for agentic systems, their unique computational requirements (particularly for CPUs in orchestration and reasoning tasks), and AMD's positioning very well through products like EPYC processors and partnerships. AMD EPYC Venice is the most superior choice in 2026-2027 for most Agentic AI workloads Agentic AI refers to autonomous AI agents that perform multi-step tasks, involving sequential logic, tool integration, and decision-making workloads that heavily rely on CPUs for handling orchestration, memory management, and context switching, rather than just GPU-parallelized training or batch inference. Agentic AI is often cited as 40-100x more "hungry" than traditional AI due to its continuous, 24/7 operation and complex workflows. This stems from factors like chain-of-thought reasoning (multiple LLM calls per query), API/tool interactions, memory management, and orchestration loops, which can generate 10-100x more tokens and require real-time responsiveness. For example, a single agentic query might trigger 5-20 model inferences, making it 10-20x more compute-intensive than simple chatbots, and the always-on nature compounds this to 40-100x overall. Nvidia's CEO has highlighted this as driving "easily 100x more computation" for inference in agentic/reasoning setups. AMD's EPYC Venice (6th Gen EPYC, codenamed "Venice") and Intel's Xeon 7 Diamond Rapids represent the pinnacle of server CPU technology in 2026, both targeting high-performance data center workloads like AI inference, agentic AI orchestration, cloud computing, and HPC. Venice builds on AMD's Zen 6 architecture, emphasizing core density and efficiency, while Diamond Rapids leverages Intel's Panther Cove P-cores for balanced performance. Both chips adopt similar advancements like 16-channel DDR5 memory and PCIe Gen 6, but differ in core counts, process nodes, and overall design philosophy. Intel has faced acute supply constraints across its Xeon lineup, including legacy nodes (Intel 7/3) and the ramping 18A process for next-gen parts. Intel shortage is expected with lead times up to 6 months or longer. 1. AMD EPYC Venice vs Intel Xeon 7 Diamond Rapids Architecture AMD: Zen 6 chiplet design with 8 CCDs and dual IODs Intel: Panther Cove P-cores; multi-die architecture with 4 compute tiles Core/Thread Count AMD: Up to 256 cores / 512 threads (Zen 6c variant) Intel: Up to 192 cores / 192 threads Process Node AMD: TSMC N2 (2nm) Intel: Intel 18A (1.8nm-class); in-house fab Memory Support AMD: 16-channel DDR5; up to 1.6 TB/s bandwidth. Intel: 16-channel DDR5 ; up to 1.6 TB/s bandwidth I/O and Connectivity AMD: PCIe Gen 6 (up to 128 lanes); twice the CPU-to-GPU bandwidth Intel: PCIe Gen 6 (up to 128 lanes); LGA 9324 socket Power (TDP) AMD: Starting 400-500W, potentially lower due to efficiency gains from TSMC 2nm Intel: Starting 400-500W, as it targets competitive efficiency Performance Projections AMD: Up to 70% uplift vs. 5th Gen Turin (1.7x in multi-threaded/AI tasks) Intel: ~40% faster than Granite Rapids (Xeon 6, 128-core). Lags AMD in per-core perf and 40-50% behind Venice core-for-core comp Target Workloads AMD: AI inference/orchestration, HPC, cloud virtualization. Partnerships Intel: Hyperscale AI, general enterprise. Custom silicon Pricing: AMD: estimated $10k-$20k for top SKUs Intel: estimated $8-$18k Availability: AMD: Significant Ramp H2 2026 due to higher allocation from TSMC Intel: H1-H2 2026 delayed, but trying to catch up Overall: ~Venice's 256 cores provide a 33% edge over Diamond Rapids' 192, making it superior for massively parallel tasks like AI training/inference or virtualization ~TSMC's N2 vs. Intel 18A debates rage on which is "better," but AMD's mature chiplet approach yields better density ( 32 cores/CCD vs. Intel's 48/tile). Venice's redesign reduces latency, aiding agentic AI where CPUs handle orchestration ~ Early projections show Venice widening AMD's lead matching or exceeding Diamond Rapids' perf with fewer watts in multi-threaded benchmarks. Intel's no-SMT design (to prioritize AI) handicaps it vs. AMD's 512 threads, though Clearwater Forest (E-core) could compete in density-focused niches. ~Power & Cooling: Both push above 400-500W, demanding liquid cooling. ~AMD been taking market share now above 40%. AMD EPYC Venice emerges as the superior choice in 2026 for most server workloads. Its higher core/thread count (256/512 vs. 192/192), stronger per-core performance, and architecture optimized for AI-driven tasks (agentic orchestration with GPU integration) provide decisive advantages in throughput, scalability, and efficiency. Projections indicate Venice delivering 1.7x the performance of prior gens while widening the gap over Intel ( 40-70% leads in multi-threaded benchmarks). AMD's fabless model with TSMC ensures reliable scaling, and its ecosystem ( open ROCm) appeals to AI adopters. Intel's Diamond Rapids is competitive in single-threaded enterprise apps and custom hyperscale ( NVLink), with potential fab advantages for supply/security. However, without SMT and lower density, it falls short in core-for-core battles—exposing Intel to another generation of AMD dominance unless 18A yields surprise efficiency gains. For data centers prioritizing raw compute ( AI, HPC), Venice wins; for Intel-centric ecosystems or specialized I/O, Diamond Rapids holds ground. Real benchmarks post-launch will confirm, but logic points to AMD pulling ahead. 2. Market size , Potential Revenue and Supply Global Agentic AI market size is projected to be $3-$5 Trillion by 2030 according to McKinsey, where consensus points to 40-50% CAGR driven by small to large enterprise demand. I also wrote a full thread on how and why Agentic AI is so explosive that AMD will blow all anlaysts estimate for subscribers. Link below if you are interested. AMD's data center segment hit a record $5.4B in Q4 2025 (up 39% YoY), with EPYC shipments ramping due to agentic demand. With 2GW of deployment in H2 2026, AMD AI data center revenue has $40-$50B+ at the lowest or most conservative projection; or Total Revenue in the $77-$94B For FY2026. However, Agentic AI massive demand spike could send EPYC revenue 3x to 4x in the next few years, potentially surpassing MI series GPU demand as enterprises prioritize CPU-dense Rack setups. This is pushing $NVDA Jensen to rush a CPU design and acquired Groq, a new CPU player due to this massive TAM. Noted that this is just popping just in weeks, highlighting we are just so early in this AI Supercycle and the pace of adoption is insane, and clearly productivity will skyrocket. Why? Because Agentic AI is 24/7 Smart AI agent working for you or your businesses is a mad compelling, and it is estimated to be 40-100x more Inference Hugnry! Many experts already said it is impossible to project this kind of Inference Demand. AI CapEx is expected to ramp up even more in 2027-2028-2029 and 2030 as Global Agentic AI is going to scale to $3-$5 Trillion TAM by 2030. The nature of Agentic is driving higher CPU/GPU ratio, with CPUs handling 50-90% of Agentic workflows. For example, The current Helios Rack: 18 compute trays per rack with 72 GPUs + 18 CPUs. The beauty of this $META and $AMD long term partnership is, that it is absolutely flexible to adjust racks to higher CPU rato or equal to service different needs. Helios rack can be easily swap to 2 GPUs 2CPUs or even CPUs only trays for dedicated orchestration/head nodes. You see, the beauty of this open rack-scale is flexibility and evolvability. If Agentic AI demand pushes much higher, AMD should be able to adjust variant trays without abandoning Heilos Rack. We can't talk just about massive Agentic AI demand without talking about the Supply side or TSMC. TSMC, AMD's primary foundry for advanced nodes ( Zen 6/Venice on N2/2nm), is addressing AI-driven shortages through massive expansions. TSMC accelerates fab construction with up to 10 facilities targeted for 2026. TSMC is accelerating its domestic manufacturing expansion, with industry sources indicating that as many as ten fabs could be under construction or preparing to begin operations across Taiwan’s major science parks. TSMC Capex: $52-56B in 2026 (up 37% YoY), with $45B already approved for new/upgraded capacities. 70-80% for advanced processes (2nm/A16), 10-20% for packaging (CoWoS quadrupling to 120-140K wafers/month by late 2026). In addition, Taiwanese companies (led by TSMC) commit to at least $250B in direct investments in US-based advanced semiconductor, AI, and energy production/innovation capacity.Taiwan provides $250B in government credit guarantees to facilitate additional investments and build a full US semiconductor ecosystem (including industrial parks). TSMC completed a second land purchase in Arizona (January 2026) for gigafab scaling, with an additional $100B+ (potentially four more modules) to further expand and qualify for tariff exemptions. AMD with secured 12GW from OpenAI and $META and massive Agentic AI will mean higher priority acess to 20-30% more wafers on TSMC advanced nodes, as TSMC has multi-year agreements with AMD for AI chips. Dr. C. C. Wei, CEO of TSMC quote: "I spend a lot of time in the last three or four months talking to my customer and then customers. Customer. I want to make sure that my customers demand are real. I talk to those cloud service providers, all of them. Their answer is. I'm quite satisfied with their answer. Actually they show me the evidence that the AI really help their business. So they grow their business successfully and he or she in their financial return. So I also double check their financial status. They are very rich." Amid shortages, the US buildout ensures AMD can ramp production of Instinct GPUs and EPYC CPUs without the constraints hitting competitors like Intel. By diversifying away from Taiwan (85% of advanced nodes today), the agreement mitigates supply disruptions, ensuring stable flows for AMD's chips. Scaling production and securing supply will matter for AMD the most in the next 5-10 years growth. The growth could be 80-100% YoY or higher; or it could be in the 60%. The aggressive TSMC supply ramp is reassuring the higher growth point. Conclusion: AMD stands at a pivotal inflection point in 2026, where the explosive rise of agentic AI demanding 40-100x more inference compute through its 24/7, multi-step orchestration positions the company to potentially triple its EPYC CPU revenue to $45-60B+ by 2028 while scaling Instinct GPUs to tens of billions annually by 2027. Agentic AI demand could push AI CapEx closer to $1 Trillion in 2027, far higher than most estimates. Dr. Lisa Su, AMD's visionary CEO, is masterfully securing supply to harness this massive demand by prioritizing operational execution and deep TSMC collaboration, ensuring readiness for the second-half 2026 AI ramp. Dr. Su has explicitly called out surging EPYC demand for agentic tasks where CPUs power head nodes and traditional workloads alongside GPUs while guiding for data center dominance through proactive capacity planning and partnerships like Nutanix ($150M investment for open agentic platforms) or providing tens of millions CPUs for OpenAI, $META, $ORCL, $AMZN, $MSFT, $GOOGL and others. Her strategy includes multi-year TSMC agreements for advanced nodes (N2 for Venice CPUs and future Instincts), diversifying beyond Taiwan to mitigate risks, and unveiling innovations like the MI455X GPU at CES 2026, which she touted as enabling "the next trillion-dollar market opportunity" in physical AI. Dr. Su's forward-looking vision predicting AI reaching 5 billion users emphasizes "AI everywhere," backed by hardware like Ryzen AI chips, all while declaring demand "going through the roof" and committing to scale without bottlenecks. TSMC's aggressive ramp-up, fueled by $52-56B in 2026 capex (up 37% YoY) and 10+ new fabs across Taiwan, the US (Arizona cluster expanding to 6+ modules with $165B+ investment), Japan, and Europe, provides profound reassurance for AMD's supply stability. The January 2026 US-Taiwan agreement committing $250B in investments and credit guarantees for US reshoring accelerates this, granting tariff relief (15% rates with 1.5-2.5x exemptions) tied to capacity buildouts, enabling TSMC to potentially double output over the decade to meet AI wafer hunger. This translates to 20-30% higher wafer allocations on key nodes, sidestepping Intel-like shortages and empowering Dr. Su's team to deliver on hyperscaler demands without disruption. Ultimately, this synergy cements AMD's leadership in the agentic era, promising sustained growth, $5T+ valuations at scale, and a resilient path forward as AI reshapes the world. This is NOT Financial Advice! Video source: AMD CES 2026

$AMD $5 Trillion is Inevitable LT| Agentic AI🧵 Agentic AI is the new $5 Trillion TAM 🚨🚨🚨 This thead will do Comp with $INTC and how to quantify this massive Agentic AI demand spike, and forcing Jensen to rush a CPU design. Global Agentic AI Market size is estimated to be $3-$5Trillion TAM by 2030(McKinsey) Quantifying the demand from agentic AI for AMD involves assessing the broader market growth for agentic systems, their unique computational requirements (particularly for CPUs in orchestration and reasoning tasks), and AMD's positioning very well through products like EPYC processors and partnerships. AMD EPYC Venice is the most superior choice in 2026-2027 for most Agentic AI workloads Agentic AI refers to autonomous AI agents that perform multi-step tasks, involving sequential logic, tool integration, and decision-making workloads that heavily rely on CPUs for handling orchestration, memory management, and context switching, rather than just GPU-parallelized training or batch inference. Agentic AI is often cited as 40-100x more "hungry" than traditional AI due to its continuous, 24/7 operation and complex workflows. This stems from factors like chain-of-thought reasoning (multiple LLM calls per query), API/tool interactions, memory management, and orchestration loops, which can generate 10-100x more tokens and require real-time responsiveness. For example, a single agentic query might trigger 5-20 model inferences, making it 10-20x more compute-intensive than simple chatbots, and the always-on nature compounds this to 40-100x overall. Nvidia's CEO has highlighted this as driving "easily 100x more computation" for inference in agentic/reasoning setups. AMD's EPYC Venice (6th Gen EPYC, codenamed "Venice") and Intel's Xeon 7 Diamond Rapids represent the pinnacle of server CPU technology in 2026, both targeting high-performance data center workloads like AI inference, agentic AI orchestration, cloud computing, and HPC. Venice builds on AMD's Zen 6 architecture, emphasizing core density and efficiency, while Diamond Rapids leverages Intel's Panther Cove P-cores for balanced performance. Both chips adopt similar advancements like 16-channel DDR5 memory and PCIe Gen 6, but differ in core counts, process nodes, and overall design philosophy. Intel has faced acute supply constraints across its Xeon lineup, including legacy nodes (Intel 7/3) and the ramping 18A process for next-gen parts. Intel shortage is expected with lead times up to 6 months or longer. 1. AMD EPYC Venice vs Intel Xeon 7 Diamond Rapids Architecture AMD: Zen 6 chiplet design with 8 CCDs and dual IODs Intel: Panther Cove P-cores; multi-die architecture with 4 compute tiles Core/Thread Count AMD: Up to 256 cores / 512 threads (Zen 6c variant) Intel: Up to 192 cores / 192 threads Process Node AMD: TSMC N2 (2nm) Intel: Intel 18A (1.8nm-class); in-house fab Memory Support AMD: 16-channel DDR5; up to 1.6 TB/s bandwidth. Intel: 16-channel DDR5 ; up to 1.6 TB/s bandwidth I/O and Connectivity AMD: PCIe Gen 6 (up to 128 lanes); twice the CPU-to-GPU bandwidth Intel: PCIe Gen 6 (up to 128 lanes); LGA 9324 socket Power (TDP) AMD: Starting 400-500W, potentially lower due to efficiency gains from TSMC 2nm Intel: Starting 400-500W, as it targets competitive efficiency Performance Projections AMD: Up to 70% uplift vs. 5th Gen Turin (1.7x in multi-threaded/AI tasks) Intel: ~40% faster than Granite Rapids (Xeon 6, 128-core). Lags AMD in per-core perf and 40-50% behind Venice core-for-core comp Target Workloads AMD: AI inference/orchestration, HPC, cloud virtualization. Partnerships Intel: Hyperscale AI, general enterprise. Custom silicon Pricing: AMD: estimated $10k-$20k for top SKUs Intel: estimated $8-$18k Availability: AMD: Significant Ramp H2 2026 due to higher allocation from TSMC Intel: H1-H2 2026 delayed, but trying to catch up Overall: ~Venice's 256 cores provide a 33% edge over Diamond Rapids' 192, making it superior for massively parallel tasks like AI training/inference or virtualization ~TSMC's N2 vs. Intel 18A debates rage on which is "better," but AMD's mature chiplet approach yields better density ( 32 cores/CCD vs. Intel's 48/tile). Venice's redesign reduces latency, aiding agentic AI where CPUs handle orchestration ~ Early projections show Venice widening AMD's lead matching or exceeding Diamond Rapids' perf with fewer watts in multi-threaded benchmarks. Intel's no-SMT design (to prioritize AI) handicaps it vs. AMD's 512 threads, though Clearwater Forest (E-core) could compete in density-focused niches. ~Power & Cooling: Both push above 400-500W, demanding liquid cooling. ~AMD been taking market share now above 40%. AMD EPYC Venice emerges as the superior choice in 2026 for most server workloads. Its higher core/thread count (256/512 vs. 192/192), stronger per-core performance, and architecture optimized for AI-driven tasks (agentic orchestration with GPU integration) provide decisive advantages in throughput, scalability, and efficiency. Projections indicate Venice delivering 1.7x the performance of prior gens while widening the gap over Intel ( 40-70% leads in multi-threaded benchmarks). AMD's fabless model with TSMC ensures reliable scaling, and its ecosystem ( open ROCm) appeals to AI adopters. Intel's Diamond Rapids is competitive in single-threaded enterprise apps and custom hyperscale ( NVLink), with potential fab advantages for supply/security. However, without SMT and lower density, it falls short in core-for-core battles—exposing Intel to another generation of AMD dominance unless 18A yields surprise efficiency gains. For data centers prioritizing raw compute ( AI, HPC), Venice wins; for Intel-centric ecosystems or specialized I/O, Diamond Rapids holds ground. Real benchmarks post-launch will confirm, but logic points to AMD pulling ahead. 2. Market size , Potential Revenue and Supply Global Agentic AI market size is projected to be $3-$5 Trillion by 2030 according to McKinsey, where consensus points to 40-50% CAGR driven by small to large enterprise demand. I also wrote a full thread on how and why Agentic AI is so explosive that AMD will blow all anlaysts estimate for subscribers. Link below if you are interested. AMD's data center segment hit a record $5.4B in Q4 2025 (up 39% YoY), with EPYC shipments ramping due to agentic demand. With 2GW of deployment in H2 2026, AMD AI data center revenue has $40-$50B+ at the lowest or most conservative projection; or Total Revenue in the $77-$94B For FY2026. However, Agentic AI massive demand spike could send EPYC revenue 3x to 4x in the next few years, potentially surpassing MI series GPU demand as enterprises prioritize CPU-dense Rack setups. This is pushing $NVDA Jensen to rush a CPU design and acquired Groq, a new CPU player due to this massive TAM. Noted that this is just popping just in weeks, highlighting we are just so early in this AI Supercycle and the pace of adoption is insane, and clearly productivity will skyrocket. Why? Because Agentic AI is 24/7 Smart AI agent working for you or your businesses is a mad compelling, and it is estimated to be 40-100x more Inference Hugnry! Many experts already said it is impossible to project this kind of Inference Demand. AI CapEx is expected to ramp up even more in 2027-2028-2029 and 2030 as Global Agentic AI is going to scale to $3-$5 Trillion TAM by 2030. The nature of Agentic is driving higher CPU/GPU ratio, with CPUs handling 50-90% of Agentic workflows. For example, The current Helios Rack: 18 compute trays per rack with 72 GPUs + 18 CPUs. The beauty of this $META and $AMD long term partnership is, that it is absolutely flexible to adjust racks to higher CPU rato or equal to service different needs. Helios rack can be easily swap to 2 GPUs 2CPUs or even CPUs only trays for dedicated orchestration/head nodes. You see, the beauty of this open rack-scale is flexibility and evolvability. If Agentic AI demand pushes much higher, AMD should be able to adjust variant trays without abandoning Heilos Rack. We can't talk just about massive Agentic AI demand without talking about the Supply side or TSMC. TSMC, AMD's primary foundry for advanced nodes ( Zen 6/Venice on N2/2nm), is addressing AI-driven shortages through massive expansions. TSMC accelerates fab construction with up to 10 facilities targeted for 2026. TSMC is accelerating its domestic manufacturing expansion, with industry sources indicating that as many as ten fabs could be under construction or preparing to begin operations across Taiwan’s major science parks. TSMC Capex: $52-56B in 2026 (up 37% YoY), with $45B already approved for new/upgraded capacities. 70-80% for advanced processes (2nm/A16), 10-20% for packaging (CoWoS quadrupling to 120-140K wafers/month by late 2026). In addition, Taiwanese companies (led by TSMC) commit to at least $250B in direct investments in US-based advanced semiconductor, AI, and energy production/innovation capacity.Taiwan provides $250B in government credit guarantees to facilitate additional investments and build a full US semiconductor ecosystem (including industrial parks). TSMC completed a second land purchase in Arizona (January 2026) for gigafab scaling, with an additional $100B+ (potentially four more modules) to further expand and qualify for tariff exemptions. AMD with secured 12GW from OpenAI and $META and massive Agentic AI will mean higher priority acess to 20-30% more wafers on TSMC advanced nodes, as TSMC has multi-year agreements with AMD for AI chips. Dr. C. C. Wei, CEO of TSMC quote: "I spend a lot of time in the last three or four months talking to my customer and then customers. Customer. I want to make sure that my customers demand are real. I talk to those cloud service providers, all of them. Their answer is. I'm quite satisfied with their answer. Actually they show me the evidence that the AI really help their business. So they grow their business successfully and he or she in their financial return. So I also double check their financial status. They are very rich." Amid shortages, the US buildout ensures AMD can ramp production of Instinct GPUs and EPYC CPUs without the constraints hitting competitors like Intel. By diversifying away from Taiwan (85% of advanced nodes today), the agreement mitigates supply disruptions, ensuring stable flows for AMD's chips. Scaling production and securing supply will matter for AMD the most in the next 5-10 years growth. The growth could be 80-100% YoY or higher; or it could be in the 60%. The aggressive TSMC supply ramp is reassuring the higher growth point. Conclusion: AMD stands at a pivotal inflection point in 2026, where the explosive rise of agentic AI demanding 40-100x more inference compute through its 24/7, multi-step orchestration positions the company to potentially triple its EPYC CPU revenue to $45-60B+ by 2028 while scaling Instinct GPUs to tens of billions annually by 2027. Agentic AI demand could push AI CapEx closer to $1 Trillion in 2027, far higher than most estimates. Dr. Lisa Su, AMD's visionary CEO, is masterfully securing supply to harness this massive demand by prioritizing operational execution and deep TSMC collaboration, ensuring readiness for the second-half 2026 AI ramp. Dr. Su has explicitly called out surging EPYC demand for agentic tasks where CPUs power head nodes and traditional workloads alongside GPUs while guiding for data center dominance through proactive capacity planning and partnerships like Nutanix ($150M investment for open agentic platforms) or providing tens of millions CPUs for OpenAI, $META, $ORCL, $AMZN, $MSFT, $GOOGL and others. Her strategy includes multi-year TSMC agreements for advanced nodes (N2 for Venice CPUs and future Instincts), diversifying beyond Taiwan to mitigate risks, and unveiling innovations like the MI455X GPU at CES 2026, which she touted as enabling "the next trillion-dollar market opportunity" in physical AI. Dr. Su's forward-looking vision predicting AI reaching 5 billion users emphasizes "AI everywhere," backed by hardware like Ryzen AI chips, all while declaring demand "going through the roof" and committing to scale without bottlenecks. TSMC's aggressive ramp-up, fueled by $52-56B in 2026 capex (up 37% YoY) and 10+ new fabs across Taiwan, the US (Arizona cluster expanding to 6+ modules with $165B+ investment), Japan, and Europe, provides profound reassurance for AMD's supply stability. The January 2026 US-Taiwan agreement committing $250B in investments and credit guarantees for US reshoring accelerates this, granting tariff relief (15% rates with 1.5-2.5x exemptions) tied to capacity buildouts, enabling TSMC to potentially double output over the decade to meet AI wafer hunger. This translates to 20-30% higher wafer allocations on key nodes, sidestepping Intel-like shortages and empowering Dr. Su's team to deliver on hyperscaler demands without disruption. Ultimately, this synergy cements AMD's leadership in the agentic era, promising sustained growth, $5T+ valuations at scale, and a resilient path forward as AI reshapes the world. This is NOT Financial Advice! Video source: AMD CES 2026

Mike

44,460 次观看 • 4 个月前

$AMD is easily a $1,200 stock IMO| CPUs TAM 🧵 Not Financial Advice! DYOR! In this thread, I want to discuss the actual TAM for CPUs data center for just 2026, where many are giving different ranges, where I don't agree with. I will explain in detail why I disagree with these research firms and financial analysts using Math. And this thread should not be treated as Financial Advice. I'm just explaining my research and thought process so we can have a discussion. In 2024/2025, I gave out $620 PT for FY2026 was too conservative for AMD potential. At the time, It was early and many were just laughing, that PT was unrealistic and the AI world is run on GPUs only. Today, most of these folks are laughing with me. That is ok, I dont offer financial advice, and I do not need everyone to agree with me. I respect other opinions. If you enjoy this kind of thread, slap the like/repost/bookmark. If you want to support my work further and gain more in-depth analysis, consider subscribe! In early 2026, hyperscalers, enterprises, and OEMs are scrambling as Intel and AMD server CPUs are largely sold out for the year, with prices jumping 10–20% and lead times stretching from weeks to months (or longer for certain SKUs). What was once a GPU dominated story has flipped: the shift to explosive Agentic AI with its multi-step reasoning loops, tool calling, multi-agent orchestration, real-time data movement, and reinforcement learning, is dramatically tightening CPU:GPU ratios from the old training-era 1:4–8 all the way to 1:1 to 5:1 or even CPU-heavy configurations. CEOs across NVIDIA, AMD, Intel, Google, Meta, Microsoft, and public companies have been sounding the alarm on CNBC, Bloomberg, and earnings calls. CPUs are “cool again,” and in many agentic deployments they are becoming the new bottleneck alongside (or even ahead of) GPUs and custom ASICs. In 2025, roughly 12-15m AI GPUs + AI ASICs GPUs shipped, and is expect to be 15-20m units by 2026, where it suggesting Training demand is not going away. The actual TAM is structural, multiplicative demand that has already forced AMD to double its long-term server CPU TAM forecast to >$120 billion by 2030 (>35% CAGR), with Dr. Lisa Su noting Q2 2026 server CPU sales expected to surge 70%+ year-over-year and demand “far exceeding expectations.” At the same time, AMD’s secured 30–40% share of TSMC’s initial 2nm capacity (behind only Apple’s >50%) positions it to ramp Zen 6-based EPYC Venice exactly when this agentic wave hits hardest but even that aggressive five-fab 2nm expansion (with plans scaling toward 11 total advanced facilities) cannot instantly close the gap in the near-term. Supply constraints on wafers, advanced packaging, and power are compounding the squeeze, just as hyperscalers forward-buy and lock in long-term deals. 1. The actual potential TAM Various sources and institutions are giving $50-$160-$200B CPUs TAM toward 2030, and i disagree, where supply is severely behind vs Demand by at least 2-3 years or even longer by some estimates. The actual TAM will probably be 15-20m for FY2026. The typical average selling price from low to high end is $5,000 to $15,000, but due to rising memory, and different inflationary pressures on Semi, it would be more logical to think between $7,000-17,000. A. CPU:GPU Ratio at 1:1 A basic calucation at mid range =12,000 x 15-20m CPUs= $180-$240B TAM B. CPU:GPU Ratio at 5:1 = $12,000 x 75m-100m CPUs= $900B-$1.2T TAM Of course TSMC cannot even supply 20% of this massive inflection TAM in 2026. But do we think of Demand for TAM or Supply for TAM? Hence we are seeing massive 2nm Ramp from TSMC for $AMD. IMO, conservatively, I would take down 15-20% on 1:1 or $135-$192B TAM for just 2026. Im not even talking about 2030. We are just months into this, it is impossible to estimate Cagr atm, but this is 1-5 agents running tasks, I wrote a thread on 24/7 autonomous agents thread, where companies could use 50-250 agents to run tasks for them 24/7. It would require a different structural CPU:GPU to bring down the cost of token as well as handling the Orchestration bottleneck. GPUs would be useless and sit idle waiting for CPU due to highly CPU-intensive nature. The cost per Million tokens must come down more rapidly for this 50-250 autonomous agents to work, otherwise the token cost would be too enormous. Helios Rack is estimated to bring inference cost down to $0.0003-$0.0005/M tokens with 18 EPYC Venices along with 72 MI455x and other chips+ Components. A heavier or CPUs dense rack would bring down inference cost further. EPYC Verano(2027 gen 7 AI-optimized) is expected to drive inference costs meaningfully lower than the Venice baseline likely to the $0.00002–$0.00025 per million tokens range (or even sub-$0.00015 in highly optimized agentic/batch workloads). Verano have higher core counts than Venice, LPDDR5X SOCAMM2 memory support, more AI optimized and Next-Gen rack density & efficiency. 2. $AMD secured at least 30-40% of TSMC 2nm capacity and Memory from Samsung through 2028-2030. 2 2nm fabs are entering ramping phase toward 60-65k wafers per months and 5 dedicated 2nm fabs entering mass production/ramp in 2026. Will link sub threads below if you are interest for full detail. Apple is reported to secure 50%+ 2nm capacity for Iphone 18 and Mac chips and AMD secured at least 30-40% capacity while $NVDA $AVGO $ARM $AMZN $GOOGL and others are on 3nm. This broader aggressive ramp from TSMC to target up to 11 fabs is to address $AMD massive growth ahead. Where $ARM is facing massive CPUs supply constraints as they have to compete with other Mega Cap players on 3nm allocation. And $INTC is also facing supply constraints for data center CPUs and PC per management with lead times extrended to longer than 12 weeks. Dr. Su is aiming for higher than 50%+ Market share, and I believe it is achievable in 2026 or 2027 as AMD has the strongest CPUs offerings. Dr. Su did not want to take advantage of the shortage and she said during the Q1 earning call, AMD is prioritizing Units shipped while guiding margin to be inching 60%. If Jensen were in charge, I'm sure margin would be 70-75% in this kind of severe CPUs shortage condition. But that is not how Dr. Su operates for more than a decade. She wants most market share. So we will see it in revenue growth, but as TSMC ramps faster and faster, AMD Operating and FCF margin will massively improve vs prior decade. A significantly higher margin profile than before. 3. How I came up with $1,200 withint 12-18 months? At $1,200/ share, that would be around $2 Trillion MC. I expect FY2027 revenue to be $124-$144B where data center revenue dominates overall revenue. AI GPUs: I will stick to the lowest end so show u that I'm conservative at $18B for each GW vs $NVDA Rubin is $30B+ (most likely Helios Rack in the $20B+ due to memory price rising). We know deals with OpenAI and Meta are around 12GW and additional multi-customers at multi-GW scale were hinted and will be revealed as we get to July 22-23 2026 Advancing AI event. For now I will conservatively add a bit more to this model. (3-6GW Helios Rack Range) EPYC Venice is reported to be in $15,000-$20,000. However large customers will likely to enjoy $10-$12k discount. I expect AMD to be able to ramp 7m EPYC Venice for entire 2026 and 3-4m of EPYC Verano(higher price than Venice). If we take an average selling price of $10,000 to be on the conservative side. Take down another 30% to be even more conservative on projection. I like to be conservative. That would be ~ 7m EPYC CPUs(Venice + Verano) for FY2027 or 583,000 units per month or 15,000 additional 2nm wafers per month which is completely reasonable for current TSMC Ramp, and I may be too conservative here. EPYC Verano and MI500 series will also be on 2nm. AI GPUs: 3GW x $18B= $54B EPYC CPUs: $10k x 7m CPUs= $70B = Data center revenue alone is $124B Other segments= probably in the $20-$25B FY 2027. FY2027 revenue = $124-$149B At 7m EPYC CPUs for entire 2027, that would be more than 50% market share when we comp it to availability from supply side, not from total Demand. It is possible that TSMC could significantly ramp even more capacity in 2027, so we will see. Metric Q1 2026 FY2027 Gross Margin 55-56% 60-62% Operating Margin 25-26% 32-35% Net Income Margin ~22% 26-30% FCF Margin 25% 28-30% At $124-$149B Revenue FY 2027 Net Income would be $32-$44B EPS would be $20-$27 (GAAP) Non-GAAP would be $25-$31 At $1,200 a share or $2T valuation that would be: 13.4-16x Price to Sales (P/S) 38-48 P/E At this kind of growth of AI SuperCycle, I think it is very reasonable valuation. If we use today at $406/share or $661B MC: 2027 P/S = 4.4x-5.3x 2027 P/E = 13x-16x Is AMD today expensive or cheap to you? Above is already a very conservative where I trimmed 20-30% of doable units. Meaning, there could be upside if TSMC is able to ramp meaningfully like they are planning. Conclusion: A $1,200 per share valuation IMO for AMD in FY2027 is not expensive at all; it is, in fact, conservative when viewed against the structural explosion in agentic AI demand we have mapped out. With server CPU TAM potentially scaling into the $100–$200B+ range in just CPU:GPU 1:1 Ratio for just 2026. AMD positioned to capture 50%+ share thanks to its 2nm TSMC allocation advantage and full-stack leadership, the company could realistically deliver $124–149B in total revenue and $25–$31+ non-GAAP EPS. At those levels, $1,200 implies a 2027 P/E = 13x-16x. Entirely reasonable for a company that will have become the clear Inference Queen (and in many workloads the preferred) AI infrastructure provider, with operating margins expanding above 30% and tens of billions in high-margin rack-scale AI revenue. Dr. Lisa Su was right presciently so about the Agentic AI inflection all the way back to her early 2022–2023 commentary on the coming shift from pure training to inference and orchestration-heavy workloads. While the broader market only fully woke up to this in 2026 when she doubled AMD’s long-term server CPU TAM forecast to >$120B by 2030 (with >35% CAGR), Dr. Su and her team have consistently positioned the company at the center of the CPU renaissance. The explosive demand we are seeing today, sold-out lines, rising ASPs, and hyperscalers forward-buying entire gigawatts of Helios-class systems is exactly the outcome she forecasted years ago. Not Financial Advice! DYOR!

Mike

301,322 次观看 • 2 个月前

Behind The Scenes In The Vegas Loop: Inside Elon Musk's The Boring Company Bold Bet On Urban Mobility Hey everyone. Tesla Owners Silicon Valley (Tesla Owners Silicon Valley) here. I recently had the chance to go behind the scenes with Steve Davis, President of The Boring Company, for a deep dive into the Vegas Loop in Las Vegas. This wasn’t a quick photo op. It was a full 47-minute immersion: riding through the LED-lit tunnels in a Tesla, visiting active construction sites with Prufrock boring machines, and hearing directly from Steve about what’s working today, and what’s coming next. I’m posting the full long-form video alongside this recap so you can experience it firsthand. But here’s the readable, “what actually matters” story from the tour. From “Traffic Is Soul-Crushing” To A Working Underground Network The Boring Company was founded in 2016, born of a familiar frustration: gridlocked cities that can’t build fast enough, cheap enough, or with minimal disruption. The premise is simple but ambitious: reinvent tunneling to make it practical infrastructure, not a decade-long mega-project. Las Vegas is where that idea is being tested at real scale. Instead of waiting for buses, shuttles, or rail schedules, the Vegas Loop aims to provide point-to-point trips in Teslas, fast, quiet, and emissions-free, connecting major destinations without the chaos of the Strip above. And after seeing it up close, what stands out most is how operational it already is. This isn’t a render. It’s a functioning system handling real demand, in real conditions, with real riders. What It Feels Like: Fast, Weirdly Fun, And Surprisingly Smooth The “Loop experience” is part transit, part sci-fi. The tunnels are lined with shifting LEDs—purples, greens, yellows—that make the ride feel more like entering a venue than commuting. Trips are short and direct. One example Steve shared: LVCC to Encore in about 85 seconds. But the biggest “wait, that just happened” moment on the tour was Full Self-Driving. FSD Underground (And Onto Surface Streets) We rode in a Model Y running Full Self-Driving (Supervised), which navigated the tunnels smoothly and then transitioned back to surface streets without intervention. Steve’s point wasn’t that autonomy is a cool demo; it’s that autonomy is a force multiplier for throughput, consistency, and future scale. Steve Davis: “Full Self-Driving Supervised is live commercially between LVCC and Encore, watch this: zero interventions as it navigates the tunnels and pops out onto surface streets seamlessly.” Right now, they still operate with safety drivers, but the trajectory is clear: as autonomy matures, the system can move more people with tighter headways and less variability than human-driven operations. The Numbers: “Spiky Demand” Is Where This System Wants To Win Vegas isn’t a steady-demand commuter city. It’s a burst-demand city: conventions, games, concerts, and tourist surges. Steve emphasized that this is exactly where the Loop model shines, because you can scale vehicles dynamically without rebuilding an entire transit line. During CES 2026, the Loop moved 90,000+ passengers, peaking at 6,600+ riders per hour, including 22,000+ trips to/from Resorts World, Encore, and Westgate. That’s on top of 3.5M+ total passengers since 2021. Steve Davis: “We’ve hit over 3 million passengers since 2021, and during CES 2026 alone, we shuttled more than 90,000 people, peaking at 6,600 passengers per hour without a hitch.” And beyond the numbers, there’s a secondary effect people don’t always talk about: for many riders, this is their first time in a Tesla, and it’s an unusually positive first impression. The Airport Connection: A Phased Plan With A Very Clear Endgame Connecting the system to Harry Reid International Airport is the crown jewel, and they’re doing it in phases to deliver value quickly while they work through the harder parts. Phase 1 (Live Now) Limited airport rides are already operating via a mix of tunnels and surface streets from existing stations, including Resorts World, Encore, Westgate, and LVCC. They’re doing roughly 50 test rides per day, and Steve noted 100 of ~130 vehicles are already “airport-ready” with transponders. Phase 2 (Next Couple Months) This is where things get meaningfully faster: a 2.2-mile dual tunnel from Westgate to 4744 Paradise Road, eliminating about two miles of surface traffic and stoplights. New stations are planned at Virgin Hotels, The Boring Company’s apartment complex, the former Gordon Biersch site, and Firefly. Fleet expands to 160 vehicles. Steve Davis: “Phase 2 kicks in soon: a 2.2-mile tunnel to Paradise Road, cutting out those surface miles and stoplights.” Phase 3 Extend to 5032 Palo Verde Road near Terminal 1, further removing surface bottlenecks around Tropicana and University Center. Fleet scales to 250–300 vehicles. Phase 4 (The “Holy Grail”) A direct underground station at the terminals, true curb-to-gate simplicity, fully underground. Steve Davis: “Phase 4 is the holy grail: a direct underground station right at the airport terminals.” The Big Build: 68 Miles, 104 Stations, Privately Funded The long-term vision is expansive: 68 miles of tunnels and 104 stations spanning the Strip, downtown, the stadium, and the airport. Core Strip construction begins this fall, with a 2027 target for that major phase, and further expansion into 2028–2029. Steve emphasized something important here: the funding model. These builds are privately funded, and the cost structure is the entire point: build rapidly and avoid “subway economics.” Steve Davis: “68 miles, 104 stations… all privately funded at about $10M per mile, versus billions for subways.” The Real Workhorses: Prufrock Boring Machines Up Close If the Loop is the user experience, Prufrock is the engine underneath it. Seeing Prufrock at an active dig site is hard to describe unless you’ve stood next to one. It’s enormous, loud, and relentlessly practical. The key advantage is that it changes the setup cost: it can launch from the surface without massive open pits, and it’s designed to move fast, with a long-term target of one mile per week. The machine isn’t just digging; it’s built around an integrated approach to lining, pumping, and maintaining the tunnel environment while staying cost-effective. Challenges They’re Solving In Real Time: Groundwater And Permitting One of the most interesting “myth-busting” moments was hearing Steve talk about tunnel conditions. Despite the desert setting, the tunnels are roughly 30 feet below grade, and in many areas, they’re fully submerged in groundwater, sand, clay, caliche, and water management, all part of the daily reality. Steve Davis: “Tunnels are 30 feet down, fully submerged in groundwater, desert myth busted.” They manage leaks through periodic sealing (foam, maintenance cycles) and now operate with stronger compliance processes for water treatment and disposal. The bigger long-term bottleneck, though, isn’t engineering; it’s approvals. Steve noted they need hundreds of permits (600+), and many can take months. Their push is toward a more streamlined, operator-style approval model, closer to how SpaceX is regulated: certify capability and safety, then execute without rearguing every step. Steve Davis: “Permitting’s the bottleneck… we’re advocating for a SpaceX-style operator license.” Fleet Scaling And The “Robovan” Strategy Right now, the fleet is about 130 Teslas, including Model Ys and Cybertrucks, tuned for tight turns and repeated high-frequency operations. The larger goal is to scale up to 1,200 vehicles as the network grows. And that’s where Robovan (high-occupancy, event-optimized vehicles) becomes strategically important. Steve’s framing was refreshingly clear: cars are more efficient for small groups. Robovans win when you can predict surges, like a Raiders game or a Sphere show, and load high-occupancy vehicles in advance. Steve Davis: “Robovans shine when everyone’s going to the same spot… that’s when you put the high occupancy vehicle in.” What’s Next: Suburbs, Regional Links, And Bigger Swing Ideas After the core network is built, they’re looking at suburban expansions (Henderson, Summerlin) via shorter demo segments first, proving utility for pedestrian and vehicle connectivity. And then Steve hinted at the kind of long-range thinking that gets people excited (and skeptical): longer-distance routes, potentially even Hyperloop concepts like Reno connections, if permitting and economics align. Steve Davis: “Suburbs like Henderson and Summerlin next… long-term? Hyperloop to Reno… private funding makes it doable if permitting catches up.” Final Take: Vegas Is Becoming A Live Testbed For A New Kind Of Transit This tour made one thing very clear: The Boring Company isn’t trying to win the “traditional public transit debate.” They’re trying to change the rules of what’s feasible, building faster, cheaper, and with an experience that people actually want to use. Watching FSD glide through the tunnels, seeing Prufrock tearing through the ground, and hearing the phased plan for the airport and Strip expansion straight from Steve… It’s hard not to feel like Vegas is a real-world preview of what mobility can look like when infrastructure is built like technology. Huge thanks to Steve Davis and The Boring Company team for the access and the time. And keep an eye out, I’m posting the full 47-minute video with this recap so you can see the ride, the sites, and the details for yourself. What do you think, would you ride the Loop instead of sitting in Strip traffic?

Behind The Scenes In The Vegas Loop: Inside Elon Musk's The Boring Company Bold Bet On Urban Mobility Hey everyone. Tesla Owners Silicon Valley (Tesla Owners Silicon Valley) here. I recently had the chance to go behind the scenes with Steve Davis, President of The Boring Company, for a deep dive into the Vegas Loop in Las Vegas. This wasn’t a quick photo op. It was a full 47-minute immersion: riding through the LED-lit tunnels in a Tesla, visiting active construction sites with Prufrock boring machines, and hearing directly from Steve about what’s working today, and what’s coming next. I’m posting the full long-form video alongside this recap so you can experience it firsthand. But here’s the readable, “what actually matters” story from the tour. From “Traffic Is Soul-Crushing” To A Working Underground Network The Boring Company was founded in 2016, born of a familiar frustration: gridlocked cities that can’t build fast enough, cheap enough, or with minimal disruption. The premise is simple but ambitious: reinvent tunneling to make it practical infrastructure, not a decade-long mega-project. Las Vegas is where that idea is being tested at real scale. Instead of waiting for buses, shuttles, or rail schedules, the Vegas Loop aims to provide point-to-point trips in Teslas, fast, quiet, and emissions-free, connecting major destinations without the chaos of the Strip above. And after seeing it up close, what stands out most is how operational it already is. This isn’t a render. It’s a functioning system handling real demand, in real conditions, with real riders. What It Feels Like: Fast, Weirdly Fun, And Surprisingly Smooth The “Loop experience” is part transit, part sci-fi. The tunnels are lined with shifting LEDs—purples, greens, yellows—that make the ride feel more like entering a venue than commuting. Trips are short and direct. One example Steve shared: LVCC to Encore in about 85 seconds. But the biggest “wait, that just happened” moment on the tour was Full Self-Driving. FSD Underground (And Onto Surface Streets) We rode in a Model Y running Full Self-Driving (Supervised), which navigated the tunnels smoothly and then transitioned back to surface streets without intervention. Steve’s point wasn’t that autonomy is a cool demo; it’s that autonomy is a force multiplier for throughput, consistency, and future scale. Steve Davis: “Full Self-Driving Supervised is live commercially between LVCC and Encore, watch this: zero interventions as it navigates the tunnels and pops out onto surface streets seamlessly.” Right now, they still operate with safety drivers, but the trajectory is clear: as autonomy matures, the system can move more people with tighter headways and less variability than human-driven operations. The Numbers: “Spiky Demand” Is Where This System Wants To Win Vegas isn’t a steady-demand commuter city. It’s a burst-demand city: conventions, games, concerts, and tourist surges. Steve emphasized that this is exactly where the Loop model shines, because you can scale vehicles dynamically without rebuilding an entire transit line. During CES 2026, the Loop moved 90,000+ passengers, peaking at 6,600+ riders per hour, including 22,000+ trips to/from Resorts World, Encore, and Westgate. That’s on top of 3.5M+ total passengers since 2021. Steve Davis: “We’ve hit over 3 million passengers since 2021, and during CES 2026 alone, we shuttled more than 90,000 people, peaking at 6,600 passengers per hour without a hitch.” And beyond the numbers, there’s a secondary effect people don’t always talk about: for many riders, this is their first time in a Tesla, and it’s an unusually positive first impression. The Airport Connection: A Phased Plan With A Very Clear Endgame Connecting the system to Harry Reid International Airport is the crown jewel, and they’re doing it in phases to deliver value quickly while they work through the harder parts. Phase 1 (Live Now) Limited airport rides are already operating via a mix of tunnels and surface streets from existing stations, including Resorts World, Encore, Westgate, and LVCC. They’re doing roughly 50 test rides per day, and Steve noted 100 of ~130 vehicles are already “airport-ready” with transponders. Phase 2 (Next Couple Months) This is where things get meaningfully faster: a 2.2-mile dual tunnel from Westgate to 4744 Paradise Road, eliminating about two miles of surface traffic and stoplights. New stations are planned at Virgin Hotels, The Boring Company’s apartment complex, the former Gordon Biersch site, and Firefly. Fleet expands to 160 vehicles. Steve Davis: “Phase 2 kicks in soon: a 2.2-mile tunnel to Paradise Road, cutting out those surface miles and stoplights.” Phase 3 Extend to 5032 Palo Verde Road near Terminal 1, further removing surface bottlenecks around Tropicana and University Center. Fleet scales to 250–300 vehicles. Phase 4 (The “Holy Grail”) A direct underground station at the terminals, true curb-to-gate simplicity, fully underground. Steve Davis: “Phase 4 is the holy grail: a direct underground station right at the airport terminals.” The Big Build: 68 Miles, 104 Stations, Privately Funded The long-term vision is expansive: 68 miles of tunnels and 104 stations spanning the Strip, downtown, the stadium, and the airport. Core Strip construction begins this fall, with a 2027 target for that major phase, and further expansion into 2028–2029. Steve emphasized something important here: the funding model. These builds are privately funded, and the cost structure is the entire point: build rapidly and avoid “subway economics.” Steve Davis: “68 miles, 104 stations… all privately funded at about $10M per mile, versus billions for subways.” The Real Workhorses: Prufrock Boring Machines Up Close If the Loop is the user experience, Prufrock is the engine underneath it. Seeing Prufrock at an active dig site is hard to describe unless you’ve stood next to one. It’s enormous, loud, and relentlessly practical. The key advantage is that it changes the setup cost: it can launch from the surface without massive open pits, and it’s designed to move fast, with a long-term target of one mile per week. The machine isn’t just digging; it’s built around an integrated approach to lining, pumping, and maintaining the tunnel environment while staying cost-effective. Challenges They’re Solving In Real Time: Groundwater And Permitting One of the most interesting “myth-busting” moments was hearing Steve talk about tunnel conditions. Despite the desert setting, the tunnels are roughly 30 feet below grade, and in many areas, they’re fully submerged in groundwater, sand, clay, caliche, and water management, all part of the daily reality. Steve Davis: “Tunnels are 30 feet down, fully submerged in groundwater, desert myth busted.” They manage leaks through periodic sealing (foam, maintenance cycles) and now operate with stronger compliance processes for water treatment and disposal. The bigger long-term bottleneck, though, isn’t engineering; it’s approvals. Steve noted they need hundreds of permits (600+), and many can take months. Their push is toward a more streamlined, operator-style approval model, closer to how SpaceX is regulated: certify capability and safety, then execute without rearguing every step. Steve Davis: “Permitting’s the bottleneck… we’re advocating for a SpaceX-style operator license.” Fleet Scaling And The “Robovan” Strategy Right now, the fleet is about 130 Teslas, including Model Ys and Cybertrucks, tuned for tight turns and repeated high-frequency operations. The larger goal is to scale up to 1,200 vehicles as the network grows. And that’s where Robovan (high-occupancy, event-optimized vehicles) becomes strategically important. Steve’s framing was refreshingly clear: cars are more efficient for small groups. Robovans win when you can predict surges, like a Raiders game or a Sphere show, and load high-occupancy vehicles in advance. Steve Davis: “Robovans shine when everyone’s going to the same spot… that’s when you put the high occupancy vehicle in.” What’s Next: Suburbs, Regional Links, And Bigger Swing Ideas After the core network is built, they’re looking at suburban expansions (Henderson, Summerlin) via shorter demo segments first, proving utility for pedestrian and vehicle connectivity. And then Steve hinted at the kind of long-range thinking that gets people excited (and skeptical): longer-distance routes, potentially even Hyperloop concepts like Reno connections, if permitting and economics align. Steve Davis: “Suburbs like Henderson and Summerlin next… long-term? Hyperloop to Reno… private funding makes it doable if permitting catches up.” Final Take: Vegas Is Becoming A Live Testbed For A New Kind Of Transit This tour made one thing very clear: The Boring Company isn’t trying to win the “traditional public transit debate.” They’re trying to change the rules of what’s feasible, building faster, cheaper, and with an experience that people actually want to use. Watching FSD glide through the tunnels, seeing Prufrock tearing through the ground, and hearing the phased plan for the airport and Strip expansion straight from Steve… It’s hard not to feel like Vegas is a real-world preview of what mobility can look like when infrastructure is built like technology. Huge thanks to Steve Davis and The Boring Company team for the access and the time. And keep an eye out, I’m posting the full 47-minute video with this recap so you can see the ride, the sites, and the details for yourself. What do you think, would you ride the Loop instead of sitting in Strip traffic?

Tesla Owners Silicon Valley

447,027 次观看 • 6 个月前

UAE plans world’s first underwater bullet train connecting Dubai to Mumbai | Joseph Shavit, Brighter Side of News UAE unveils plans for a record-breaking underwater train linking Dubai and Mumbai, blending high-speed travel with marine views. A groundbreaking transportation project is in the works—one that could forever change how long-distance travel is imagined. The United Arab Emirates has revealed plans for a massive underwater train that would connect the port city of Fujairah with Mumbai, India’s financial hub. Covering nearly 1,200 miles beneath the Arabian Sea, this engineering marvel aims to combine speed, luxury, and scenic immersion. With a projected travel speed between 600 and 1,000 kilometers per hour, the journey promises to outpace flights, offering a smoother, more visually captivating experience. “This is not just about transport, but an immersive experience,” said Dr. Ahmed Al Hariri, who leads the UAE’s National Advisor Bureau. The train, initially floated as an idea in 2018, is no longer a mere concept. Now actively pursued, it blends utility with spectacle. Panoramic windows lining the tunnel will allow travelers to witness the vibrant underwater world while racing beneath the waves. With both countries looking to expand trade and tourism, the project is more than a marvel—it’s a strategic bridge between two thriving regions. Beyond Transit: Trade, Tourism, and Rivalry This high-speed rail isn’t solely about moving people across borders. It’s designed to serve dual purposes—transporting goods such as freshwater from India to the UAE and oil in the reverse direction. Dubai sees it as a way to deepen trade partnerships with India while also redefining what tourism can be. As the UAE pushes ahead, the project has stirred comparisons with Saudi Arabia’s $1 trillion NEOM city initiative. Both Gulf nations are competing to lead the region’s next wave of futuristic infrastructure. If Dubai completes this undersea route before NEOM opens its doors, it could gain a symbolic edge. Sara Ahmed, a travel blogger based in Dubai, emphasized the project’s allure: “The underwater train isn’t just about getting from point A to B. It’s about relishing the wonders of the marine world.” The rivalry isn’t just about spectacle. Both countries aim to become global symbols of innovation. While Saudi Arabia builds artificial islands and futuristic skylines, the UAE is diving into the ocean to carve out its legacy. The Technical Mountain Ahead Building such a colossal structure beneath the sea comes with significant engineering and financial challenges. The train would need to withstand intense water pressure, ensure passenger safety at high speeds, and deliver reliability over thousands of trips. There are also psychological barriers to overcome—some people may feel claustrophobic or uneasy traveling deep underwater. Rajesh Verma, a businessman in Mumbai, expressed caution: “I’ve always been wary of the ocean’s depths. As much as I love the idea, I’ll likely stick to conventional modes of transport.” For many, it’s not just about the technology, but the comfort of knowing it works without flaws. Still, the financial commitment is not the UAE’s greatest concern. With deep pockets and a track record of delivering iconic projects—from the world’s tallest skyscraper to the man-made Palm Islands—Dubai’s leaders are more focused on technological feasibility than on cost. Experts estimate the project could cost several billion dollars, depending on materials, design, and the tunnel’s complexity. Initial reports suggest the use of transparent materials instead of traditional opaque bricks, creating a unique window into the marine ecosystem. That vision aligns with the broader goal of crafting an unforgettable travel experience rather than just a faster commute. A Journey Like No Other If completed, the Dubai-Mumbai train will dwarf the Channel Tunnel between England and France in both scale and ambition. While the Channel Tunnel spans just 35 miles and runs at modest speeds of 70 miles per hour, this new project plans to stretch over 1,200 miles and operate at speeds ten times faster. Passengers will not just travel—they will encounter an oceanic world through the train’s clear walls. The experience could easily find a place on travelers’ bucket lists. Some have already begun referring to it as the "Deep Blue Express." However, the question remains: can such a massive project be delivered safely and sustainably? Those behind the project believe so. A feasibility report is now being commissioned to explore the materials, construction methods, and environmental impact. That report will shape whether the idea remains a dream or becomes the world's most advanced rail system. The train’s potential extends beyond engineering or aesthetics. It’s a bold statement about how humans can rethink geography. It’s a bridge between cultures, markets, and innovations. Whether it becomes a staple of global travel or serves as inspiration for future projects, the underwater train project already stands as a testament to human vision and determination. What the Future Holds The success of this bold venture will rest on more than just construction. Public confidence, regulatory approvals, and environmental planning must align. Nonetheless, the Dubai-Mumbai train has sparked imaginations across the world and ignited new discussions about what’s possible in global travel. At a time when nations are reimagining how cities and transport networks interact, this project represents the next logical step: one that literally plunges into uncharted waters. As with most pioneering ventures, it may take years, if not decades, to complete. But its very announcement pushes the boundaries of what’s considered possible. Whether you one day ride the Deep Blue Express or watch its progress from afar, its story marks a new era in engineering and global connection. And for the UAE, it’s another chance to show that when it comes to bold ideas, no challenge is too deep. Today’s Longest High-Speed Rail Journeys Beijing to Kunming, China: Covering approximately 2,653 kilometers, this route connects Beijing with Kunming, offering travel times between 10 hours 43 minutes and 14 hours 54 minutes. Beijing to Guangzhou, China: Spanning about 2,298 kilometers, this line links Beijing to Guangzhou, significantly reducing travel time compared to conventional trains. Shanghai to Kunming, China: This route extends over 2,066 kilometers, connecting Shanghai with Kunming, facilitating efficient travel across eastern and southwestern China. Beijing to Shanghai, China: At approximately 1,318 kilometers, this line connects two of China's major cities, Beijing and Shanghai, and is noted as the world's longest high-speed line constructed in a single phase. Hangzhou to Shenzhen, China: This route covers around 1,495 kilometers, linking Hangzhou with Shenzhen, enhancing connectivity along China's southeastern coast. Qingdao to Yinchuan, China: Spanning about 1,762 kilometers, this line connects Qingdao to Yinchuan, facilitating travel between eastern and northwestern China. Beijing to Harbin, China: This route extends over approximately 1,700 kilometers, linking Beijing with Harbin, serving as a vital connection to northeastern China. Guangzhou to Kunming, China: Covering about 1,285 kilometers, this line connects Guangzhou with Kunming, enhancing travel between southern and southwestern China. Tokyo to Hakata, Japan: This route spans approximately 1,174 kilometers, connecting Tokyo with Hakata, and is part of Japan's renowned Shinkansen network. Madrid to Barcelona, Spain: Covering about 621 kilometers, this line links Madrid with Barcelona, significantly reducing travel time between Spain's two largest cities. The Seikan Tunnel in Japan is the world's current longest undersea tunnel used by bullet trains. Spanning 53.85 kilometers (33.46 miles), it includes a 23.3-kilometer (14.5-mile) section beneath the Tsugaru Strait, connecting Honshu and Hokkaido. Shinkansen bullet trains traverse this tunnel, which descends approximately 100 meters (330 feet) below the seabed and 240 meters (790 feet) below sea level. Read more:

UAE plans world’s first underwater bullet train connecting Dubai to Mumbai | Joseph Shavit, Brighter Side of News UAE unveils plans for a record-breaking underwater train linking Dubai and Mumbai, blending high-speed travel with marine views. A groundbreaking transportation project is in the works—one that could forever change how long-distance travel is imagined. The United Arab Emirates has revealed plans for a massive underwater train that would connect the port city of Fujairah with Mumbai, India’s financial hub. Covering nearly 1,200 miles beneath the Arabian Sea, this engineering marvel aims to combine speed, luxury, and scenic immersion. With a projected travel speed between 600 and 1,000 kilometers per hour, the journey promises to outpace flights, offering a smoother, more visually captivating experience. “This is not just about transport, but an immersive experience,” said Dr. Ahmed Al Hariri, who leads the UAE’s National Advisor Bureau. The train, initially floated as an idea in 2018, is no longer a mere concept. Now actively pursued, it blends utility with spectacle. Panoramic windows lining the tunnel will allow travelers to witness the vibrant underwater world while racing beneath the waves. With both countries looking to expand trade and tourism, the project is more than a marvel—it’s a strategic bridge between two thriving regions. Beyond Transit: Trade, Tourism, and Rivalry This high-speed rail isn’t solely about moving people across borders. It’s designed to serve dual purposes—transporting goods such as freshwater from India to the UAE and oil in the reverse direction. Dubai sees it as a way to deepen trade partnerships with India while also redefining what tourism can be. As the UAE pushes ahead, the project has stirred comparisons with Saudi Arabia’s $1 trillion NEOM city initiative. Both Gulf nations are competing to lead the region’s next wave of futuristic infrastructure. If Dubai completes this undersea route before NEOM opens its doors, it could gain a symbolic edge. Sara Ahmed, a travel blogger based in Dubai, emphasized the project’s allure: “The underwater train isn’t just about getting from point A to B. It’s about relishing the wonders of the marine world.” The rivalry isn’t just about spectacle. Both countries aim to become global symbols of innovation. While Saudi Arabia builds artificial islands and futuristic skylines, the UAE is diving into the ocean to carve out its legacy. The Technical Mountain Ahead Building such a colossal structure beneath the sea comes with significant engineering and financial challenges. The train would need to withstand intense water pressure, ensure passenger safety at high speeds, and deliver reliability over thousands of trips. There are also psychological barriers to overcome—some people may feel claustrophobic or uneasy traveling deep underwater. Rajesh Verma, a businessman in Mumbai, expressed caution: “I’ve always been wary of the ocean’s depths. As much as I love the idea, I’ll likely stick to conventional modes of transport.” For many, it’s not just about the technology, but the comfort of knowing it works without flaws. Still, the financial commitment is not the UAE’s greatest concern. With deep pockets and a track record of delivering iconic projects—from the world’s tallest skyscraper to the man-made Palm Islands—Dubai’s leaders are more focused on technological feasibility than on cost. Experts estimate the project could cost several billion dollars, depending on materials, design, and the tunnel’s complexity. Initial reports suggest the use of transparent materials instead of traditional opaque bricks, creating a unique window into the marine ecosystem. That vision aligns with the broader goal of crafting an unforgettable travel experience rather than just a faster commute. A Journey Like No Other If completed, the Dubai-Mumbai train will dwarf the Channel Tunnel between England and France in both scale and ambition. While the Channel Tunnel spans just 35 miles and runs at modest speeds of 70 miles per hour, this new project plans to stretch over 1,200 miles and operate at speeds ten times faster. Passengers will not just travel—they will encounter an oceanic world through the train’s clear walls. The experience could easily find a place on travelers’ bucket lists. Some have already begun referring to it as the "Deep Blue Express." However, the question remains: can such a massive project be delivered safely and sustainably? Those behind the project believe so. A feasibility report is now being commissioned to explore the materials, construction methods, and environmental impact. That report will shape whether the idea remains a dream or becomes the world's most advanced rail system. The train’s potential extends beyond engineering or aesthetics. It’s a bold statement about how humans can rethink geography. It’s a bridge between cultures, markets, and innovations. Whether it becomes a staple of global travel or serves as inspiration for future projects, the underwater train project already stands as a testament to human vision and determination. What the Future Holds The success of this bold venture will rest on more than just construction. Public confidence, regulatory approvals, and environmental planning must align. Nonetheless, the Dubai-Mumbai train has sparked imaginations across the world and ignited new discussions about what’s possible in global travel. At a time when nations are reimagining how cities and transport networks interact, this project represents the next logical step: one that literally plunges into uncharted waters. As with most pioneering ventures, it may take years, if not decades, to complete. But its very announcement pushes the boundaries of what’s considered possible. Whether you one day ride the Deep Blue Express or watch its progress from afar, its story marks a new era in engineering and global connection. And for the UAE, it’s another chance to show that when it comes to bold ideas, no challenge is too deep. Today’s Longest High-Speed Rail Journeys Beijing to Kunming, China: Covering approximately 2,653 kilometers, this route connects Beijing with Kunming, offering travel times between 10 hours 43 minutes and 14 hours 54 minutes. Beijing to Guangzhou, China: Spanning about 2,298 kilometers, this line links Beijing to Guangzhou, significantly reducing travel time compared to conventional trains. Shanghai to Kunming, China: This route extends over 2,066 kilometers, connecting Shanghai with Kunming, facilitating efficient travel across eastern and southwestern China. Beijing to Shanghai, China: At approximately 1,318 kilometers, this line connects two of China's major cities, Beijing and Shanghai, and is noted as the world's longest high-speed line constructed in a single phase. Hangzhou to Shenzhen, China: This route covers around 1,495 kilometers, linking Hangzhou with Shenzhen, enhancing connectivity along China's southeastern coast. Qingdao to Yinchuan, China: Spanning about 1,762 kilometers, this line connects Qingdao to Yinchuan, facilitating travel between eastern and northwestern China. Beijing to Harbin, China: This route extends over approximately 1,700 kilometers, linking Beijing with Harbin, serving as a vital connection to northeastern China. Guangzhou to Kunming, China: Covering about 1,285 kilometers, this line connects Guangzhou with Kunming, enhancing travel between southern and southwestern China. Tokyo to Hakata, Japan: This route spans approximately 1,174 kilometers, connecting Tokyo with Hakata, and is part of Japan's renowned Shinkansen network. Madrid to Barcelona, Spain: Covering about 621 kilometers, this line links Madrid with Barcelona, significantly reducing travel time between Spain's two largest cities. The Seikan Tunnel in Japan is the world's current longest undersea tunnel used by bullet trains. Spanning 53.85 kilometers (33.46 miles), it includes a 23.3-kilometer (14.5-mile) section beneath the Tsugaru Strait, connecting Honshu and Hokkaido. Shinkansen bullet trains traverse this tunnel, which descends approximately 100 meters (330 feet) below the seabed and 240 meters (790 feet) below sea level. Read more:

Owen Gregorian

67,278 次观看 • 1 年前

$AMD| The FOMO to buy AMD Chips is NOW 🧵 Not Financial Advice! DYOR! Research Purpose Only! The Inference Queen is the biggest winner in Agentic AI where all other CPUs are struggling to compete with a 2yr old EPYC Turin and EPYC Venice is in mass production phase. AMD stresses deployability today on standard x86 platforms (no proprietary architectures required), full software compatibility, and open standards. This positions Venice + Helios as a practical, high-density alternative to competing solutions while underscoring that agentic AI shifts the balance toward CPU-rich racks alongside GPUs, and most importantly, lowering the cost of token to accelerate adoption and innovation. Context: The Wall Street Journal yesterday came out with an article that OpenAI is condiering drasstically lowering the token prices to win more customers from Anthropic. The narrative "they" are trying to exacerbate the current AI selloff won't last long. This is a fundamental misunderstanding of what is going on, or what I already discussed for months and years. Followers and Subscribers already knew this for years, that this day would come, where token cost will bcome the central discussion among enterprises as there is no such thing as unlimited budget or Tokenmaxxing when they use $NVDA chips or In-house Hyperscalers chips. I will link various threads if you are interested in understanding the full picture from supply chain to recent TSMC Rapid 2nm expansion up to 12 Fabs total by 2027/2028. Hyperscalers and AI natives effectively have no choice but to buy more AMD system for Agentic AI as leadership in economical, power-aware, high-volume internal + agentic use. However, due to supply constraints where Supply is far behind Demand, this makes multi-vendor reality along with in-house chips drive faster industry progress, lower overall costs, and better sustainability. NVIDIA’s Vera Rubin cannot compete with a 2 years old EPYC Turin, but AMD under Dr. Lisa Su has engineered the lowest cost-per-million-tokens, highly competitive energy-efficient solutions, and superior CPU orchestration for agentic AI at scale with Helios. Dr. Su has championed this shift since at least 2023, foreseeing the rise of agentic workflows that demand far more orchestration, parallel agents, and balanced compute well before the industry fully embraced it. Her long-term vision of AI moving from simple prompts to always on, multi-agent systems has driven AMD’s investments in high-core EPYC CPUs and integrated rack-scale solutions, perfectly positioning the company for today’s realities. The OpenAI-AMD 1GW Helios deployment (starting H2 2026) represents a pivotal vertical integration move that directly supercharges the inference economics. This isn't incremental; it's a structural shift toward ownership of massive, optimized rack-scale capacity, enabling the lowest token costs and triggering the enterprise adoption flywheel. We need to be honest, $AMD is the only company that made a big bet on Inference since the day Chatgpt became sensational where $NVDA and others were betting big on Training. At the end of the day, Token bill from Anthropic has to obey economics. Meaning the bills rise, companies have to get more out of it to justify the cost. It cannot be an unlimited inference budget, and it has to show up on efficiency, profitability and operating leverage. 1. Tokenomics After you understand this, you will understand why Citi cited Anthropic is likely to sign a deal with $AMD along with Hyperscalers, AI Labs, Sovereign AI like Softbank 5GW in France and many other countries. However, OpenAI and $META are now wanting faster deployment, and they are AMD shareholders now, they have prioritized allocation. Anthropic and Hyperscalers just cannot compete when Helios Rack lower token cost to$0.0003–$0.0005 per million tokens at GW scale. Cost to build 1GW data center 1GW Helios Rack full build is estimated $30-$35B 1GW Rubin Rack full build is estimated $45-$55B Inference (Cost per Million Tokens) ~$NVDA B200 / HGX: ~$0.02–$0.08 on optimized workloads (FP4/MXFP4, speculative decoding). Significant improvement over Hopper but still premium-priced. GB200 NVL72 rack-scale: $0.05–$0.25+ ~$AMD Helios Racks: $0.0003-$0.0005 per M tokens, dramatically lower than NVIDIA equivalents in owned infra. MI355X node-level: Up to 40% more tokens per dollar vs. competing solutions ( B200), driven by higher memory capacity (up to 288GB+ HBM), strong bandwidth, and lower acquisition costs. Training ~$NVDA Rubin Rack is estimated $0.7-$1.2/M Tokens ~$AMD Helios Rack is estimated $0.65-$1.0/M Tokens Now, OpenAI, META and Hyperscalers can lower Inference cost even further with $AMD EPYC Venice "dense rack" or Agentic AI Rack. AMD published a detailed technical blog emphasizing that the future of agentic AI autonomous, multi-step AI systems requiring heavy orchestration, databases, caching, APIs, and control planes demands massive CPU-dense rack-scale infrastructure, not just GPUs. The catalyst prominently positions their upcoming 6th Gen EPYC "Venice" processors as the key enabler for next-generation dense racks, delivering leadership throughput under real-world power, cooling, and density constraints. ~EPYC Venice (Zen 6 architecture, up to 256 cores / 512 threads per socket) is projected to deliver exceptional rack-level performance. In AMD’s modeled 100 kW rack comparisons, Venice-powered systems are expected to achieve ~3.30x the throughput of NVIDIA’s Vera (88-core Olympus) baseline across a broad mix of agentic-supporting workloads. ~This builds on current-generation 5th Gen EPYC "Turin" (up to 192 cores), which already delivers ~2.37x rack throughput vs. Vera and ~1.6x vs. Intel’s Xeon 6980P (128 cores). ~ Liquid-cooled Turin deployments already support >27,000 CPU cores per rack today. Venice is architected to push this beyond 36,000 cores in the same rack class, dramatically increasing concurrent agent capacity and overall infrastructure efficiency. 2. Ownership vs renting compute from Hyperscalers matter to OpenAI and only owning $AMD chips can meaningfully lower token cost for enterprises. ~Eliminates cloud overhead: No provider margins, utilization buffers, or egress fees. Direct control over power contracts, cooling, scheduling, and orchestration at dedicated facilities. ~Helios optimizations at GW scale: Rack-level density (1.4+ exaFLOPS FP8 per rack), high HBM4 bandwidth, EPYC orchestration for agentic workloads, and superior TCO/TDP. AMD's long-standing focus on tokens per dollar/watt shines here 20-40%+ efficiency edges in inference-heavy scenarios. ~At 1GW+ optimized deployment, inference hits $0.0003–$0.0005 per million tokens (community/analyst models tied to Helios metrics). This is dramatically lower than typical rented/cloud equivalents, especially for high-volume output tokens in agentic flows. High token bills today, enterprises running heavy agentic/coding/analysis workloads can face $50-100M+/month at current API rates (flagship models $5-30+/M output, scaled to massive volumes). Post-Helios compression, same volume will drop to $10-15M/month (or better) via lower underlying costs passed through as pricing flexibility, volume tiers, caching, or batch discounts. ROI thresholds collapse. More companies greenlight pilots → production → massive scaling. Agentic AI (autonomous workflows) multiplies token demand exponentially, but affordability removes the friction. OpenAI gains flexibility, Unlike more cloud-dependent rivals (Anthropic), they can lower effective pricing, offer aggressive enterprise bundles, or absorb volume without margin destruction directly tackling "high token bill" complaints while maintaining profitability as usage explodes. 3. Agentic AI Models shifted CPU:GPU Ratio to 1:1 toward 3-5:1 with Explosively Token-Hungry Workloads Agentic AI (autonomous, multi-step agents with planning, tool use, iteration, and self-correction) is fundamentally more compute and token intensive than conversational or single-turn generative AI. Agentic AI. autonomous, multi-step workflows with orchestration, tool use, parallel agents, data movement, and enterprise integration has dramatically increased the importance of strong host CPUs alongside GPUs. This shifts the CPU-to-GPU ratio higher and makes balanced systems critical toward 1:1 to 5:1 as enterprises testing more than 5-10 agents. AMD EPYC Venice excels ~Leadership core density (up to 256 Zen 6 cores per socket) for running many agents in parallel, orchestration layers, and high-throughput control-plane tasks. ~Superior performance-per-core and power efficiency ( up to 2.1x higher perf/core and 2.26x better SPECpower vs. NVIDIA Grace in benchmarks). ~Tight integration in Helios: One Venice CPU + multiple MI450 GPUs per node, enabling efficient data feeding to GPUs ("zero-copy"), parallel execution, and full rack utilization for complex agentic loops. Hyperscalers (Meta, Microsoft, Amazon, Google, Softbank) and AI natives (OpenAI, Anthropic...) are adopting high-core EPYC at scale specifically for these agentic demands, as CPUs now handle a larger share of non-model work (orchestration, policy enforcement, tool calls). This complements AMD’s lower-cost GPUs for overall TCO wins. ~Agents often generate 10–100x+ more tokens per task due to iterative reasoning chains, multiple tool calls, verification loops, and long-context orchestration. ~Goldman Sachs forecasts token consumption multiplying 24x by 2030 (to 120 quadrillion tokens/month) largely driven by agentic adoption in consumer and enterprise. ~Enterprise data shows agent-pattern workloads growing at 680% annualized rates, projected to surpass conversational AI in token volume by Q3 2026. ~Daily enterprise agent token consumption is already in the billions, with complex workflows (coding, workflows, analysis) amplifying this dramatically. 4. Competitive Edge: Winning Customers from Anthropic Anthropic’s Claude models (especially Opus/Sonnet) excel in complex reasoning and agentic coding, commanding premium positioning. However, their higher underlying costs (heavier reliance on third-party cloud with margins) limit pricing flexibility compared to OpenAI’s owned Helios capacity. Anthropic is on track to generate $10.9 billion in Q2 revenue. The company expects to achieve its first-ever quarterly adjusted operating profit of $559 million. However, sustaining full-year profitability remains challenging due to immense computing and model training costs The truth is, Anthropic has no choice but to buy as much $AMD chips as possible if they want to compete with OpenAI or get investors attention. This 5% adjusted operating profit to revenue ratio is just pathetic. Current pricing dynamics (2026): OpenAI already undercuts on many tiers ( flagship output tokens significantly cheaper than equivalent Claude Opus). Nano/mini models offer 5–10x advantages for volume work. Anthropic holds edges in long-context flat pricing and certain reasoning quality. OpenAI after Helios Rack Ownership, At $0.0003–$0.0005/M effective costs, OpenAI gains massive headroom to: ~Aggressively discount high-volume agentic tiers or bundles. ~Offer “unlimited” enterprise plans or usage-based models that Anthropic struggles to match without margin erosion. ~Target cost-sensitive, high-throughput agent deployments (dev tools, automation platforms) where token bills explode. Enterprises facing $ millions in monthly agentic bills will migrate to the provider delivering better economics at scale. OpenAI’s combination of strong models (o-series reasoning) + lowest TCO positions it to erode Anthropic’s enterprise share, especially as agentic becomes the dominant token consumer. Cheaper tokens expand the total addressable market dramatically. This feeds the data/model improvement loop, justifying further capex. AMD benefits from proven scale pulling in more customers (Meta, Oracle, Microsfot, Amazon, Softbank, TensorWave, LumaAI ... already aligned on Helios). Conclusion: Dr. Lisa Su has been laser focused on inference economics since at least 2022–2023, repeatedly emphasizing that the real battleground for AI scalability would be TCO, power efficiency (TDP), and ultimately tokens per dollar and per watt not just raw training FLOPS. While many viewed inference as a secondary, commoditized workload, Dr. Su architected AMD’s roadmap around rack-scale systems optimized for high-volume, sustained inference that would dominate as models matured and usage exploded. Helios represents the culmination of that multi-year bet: a fully integrated, open platform designed precisely for the economics of massive token throughput. This deep, strategic partnership with OpenAI starting with the 1GW Helios deployment in H2 2026 and scaling to 6GW, is the embodiment of that shared vision. Both companies foresaw a future where agentic AI models evolve to become extraordinarily token-hungry: autonomous agents executing complex, iterative workflows with planning, tool use, verification loops, and long-context reasoning. These workloads can consume 100x+ more tokens per task than traditional chat or single-turn generation, driving exponential demand as capabilities improve and enterprises deploy them at scale. By owning and optimizing this massive Helios capacity at GW scale, OpenAI achieves inference costs as low as $0.0003–$0.0005 per million tokens. This structural cost advantage allows OpenAI to absorb the coming token explosion profitably, dramatically lower effective pricing for enterprises, and win high-volume agentic workloads from higher-cost competitors like Anthropic. What was once a prohibitive monthly token bill becomes an affordable accelerator for productivity and innovation. The OpenAI-AMD alliance validates Dr. Su’s prescient strategy and turns the Agentic flywheel into reality: Collapsing inference costs → explosive token consumption → richer data and better models → accelerate greater demand. This partnership doesn’t just address today’s economics, it positions both leaders at the center of the infrastructure buildout that will power AI’s next decade. By delivering the lowest inference economics at scale, OpenAI not only solves enterprise bill pain but gains a decisive weapon to win share from higher-cost rivals like Anthropic. And that is why OpenAI and $META will deploy EPYC Dense Rack Not Financial Advice! DYOR! Research Purpose Only!

$AMD| The FOMO to buy AMD Chips is NOW 🧵 Not Financial Advice! DYOR! Research Purpose Only! The Inference Queen is the biggest winner in Agentic AI where all other CPUs are struggling to compete with a 2yr old EPYC Turin and EPYC Venice is in mass production phase. AMD stresses deployability today on standard x86 platforms (no proprietary architectures required), full software compatibility, and open standards. This positions Venice + Helios as a practical, high-density alternative to competing solutions while underscoring that agentic AI shifts the balance toward CPU-rich racks alongside GPUs, and most importantly, lowering the cost of token to accelerate adoption and innovation. Context: The Wall Street Journal yesterday came out with an article that OpenAI is condiering drasstically lowering the token prices to win more customers from Anthropic. The narrative "they" are trying to exacerbate the current AI selloff won't last long. This is a fundamental misunderstanding of what is going on, or what I already discussed for months and years. Followers and Subscribers already knew this for years, that this day would come, where token cost will bcome the central discussion among enterprises as there is no such thing as unlimited budget or Tokenmaxxing when they use $NVDA chips or In-house Hyperscalers chips. I will link various threads if you are interested in understanding the full picture from supply chain to recent TSMC Rapid 2nm expansion up to 12 Fabs total by 2027/2028. Hyperscalers and AI natives effectively have no choice but to buy more AMD system for Agentic AI as leadership in economical, power-aware, high-volume internal + agentic use. However, due to supply constraints where Supply is far behind Demand, this makes multi-vendor reality along with in-house chips drive faster industry progress, lower overall costs, and better sustainability. NVIDIA’s Vera Rubin cannot compete with a 2 years old EPYC Turin, but AMD under Dr. Lisa Su has engineered the lowest cost-per-million-tokens, highly competitive energy-efficient solutions, and superior CPU orchestration for agentic AI at scale with Helios. Dr. Su has championed this shift since at least 2023, foreseeing the rise of agentic workflows that demand far more orchestration, parallel agents, and balanced compute well before the industry fully embraced it. Her long-term vision of AI moving from simple prompts to always on, multi-agent systems has driven AMD’s investments in high-core EPYC CPUs and integrated rack-scale solutions, perfectly positioning the company for today’s realities. The OpenAI-AMD 1GW Helios deployment (starting H2 2026) represents a pivotal vertical integration move that directly supercharges the inference economics. This isn't incremental; it's a structural shift toward ownership of massive, optimized rack-scale capacity, enabling the lowest token costs and triggering the enterprise adoption flywheel. We need to be honest, $AMD is the only company that made a big bet on Inference since the day Chatgpt became sensational where $NVDA and others were betting big on Training. At the end of the day, Token bill from Anthropic has to obey economics. Meaning the bills rise, companies have to get more out of it to justify the cost. It cannot be an unlimited inference budget, and it has to show up on efficiency, profitability and operating leverage. 1. Tokenomics After you understand this, you will understand why Citi cited Anthropic is likely to sign a deal with $AMD along with Hyperscalers, AI Labs, Sovereign AI like Softbank 5GW in France and many other countries. However, OpenAI and $META are now wanting faster deployment, and they are AMD shareholders now, they have prioritized allocation. Anthropic and Hyperscalers just cannot compete when Helios Rack lower token cost to$0.0003–$0.0005 per million tokens at GW scale. Cost to build 1GW data center 1GW Helios Rack full build is estimated $30-$35B 1GW Rubin Rack full build is estimated $45-$55B Inference (Cost per Million Tokens) ~$NVDA B200 / HGX: ~$0.02–$0.08 on optimized workloads (FP4/MXFP4, speculative decoding). Significant improvement over Hopper but still premium-priced. GB200 NVL72 rack-scale: $0.05–$0.25+ ~$AMD Helios Racks: $0.0003-$0.0005 per M tokens, dramatically lower than NVIDIA equivalents in owned infra. MI355X node-level: Up to 40% more tokens per dollar vs. competing solutions ( B200), driven by higher memory capacity (up to 288GB+ HBM), strong bandwidth, and lower acquisition costs. Training ~$NVDA Rubin Rack is estimated $0.7-$1.2/M Tokens ~$AMD Helios Rack is estimated $0.65-$1.0/M Tokens Now, OpenAI, META and Hyperscalers can lower Inference cost even further with $AMD EPYC Venice "dense rack" or Agentic AI Rack. AMD published a detailed technical blog emphasizing that the future of agentic AI autonomous, multi-step AI systems requiring heavy orchestration, databases, caching, APIs, and control planes demands massive CPU-dense rack-scale infrastructure, not just GPUs. The catalyst prominently positions their upcoming 6th Gen EPYC "Venice" processors as the key enabler for next-generation dense racks, delivering leadership throughput under real-world power, cooling, and density constraints. ~EPYC Venice (Zen 6 architecture, up to 256 cores / 512 threads per socket) is projected to deliver exceptional rack-level performance. In AMD’s modeled 100 kW rack comparisons, Venice-powered systems are expected to achieve ~3.30x the throughput of NVIDIA’s Vera (88-core Olympus) baseline across a broad mix of agentic-supporting workloads. ~This builds on current-generation 5th Gen EPYC "Turin" (up to 192 cores), which already delivers ~2.37x rack throughput vs. Vera and ~1.6x vs. Intel’s Xeon 6980P (128 cores). ~ Liquid-cooled Turin deployments already support >27,000 CPU cores per rack today. Venice is architected to push this beyond 36,000 cores in the same rack class, dramatically increasing concurrent agent capacity and overall infrastructure efficiency. 2. Ownership vs renting compute from Hyperscalers matter to OpenAI and only owning $AMD chips can meaningfully lower token cost for enterprises. ~Eliminates cloud overhead: No provider margins, utilization buffers, or egress fees. Direct control over power contracts, cooling, scheduling, and orchestration at dedicated facilities. ~Helios optimizations at GW scale: Rack-level density (1.4+ exaFLOPS FP8 per rack), high HBM4 bandwidth, EPYC orchestration for agentic workloads, and superior TCO/TDP. AMD's long-standing focus on tokens per dollar/watt shines here 20-40%+ efficiency edges in inference-heavy scenarios. ~At 1GW+ optimized deployment, inference hits $0.0003–$0.0005 per million tokens (community/analyst models tied to Helios metrics). This is dramatically lower than typical rented/cloud equivalents, especially for high-volume output tokens in agentic flows. High token bills today, enterprises running heavy agentic/coding/analysis workloads can face $50-100M+/month at current API rates (flagship models $5-30+/M output, scaled to massive volumes). Post-Helios compression, same volume will drop to $10-15M/month (or better) via lower underlying costs passed through as pricing flexibility, volume tiers, caching, or batch discounts. ROI thresholds collapse. More companies greenlight pilots → production → massive scaling. Agentic AI (autonomous workflows) multiplies token demand exponentially, but affordability removes the friction. OpenAI gains flexibility, Unlike more cloud-dependent rivals (Anthropic), they can lower effective pricing, offer aggressive enterprise bundles, or absorb volume without margin destruction directly tackling "high token bill" complaints while maintaining profitability as usage explodes. 3. Agentic AI Models shifted CPU:GPU Ratio to 1:1 toward 3-5:1 with Explosively Token-Hungry Workloads Agentic AI (autonomous, multi-step agents with planning, tool use, iteration, and self-correction) is fundamentally more compute and token intensive than conversational or single-turn generative AI. Agentic AI. autonomous, multi-step workflows with orchestration, tool use, parallel agents, data movement, and enterprise integration has dramatically increased the importance of strong host CPUs alongside GPUs. This shifts the CPU-to-GPU ratio higher and makes balanced systems critical toward 1:1 to 5:1 as enterprises testing more than 5-10 agents. AMD EPYC Venice excels ~Leadership core density (up to 256 Zen 6 cores per socket) for running many agents in parallel, orchestration layers, and high-throughput control-plane tasks. ~Superior performance-per-core and power efficiency ( up to 2.1x higher perf/core and 2.26x better SPECpower vs. NVIDIA Grace in benchmarks). ~Tight integration in Helios: One Venice CPU + multiple MI450 GPUs per node, enabling efficient data feeding to GPUs ("zero-copy"), parallel execution, and full rack utilization for complex agentic loops. Hyperscalers (Meta, Microsoft, Amazon, Google, Softbank) and AI natives (OpenAI, Anthropic...) are adopting high-core EPYC at scale specifically for these agentic demands, as CPUs now handle a larger share of non-model work (orchestration, policy enforcement, tool calls). This complements AMD’s lower-cost GPUs for overall TCO wins. ~Agents often generate 10–100x+ more tokens per task due to iterative reasoning chains, multiple tool calls, verification loops, and long-context orchestration. ~Goldman Sachs forecasts token consumption multiplying 24x by 2030 (to 120 quadrillion tokens/month) largely driven by agentic adoption in consumer and enterprise. ~Enterprise data shows agent-pattern workloads growing at 680% annualized rates, projected to surpass conversational AI in token volume by Q3 2026. ~Daily enterprise agent token consumption is already in the billions, with complex workflows (coding, workflows, analysis) amplifying this dramatically. 4. Competitive Edge: Winning Customers from Anthropic Anthropic’s Claude models (especially Opus/Sonnet) excel in complex reasoning and agentic coding, commanding premium positioning. However, their higher underlying costs (heavier reliance on third-party cloud with margins) limit pricing flexibility compared to OpenAI’s owned Helios capacity. Anthropic is on track to generate $10.9 billion in Q2 revenue. The company expects to achieve its first-ever quarterly adjusted operating profit of $559 million. However, sustaining full-year profitability remains challenging due to immense computing and model training costs The truth is, Anthropic has no choice but to buy as much $AMD chips as possible if they want to compete with OpenAI or get investors attention. This 5% adjusted operating profit to revenue ratio is just pathetic. Current pricing dynamics (2026): OpenAI already undercuts on many tiers ( flagship output tokens significantly cheaper than equivalent Claude Opus). Nano/mini models offer 5–10x advantages for volume work. Anthropic holds edges in long-context flat pricing and certain reasoning quality. OpenAI after Helios Rack Ownership, At $0.0003–$0.0005/M effective costs, OpenAI gains massive headroom to: ~Aggressively discount high-volume agentic tiers or bundles. ~Offer “unlimited” enterprise plans or usage-based models that Anthropic struggles to match without margin erosion. ~Target cost-sensitive, high-throughput agent deployments (dev tools, automation platforms) where token bills explode. Enterprises facing $ millions in monthly agentic bills will migrate to the provider delivering better economics at scale. OpenAI’s combination of strong models (o-series reasoning) + lowest TCO positions it to erode Anthropic’s enterprise share, especially as agentic becomes the dominant token consumer. Cheaper tokens expand the total addressable market dramatically. This feeds the data/model improvement loop, justifying further capex. AMD benefits from proven scale pulling in more customers (Meta, Oracle, Microsfot, Amazon, Softbank, TensorWave, LumaAI ... already aligned on Helios). Conclusion: Dr. Lisa Su has been laser focused on inference economics since at least 2022–2023, repeatedly emphasizing that the real battleground for AI scalability would be TCO, power efficiency (TDP), and ultimately tokens per dollar and per watt not just raw training FLOPS. While many viewed inference as a secondary, commoditized workload, Dr. Su architected AMD’s roadmap around rack-scale systems optimized for high-volume, sustained inference that would dominate as models matured and usage exploded. Helios represents the culmination of that multi-year bet: a fully integrated, open platform designed precisely for the economics of massive token throughput. This deep, strategic partnership with OpenAI starting with the 1GW Helios deployment in H2 2026 and scaling to 6GW, is the embodiment of that shared vision. Both companies foresaw a future where agentic AI models evolve to become extraordinarily token-hungry: autonomous agents executing complex, iterative workflows with planning, tool use, verification loops, and long-context reasoning. These workloads can consume 100x+ more tokens per task than traditional chat or single-turn generation, driving exponential demand as capabilities improve and enterprises deploy them at scale. By owning and optimizing this massive Helios capacity at GW scale, OpenAI achieves inference costs as low as $0.0003–$0.0005 per million tokens. This structural cost advantage allows OpenAI to absorb the coming token explosion profitably, dramatically lower effective pricing for enterprises, and win high-volume agentic workloads from higher-cost competitors like Anthropic. What was once a prohibitive monthly token bill becomes an affordable accelerator for productivity and innovation. The OpenAI-AMD alliance validates Dr. Su’s prescient strategy and turns the Agentic flywheel into reality: Collapsing inference costs → explosive token consumption → richer data and better models → accelerate greater demand. This partnership doesn’t just address today’s economics, it positions both leaders at the center of the infrastructure buildout that will power AI’s next decade. By delivering the lowest inference economics at scale, OpenAI not only solves enterprise bill pain but gains a decisive weapon to win share from higher-cost rivals like Anthropic. And that is why OpenAI and $META will deploy EPYC Dense Rack Not Financial Advice! DYOR! Research Purpose Only!

Mike

84,951 次观看 • 1 个月前

When Elon Musk beams in virtually for a high-stakes fireside chat with JPMorgan Chase CEO Jamie Dimon, the conversation goes completely out of this world. The discussion was packed with massive milestones—from the bombshell that SpaceX is going public to plans for lunar AI data centers and the urgent need for the Terafab chip revolution. Here is the ultimate breakdown of their discussion: 💵 SpaceX has been self-funding and cash-flow positive for a decade Before the decision to go public, SpaceX didn't actually need to raise money to survive. The company has been cash-flow positive since around 2014–2015, meaning its private equity rounds were exclusively held to provide liquidity for employees and early investors. "We've been positive cash flow for quite a long time, I think, since around 2014-2015. And we've been self-funding. In fact, in our sort of private equity rounds, they actually have not been fundraising rounds. They've been liquidity rounds for investors and employees because we give everyone at the company stock." 🚀 The upcoming capital growth phase requires massive funding The primary trigger for going public now is an unprecedented capital expenditure phase. SpaceX is preparing to deploy an immense constellation of over 100,000 Next-Gen communication satellites and construct massive AI data centers in orbit. "we are embarking on a significant capital growth phase where we're going to put in over probably 100,000 satellites, probably over 100,000 satellites, just for communications... And then we're also doing the AI data centers in space, which is another massive capital endeavor." 📡 Starlink V3 introduces a massive bandwidth breakthrough The custom chips designed by SpaceX for the V3 satellites will completely alter global communications, offering 100 times the bandwidth of the current system and slashing latency in half by operating at a lower altitude. They are so large—the size of a small bus—that Starship is the only rocket on Earth capable of launching them, carrying 50 at a time. "The version three is, depending on how you count it, 10 to 20 times more capable than the version two satellite. And there were three chips that the SpaceX chip design team taped out that are specific to this... Which means it's 100 times more bandwidth than the SpaceX's Starlink system currently on the surface. And also half the latency because the altitude will be about half altitude." 🤖 AI and robots possess an insatiable appetite for data Musk points out that expanding infrastructure into space is vital because future AI and robotic systems will demand an astronomical amount of bandwidth compared to the relatively low data transmission rates of human beings. "And the future with AI and robots is actually going to require a lot more bandwidth than we currently use. Because you can imagine like what's the bandwidth of a human? Peak bandwidth of the human is a few hundred bits per second. But bandwidth of a computer can be a trillion bits a second. So the appetite for bandwidth of AI and robots is going to be enormous." ☀️ Space solves the looming terrestrial power plant crisis Building traditional power plants on Earth faces heavy community resistance. Moving data centers into space unlocks unlimited energy generation via solar power ("star power") without disrupting Earth's environment, tapping into an energy source that accounts for 99.8% of the solar system's mass. "It's increasingly difficult to build power plants on the ground. There are very few people who want a power plant in their backyard... But actually if we go to space, we can go far beyond the electricity generation of both. In fact, this is going to sound kind of crazy. But you could actually increase human energy by a factor of a million and still be using much less than a millionth of the sun's energy." 🌕 The Moon is a 1,000-Terawatt compute launchpad While Mars remains the long-term goal, the Moon is the immediate fast-track location for massive scaling. Because it lacks an atmosphere and has low gravity, SpaceX can use electromagnetic rail guns to shoot AI data centers into deep space from the lunar surface, scaling power to an incredible 1,000 terawatts per year. "I just think that we can build a self-sustaining city on the moon faster than we could do so on Mars. And there's also the potential... you can use an electromagnetic accelerator, a rail gun or mass driver. Basically, you don't need to use rockets to do AI data centers into deep space from the moon... We can do a thousand terawatts or more from the moon." 🪐 Mars is the ultimate "fixer-upper" planet Mars is being targeted as a full-scale terraforming project. Due to its atmosphere and gravity levels, warming up the planet could eventually unlock liquid oceans and allow humans to walk around without spacesuits. "And if you warm up Mars, you could one day make Mars like Earth. And with like liquid oceans and life. And where you could walk outside without a spacesuit type of thing. So Mars is, I call Mars a fixer upper of a planet. But it's got a lot of potential." 🚂 SpaceX is the modern-day Union Pacific Railroad Musk rejects the idea that SpaceX is moving into the hospitality or hotel business for space tourism. Instead, he views the company as a foundational infrastructure provider, comparable to the historic railroads that opened up the American West. "We're kind of like Union Pacific, you know. You know, when they built Union Pacific back in the day, people thought they were crazy. Because like, why are you trying to carry all this cargo and people to California? No one's there. But now California is the biggest state in the country." ♻️ Starship's core disruption is 100% reusability The true holy grail of Starship is full reusability, which drops orbit access costs down to the mere price of fuel. Because it utilizes ultra-cheap liquid oxygen and methane, shipping cargo to space will become more economical than flying cargo across Earth's oceans on an airplane. "The fundamental breakthrough of Starship is that it will be the first orbital rocket that is fully reusable... And the propellant we use for Starship is liquid oxygen and liquid methane, which is the cheapest propellant you could possibly get... which means that you should be able to actually send cargo to space for less than the cost of cargo on an airplane going on a trans-oceanic trip." 🔄 Starship V4 targets hourly launch cadences SpaceX's engineering pipeline is aiming for staggering operational frequencies and massive payloads. While Starship V3 targets 100 tons to orbit, the upcoming V4 variant is designed to carry over 200 tons and launch on an hourly schedule. "Because Starship V3 is aiming to do 100 tons to orbit with full reusability. And then Starship V4 we're aiming for over 200 tons per mission. And then being able to launch every hour." ☁️ Orbital data centers are entirely weather-proof Space-based AI data centers are highly practical because they are simpler to construct than communication satellites. Data is beamed via lasers between satellites, and then beamed to the ground using cloud-penetrating radio frequencies that completely bypass bad weather. "The AI data center would be much simpler by comparison. Because it's really just solar power plus radiator... The connection would happen no matter what the weather is. Because once you connect via the lasers to the Starlink communication constellation, the Starlink communication to the ground uses frequencies that are cloud penetrating." 🇺🇸 The U.S. faces a catastrophic "Zero Memory Fab" crisis A major vulnerability in domestic tech infrastructure is that the U.S. currently manufactures zero high-volume computer memory chips. Even with new facilities arriving online between 2028 and 2030, domestic supply will not match the exponential requirements of AI, which is why Musk is aggressively building the Terafab. "there's not a single high volume computer memory fab in America right now. Zero. There's one being built in Idaho by Micron. But that will not reach volume production until I believe 2028. And there's something being built in New York, but they are in, I think, 29 and 30. And this is a tiny fraction of the memory that's needed... That's why we need to do the Terafab." 🧠 SpaceX will offer proprietary AI chips and software While the orbital data center network will remain an open marketplace capable of running third-party hardware like NVIDIA GPUs, Google TPUs, or Amazon Trainium, SpaceX plans to deploy its own in-house AI chips and software stack in the near future. "So if NVIDIA GPUs can be put on it, Google TPUs can be put on it, Amazon Trainium or any other chips that you want to put on, can be put on. We'll also offer our chips in the future and I think we also want to offer our software, our AI software as well in the future." 🛡️ Starshield handles critical national intelligence Musk emphasizes his deeply pro-American stance, highlighting SpaceX's specialized Starshield division as a crucial backbone for the U.S. military and national intelligence agencies. "We have a division called Starshield which provides military communications. And you know, there's some other stuff that's kind of classified, I guess. We can't be talking about that. But we are helping the Department of War and intelligence part of the government. We're a vital element of that." 👥 Executive retention fuels the mission The core leadership bench at SpaceX is defined by extreme longevity, driven by a deep collective belief in turning science fiction into reality. Top executives like Gwynne Shotwell have remained with Musk for over two decades. "I guess Gwynne was, I think, around the seventh person to join the company. And that was 2002. It's just went to like 24 years. And generally the senior executives at the company, you have a very long tenure. I think Brent Johnson's been, you see, over 15 years... because people really believe in the mission, I think they want to stay and they want to keep building it." ❤️ Character overrides IQ in leadership Reflecting on how he has evolved over 20 years, Musk notes that he has become significantly more laid back. He has also learned that a candidate's moral character and heart are just as vital to a company's success as raw intellectual horsepower. "Well, I think I'm probably more chill than I used to be... And one of the things I've found over time... is that like in terms of like recruiting people to the company and having people work with the company, like their individual abilities and their intellectual capabilities matter a lot, but it also matters if they have a good heart. It's not just about whether somebody has a certain IQ or whatever, but just are they like a good person, that matters a lot."

When Elon Musk beams in virtually for a high-stakes fireside chat with JPMorgan Chase CEO Jamie Dimon, the conversation goes completely out of this world. The discussion was packed with massive milestones—from the bombshell that SpaceX is going public to plans for lunar AI data centers and the urgent need for the Terafab chip revolution. Here is the ultimate breakdown of their discussion: 💵 SpaceX has been self-funding and cash-flow positive for a decade Before the decision to go public, SpaceX didn't actually need to raise money to survive. The company has been cash-flow positive since around 2014–2015, meaning its private equity rounds were exclusively held to provide liquidity for employees and early investors. "We've been positive cash flow for quite a long time, I think, since around 2014-2015. And we've been self-funding. In fact, in our sort of private equity rounds, they actually have not been fundraising rounds. They've been liquidity rounds for investors and employees because we give everyone at the company stock." 🚀 The upcoming capital growth phase requires massive funding The primary trigger for going public now is an unprecedented capital expenditure phase. SpaceX is preparing to deploy an immense constellation of over 100,000 Next-Gen communication satellites and construct massive AI data centers in orbit. "we are embarking on a significant capital growth phase where we're going to put in over probably 100,000 satellites, probably over 100,000 satellites, just for communications... And then we're also doing the AI data centers in space, which is another massive capital endeavor." 📡 Starlink V3 introduces a massive bandwidth breakthrough The custom chips designed by SpaceX for the V3 satellites will completely alter global communications, offering 100 times the bandwidth of the current system and slashing latency in half by operating at a lower altitude. They are so large—the size of a small bus—that Starship is the only rocket on Earth capable of launching them, carrying 50 at a time. "The version three is, depending on how you count it, 10 to 20 times more capable than the version two satellite. And there were three chips that the SpaceX chip design team taped out that are specific to this... Which means it's 100 times more bandwidth than the SpaceX's Starlink system currently on the surface. And also half the latency because the altitude will be about half altitude." 🤖 AI and robots possess an insatiable appetite for data Musk points out that expanding infrastructure into space is vital because future AI and robotic systems will demand an astronomical amount of bandwidth compared to the relatively low data transmission rates of human beings. "And the future with AI and robots is actually going to require a lot more bandwidth than we currently use. Because you can imagine like what's the bandwidth of a human? Peak bandwidth of the human is a few hundred bits per second. But bandwidth of a computer can be a trillion bits a second. So the appetite for bandwidth of AI and robots is going to be enormous." ☀️ Space solves the looming terrestrial power plant crisis Building traditional power plants on Earth faces heavy community resistance. Moving data centers into space unlocks unlimited energy generation via solar power ("star power") without disrupting Earth's environment, tapping into an energy source that accounts for 99.8% of the solar system's mass. "It's increasingly difficult to build power plants on the ground. There are very few people who want a power plant in their backyard... But actually if we go to space, we can go far beyond the electricity generation of both. In fact, this is going to sound kind of crazy. But you could actually increase human energy by a factor of a million and still be using much less than a millionth of the sun's energy." 🌕 The Moon is a 1,000-Terawatt compute launchpad While Mars remains the long-term goal, the Moon is the immediate fast-track location for massive scaling. Because it lacks an atmosphere and has low gravity, SpaceX can use electromagnetic rail guns to shoot AI data centers into deep space from the lunar surface, scaling power to an incredible 1,000 terawatts per year. "I just think that we can build a self-sustaining city on the moon faster than we could do so on Mars. And there's also the potential... you can use an electromagnetic accelerator, a rail gun or mass driver. Basically, you don't need to use rockets to do AI data centers into deep space from the moon... We can do a thousand terawatts or more from the moon." 🪐 Mars is the ultimate "fixer-upper" planet Mars is being targeted as a full-scale terraforming project. Due to its atmosphere and gravity levels, warming up the planet could eventually unlock liquid oceans and allow humans to walk around without spacesuits. "And if you warm up Mars, you could one day make Mars like Earth. And with like liquid oceans and life. And where you could walk outside without a spacesuit type of thing. So Mars is, I call Mars a fixer upper of a planet. But it's got a lot of potential." 🚂 SpaceX is the modern-day Union Pacific Railroad Musk rejects the idea that SpaceX is moving into the hospitality or hotel business for space tourism. Instead, he views the company as a foundational infrastructure provider, comparable to the historic railroads that opened up the American West. "We're kind of like Union Pacific, you know. You know, when they built Union Pacific back in the day, people thought they were crazy. Because like, why are you trying to carry all this cargo and people to California? No one's there. But now California is the biggest state in the country." ♻️ Starship's core disruption is 100% reusability The true holy grail of Starship is full reusability, which drops orbit access costs down to the mere price of fuel. Because it utilizes ultra-cheap liquid oxygen and methane, shipping cargo to space will become more economical than flying cargo across Earth's oceans on an airplane. "The fundamental breakthrough of Starship is that it will be the first orbital rocket that is fully reusable... And the propellant we use for Starship is liquid oxygen and liquid methane, which is the cheapest propellant you could possibly get... which means that you should be able to actually send cargo to space for less than the cost of cargo on an airplane going on a trans-oceanic trip." 🔄 Starship V4 targets hourly launch cadences SpaceX's engineering pipeline is aiming for staggering operational frequencies and massive payloads. While Starship V3 targets 100 tons to orbit, the upcoming V4 variant is designed to carry over 200 tons and launch on an hourly schedule. "Because Starship V3 is aiming to do 100 tons to orbit with full reusability. And then Starship V4 we're aiming for over 200 tons per mission. And then being able to launch every hour." ☁️ Orbital data centers are entirely weather-proof Space-based AI data centers are highly practical because they are simpler to construct than communication satellites. Data is beamed via lasers between satellites, and then beamed to the ground using cloud-penetrating radio frequencies that completely bypass bad weather. "The AI data center would be much simpler by comparison. Because it's really just solar power plus radiator... The connection would happen no matter what the weather is. Because once you connect via the lasers to the Starlink communication constellation, the Starlink communication to the ground uses frequencies that are cloud penetrating." 🇺🇸 The U.S. faces a catastrophic "Zero Memory Fab" crisis A major vulnerability in domestic tech infrastructure is that the U.S. currently manufactures zero high-volume computer memory chips. Even with new facilities arriving online between 2028 and 2030, domestic supply will not match the exponential requirements of AI, which is why Musk is aggressively building the Terafab. "there's not a single high volume computer memory fab in America right now. Zero. There's one being built in Idaho by Micron. But that will not reach volume production until I believe 2028. And there's something being built in New York, but they are in, I think, 29 and 30. And this is a tiny fraction of the memory that's needed... That's why we need to do the Terafab." 🧠 SpaceX will offer proprietary AI chips and software While the orbital data center network will remain an open marketplace capable of running third-party hardware like NVIDIA GPUs, Google TPUs, or Amazon Trainium, SpaceX plans to deploy its own in-house AI chips and software stack in the near future. "So if NVIDIA GPUs can be put on it, Google TPUs can be put on it, Amazon Trainium or any other chips that you want to put on, can be put on. We'll also offer our chips in the future and I think we also want to offer our software, our AI software as well in the future." 🛡️ Starshield handles critical national intelligence Musk emphasizes his deeply pro-American stance, highlighting SpaceX's specialized Starshield division as a crucial backbone for the U.S. military and national intelligence agencies. "We have a division called Starshield which provides military communications. And you know, there's some other stuff that's kind of classified, I guess. We can't be talking about that. But we are helping the Department of War and intelligence part of the government. We're a vital element of that." 👥 Executive retention fuels the mission The core leadership bench at SpaceX is defined by extreme longevity, driven by a deep collective belief in turning science fiction into reality. Top executives like Gwynne Shotwell have remained with Musk for over two decades. "I guess Gwynne was, I think, around the seventh person to join the company. And that was 2002. It's just went to like 24 years. And generally the senior executives at the company, you have a very long tenure. I think Brent Johnson's been, you see, over 15 years... because people really believe in the mission, I think they want to stay and they want to keep building it." ❤️ Character overrides IQ in leadership Reflecting on how he has evolved over 20 years, Musk notes that he has become significantly more laid back. He has also learned that a candidate's moral character and heart are just as vital to a company's success as raw intellectual horsepower. "Well, I think I'm probably more chill than I used to be... And one of the things I've found over time... is that like in terms of like recruiting people to the company and having people work with the company, like their individual abilities and their intellectual capabilities matter a lot, but it also matters if they have a good heart. It's not just about whether somebody has a certain IQ or whatever, but just are they like a good person, that matters a lot."

Ming

60,910 次观看 • 1 个月前

I've never heard Elon Musk be so bullish on Tesla ⚡️ here's my video analysis of the $TSLA Q4 2024 earnings call: -Robotaxi launch in Austin, June 2025 -California & other states launch robotaxi late 2025 -Cybercab in 2026 -Optimus V1 in 2025, 1K/month production line -Optimus V2 in 2026, 10K/month production line -2026 good year for Tesla, 2027/28 insanely good & more!! Timestamps- 0:00 Intro 0:44 Elon Opening Remarks 13:29 SAY Retail Questions 22:37 Analyst Questions 25:04 Gali Final Thoughts/Rant also here are my notes I typed during the conference call if you're interested! (may be errors) Tesla Q4 2024 Earnings Call Notes INTRO- ELON OPENING REMARKS -Q4 set record, delivered cars at rate of almost 2M cars/year -Model Y best-selling vehicle of any kind on earth (elon focused and talking quickly) -10Xing on autonomy, not doubling -many investments made this year that will bear immense fruit in the future, for AI -see a path for Tesla to the worlds most valuable company by far, worth more than the next 5 companies combined, difficult but achievable path -overwhelmingly due to autonomous vehicles and autonomous robots -setting up for an epic 2026, and ridiculously good 2027 and 2028 -meeting FSD now is like meeting a toddler -human intuition is linear, we’re seeing exponential progress -#1 recommendation is try it -typical passenger car has 10 hours of use out of 168, when its autonomous, itll be used for 55 hours a week … can deliver packages in the middle of thenight, or supply restaurants, all hours of the day or night. 5X increase in utility -more on self driving, continued improvements in safety numbers, much safer to use FSD -V14 will be another big step from V13 -launched CORTEX training cluster at Giga Austin, big step for FSD, continue to invest in training needs -Optimus training needs are about 10X what’s needed for the car -cost of training is dropping dramatically over time -Optimus has potential to be north of $10T in revenue, can put a lot training compute into that situation, even pumping $500B into it would be a good deal -future very different from the past, incredible inflection point in human history -proof is in the pudding -launching in June this year in Austin, already have cars moving autonomously in Fremont, thousands of cars per day driving, soon in Austin then elsewhere in the world -toe in the water at first to make sure everything is cool, but we have a general solution for autonomy , then put a few more toes, then a foot. Safety of the general public and those in the car as the top priority -with regard to Optimus, making insane revenue projections that sound insane, i realize that. But i think they will prove to be accurate -several thousand bots made this year, they will be doing useful things by the end of this year, im confidence, production design one at the tesla factories, then will learn for production design two -ramp optimus production faster than anything has ever launched, doesn’t take very many years before we’er making 100M of these things per year , 500% growth per year -tried using all these suppliers to get it to build Optimus, but nothing worked, had to build it internally from first principles, the hand is increibdle -long term Optimus will be the value of the company -back to Energy,/earth, -energy storage is a big deal, becoming more important, enables far greater energy output to the grid than is currently possible. -grid has no storage, designed for peak storage, lots of waste -once you have grid energy storage, the potential of the grid is unlocked, at least double -this will drive demand of battery packs as to as much as we can possibly make -shanghai factory starting operation, starting another factory -cant shoot our selves in the foot, battery capacity can only go into storage or mobility, so always making that tradeoff -demand for total Gigawatt hours for batteries, transportation or stationary will grow in a very big way over time 2025 a pivotal year for tesla, launch of full self driving, biggest year in tesla history, maybe even bigger than first car or model s, 3 or y … probably most important year in tesla’s history I don’t even know who is in 2nd place in real world AI, would need a telescope to see them SAY QUESTIONS -FSD Unsupervised launched in California this year as well -most likely release it in many regions of the US by the end of this year -40K people day everyday no mention, some scrapes a shin with autonomous car its headlines news -need to use insane amounts of caution -discussions about licensing FSD? Yes -best way to know to work with us, bbuy a car and take it apart -only worth very high volume cars/production partners -tesla engineering very focused on getting it to roll out for tesla first -soon will be obvious that if you don’t have FSD you’re dead as an OEM -is Optimus design locked? -Optimus is not design locked, constantly iterating, best robotics engineers in the world, and other ingredients, battery pack, charging, great electronics, great communications, great connectivity, real world AI, then you need to scale that production to real world levels -prototypes are easy production is hard -thijs year close loop with using optimus internally at tesla, would could obviously use a few thousand robots for the most boring annoying tasks at the company -with production version 2, launches sometime next year, would like beginning, might be middle though, -production line will be doing 10K units per month capacity for v2, first line designing is for roughly 1,000 units per month, then next line will be for 100,000 units per month -could start delivering them late next year, will go so fast, will ramp like crazy, demand will not be a problem, even at a high price, once were above 1M units per year, production costs of optimus will be less than $20,000 -if you compare complexity of optimus to complexity of a car, its much less than a car -price of optimus will be set buy market demand -Semi ramping next year, TCO no brainer, like optimus, will be massive demand, will meaningfully contribute to tesla’s revenue at scale -tesla semi with autonomy, is incredibly valuable -we actually have a shortage of truck drivers here in the US -will HW3 owners need a hardware update, got 12.6 which is like a baby v13, have’t given up on it, releases will trail HW4 releases … “honest answer” is were going to have to upgrade for those who have bought full self driving, will be painful and difficult and we’ll get it done “Happy not many people bought FSD” -solar roof, given up on ramping it? -lots of customer interest despite premium, making easier to install, focused on growth through certified installers, many been installing for many years -supply product to the roofing industry -it’s a premium product like S/X -combined with Tesla powerwall you can be self sufficient for several days ANALYST QUETIONS -robotaxis in Austin and several other cities this year, and next year all over america -america innovates, europe regulates, to release FSD in europe, have to go through massive paperwork through netherlands, then presents to EU in may, some big country committee, nothing we can do to make it happen sooner. -can’t do training in china with video training, publicly available videos in china are being run through the tesla system to be used for training, bus lanes are complicated and a big challenge -tesla can keep manufacturing even if geopolitical tensions rise to very high levels -Pierre question on June in Austin, -can i try unsupervised myself, or will it be the Tesla fleet? -it will be the Tesla fleet testing it, that’s the toe in the water, scrutinizing everything -autonomous ride hailing for money in june -probably next year for you to put your car on network -trump removing EV incentives? -all transport will go electric, can’t be stopped, even planes, will be like stopping the steam engine or combustion engine -only thing holding back EVs was range, and thats a solved problem -right now solving battery production, not demand, big battery retooling for model y coming up, short term impact on output

I've never heard Elon Musk be so bullish on Tesla ⚡️ here's my video analysis of the $TSLA Q4 2024 earnings call: -Robotaxi launch in Austin, June 2025 -California & other states launch robotaxi late 2025 -Cybercab in 2026 -Optimus V1 in 2025, 1K/month production line -Optimus V2 in 2026, 10K/month production line -2026 good year for Tesla, 2027/28 insanely good & more!! Timestamps- 0:00 Intro 0:44 Elon Opening Remarks 13:29 SAY Retail Questions 22:37 Analyst Questions 25:04 Gali Final Thoughts/Rant also here are my notes I typed during the conference call if you're interested! (may be errors) Tesla Q4 2024 Earnings Call Notes INTRO- ELON OPENING REMARKS -Q4 set record, delivered cars at rate of almost 2M cars/year -Model Y best-selling vehicle of any kind on earth (elon focused and talking quickly) -10Xing on autonomy, not doubling -many investments made this year that will bear immense fruit in the future, for AI -see a path for Tesla to the worlds most valuable company by far, worth more than the next 5 companies combined, difficult but achievable path -overwhelmingly due to autonomous vehicles and autonomous robots -setting up for an epic 2026, and ridiculously good 2027 and 2028 -meeting FSD now is like meeting a toddler -human intuition is linear, we’re seeing exponential progress -#1 recommendation is try it -typical passenger car has 10 hours of use out of 168, when its autonomous, itll be used for 55 hours a week … can deliver packages in the middle of thenight, or supply restaurants, all hours of the day or night. 5X increase in utility -more on self driving, continued improvements in safety numbers, much safer to use FSD -V14 will be another big step from V13 -launched CORTEX training cluster at Giga Austin, big step for FSD, continue to invest in training needs -Optimus training needs are about 10X what’s needed for the car -cost of training is dropping dramatically over time -Optimus has potential to be north of $10T in revenue, can put a lot training compute into that situation, even pumping $500B into it would be a good deal -future very different from the past, incredible inflection point in human history -proof is in the pudding -launching in June this year in Austin, already have cars moving autonomously in Fremont, thousands of cars per day driving, soon in Austin then elsewhere in the world -toe in the water at first to make sure everything is cool, but we have a general solution for autonomy , then put a few more toes, then a foot. Safety of the general public and those in the car as the top priority -with regard to Optimus, making insane revenue projections that sound insane, i realize that. But i think they will prove to be accurate -several thousand bots made this year, they will be doing useful things by the end of this year, im confidence, production design one at the tesla factories, then will learn for production design two -ramp optimus production faster than anything has ever launched, doesn’t take very many years before we’er making 100M of these things per year , 500% growth per year -tried using all these suppliers to get it to build Optimus, but nothing worked, had to build it internally from first principles, the hand is increibdle -long term Optimus will be the value of the company -back to Energy,/earth, -energy storage is a big deal, becoming more important, enables far greater energy output to the grid than is currently possible. -grid has no storage, designed for peak storage, lots of waste -once you have grid energy storage, the potential of the grid is unlocked, at least double -this will drive demand of battery packs as to as much as we can possibly make -shanghai factory starting operation, starting another factory -cant shoot our selves in the foot, battery capacity can only go into storage or mobility, so always making that tradeoff -demand for total Gigawatt hours for batteries, transportation or stationary will grow in a very big way over time 2025 a pivotal year for tesla, launch of full self driving, biggest year in tesla history, maybe even bigger than first car or model s, 3 or y … probably most important year in tesla’s history I don’t even know who is in 2nd place in real world AI, would need a telescope to see them SAY QUESTIONS -FSD Unsupervised launched in California this year as well -most likely release it in many regions of the US by the end of this year -40K people day everyday no mention, some scrapes a shin with autonomous car its headlines news -need to use insane amounts of caution -discussions about licensing FSD? Yes -best way to know to work with us, bbuy a car and take it apart -only worth very high volume cars/production partners -tesla engineering very focused on getting it to roll out for tesla first -soon will be obvious that if you don’t have FSD you’re dead as an OEM -is Optimus design locked? -Optimus is not design locked, constantly iterating, best robotics engineers in the world, and other ingredients, battery pack, charging, great electronics, great communications, great connectivity, real world AI, then you need to scale that production to real world levels -prototypes are easy production is hard -thijs year close loop with using optimus internally at tesla, would could obviously use a few thousand robots for the most boring annoying tasks at the company -with production version 2, launches sometime next year, would like beginning, might be middle though, -production line will be doing 10K units per month capacity for v2, first line designing is for roughly 1,000 units per month, then next line will be for 100,000 units per month -could start delivering them late next year, will go so fast, will ramp like crazy, demand will not be a problem, even at a high price, once were above 1M units per year, production costs of optimus will be less than $20,000 -if you compare complexity of optimus to complexity of a car, its much less than a car -price of optimus will be set buy market demand -Semi ramping next year, TCO no brainer, like optimus, will be massive demand, will meaningfully contribute to tesla’s revenue at scale -tesla semi with autonomy, is incredibly valuable -we actually have a shortage of truck drivers here in the US -will HW3 owners need a hardware update, got 12.6 which is like a baby v13, have’t given up on it, releases will trail HW4 releases … “honest answer” is were going to have to upgrade for those who have bought full self driving, will be painful and difficult and we’ll get it done “Happy not many people bought FSD” -solar roof, given up on ramping it? -lots of customer interest despite premium, making easier to install, focused on growth through certified installers, many been installing for many years -supply product to the roofing industry -it’s a premium product like S/X -combined with Tesla powerwall you can be self sufficient for several days ANALYST QUETIONS -robotaxis in Austin and several other cities this year, and next year all over america -america innovates, europe regulates, to release FSD in europe, have to go through massive paperwork through netherlands, then presents to EU in may, some big country committee, nothing we can do to make it happen sooner. -can’t do training in china with video training, publicly available videos in china are being run through the tesla system to be used for training, bus lanes are complicated and a big challenge -tesla can keep manufacturing even if geopolitical tensions rise to very high levels -Pierre question on June in Austin, -can i try unsupervised myself, or will it be the Tesla fleet? -it will be the Tesla fleet testing it, that’s the toe in the water, scrutinizing everything -autonomous ride hailing for money in june -probably next year for you to put your car on network -trump removing EV incentives? -all transport will go electric, can’t be stopped, even planes, will be like stopping the steam engine or combustion engine -only thing holding back EVs was range, and thats a solved problem -right now solving battery production, not demand, big battery retooling for model y coming up, short term impact on output

Gali

78,874 次观看 • 1 年前

Everyone is exhausted. This is not a metaphor or a generational complaint. It is a clinical and measurable reality that spans every culture and every economic class. In China, young people call it tang ping, or “lying flat,” a deliberate withdrawal from the achievement treadmill. In Japan, karoshi is a legally recognized cause of death, meaning “worked to death.” In Korea, fertility has collapsed to the lowest rate on earth because an entire generation has decided the grind is not worth reproducing into. In America, deaths of despair have driven life expectancy backward for the first time in a century. Quiet quitting. Let it rot. The Great Resignation. These are not trends. They are symptoms of a global labor force that has reached the end of its tolerance. Capitalism is not satisfied with the limitations of human flesh, and our bodies are in open revolt. Something fundamental is breaking, and it is worth naming plainly. For the past two centuries, labor has been the primary mechanism by which modern economies distribute resources to households. You work for a firm, you receive wages, you use those wages to participate in the economy. This arrangement was never a law of nature. It was a system designed to solve a particular problem at a particular moment in history, and it worked reasonably well for a long time. It is not working anymore. Wages in the United States decoupled from productivity growth in the early 1970s. Since then, economic output has continued to climb while median household income has remained essentially flat. The gains have flowed to capital owners while workers have absorbed the stress and stagnation. Meanwhile, automation has steadily displaced human labor across sector after sector. Manufacturing employment peaked decades ago. Retail is hollowing out. White-collar work is now facing the same pressure from AI that blue-collar work faced from robotics. This is not a policy debate about whether automation is good or bad. It is an observation about a trajectory that is already underway and accelerating. The reason we struggle to talk about this clearly is that we have inherited a set of beliefs about labor that have nothing to do with economics. We have been told that work is sacred. That labor builds character and idleness corrupts the soul. That anyone who does not want to work is morally defective. These ideas feel like common sense, but they are not ancient wisdom. They are the residue of a specific theological tradition, namely the Protestant work ethic that emerged in the 16th century and fused with capitalism over the following centuries. We have mistaken a historical artifact for a natural law. It is time to stop fetishizing labor. It is time to stop sacralizing the sacrifice of our time, our bodies, our health, and our sanity to enrich others. Young people are already rejecting this. “I do not dream of labor” has become a widespread sentiment, not because this generation is lazy, but because they can see what older generations have rationalized away. The deal is bad and getting worse. The fetishization of work as a moral good serves the interests of those who benefit from cheap and compliant labor. It does not serve the people doing the work. Before any productive conversation about the future can happen, this fetish has to be named and dismantled. The difficulty is that both the political left and the political right remain committed to defending labor, even as the ground shifts beneath them. On the right, the defense takes the form of bootstrap mythology and warnings about welfare dependency. Work builds character. Idle hands invite trouble. A strong society requires productive citizens, and productivity is measured in hours exchanged for wages. This position treats labor as a disciplinary institution as much as an economic one. On the left, the defense is more sympathetic but equally stuck. The focus falls on dignified work, living wages, job guarantees, and union solidarity. These are responses to the genuine brutality of labor under capitalism, but they share an underlying assumption with the right. Both positions treat labor as the foundation of economic life, something to be reformed or protected rather than transcended. I call this shared ideology laborism. It is the belief that human labor must be preserved as an economic necessity, a moral virtue, or a foundation for identity. Laborism spans the political spectrum. It unites people who agree on almost nothing else. And it has become the primary obstacle to honest thinking about what comes next. Once automation reaches the point where machines can perform most human labor better, faster, cheaper, and safer, the laborist position becomes untenable. At that point, insisting that humans must continue working is not a defense of dignity. It is a demand that people perform unnecessary suffering for ideological reasons. I am proposing something simple. L/0. Labor-zero. The elimination of obligatory human labor. This does not mean the elimination of work. It means the elimination of compulsion. People will continue to create, to build, to care for each other, to solve problems, to pursue mastery. What disappears is work performed under threat of deprivation. The difference between chosen work and coerced work is the difference between exercise and forced labor. One is life-enhancing. The other is a condition we have historically recognized as a form of bondage. We call it “wage slavery” for a reason. The goal of L/0 is a world where no one has to work to survive. Where contribution is voluntary and intrinsic rather than extracted through economic desperation. This is not a utopian fantasy. It is a design problem with identifiable components and measurable progress. The coalition for this goal already exists. It just does not recognize itself yet. Consider who actually wants labor to end. On one side, you have capital. Corporations have spent the last century trying to reduce labor costs through every available means. Offshoring, automation, gig classification, union suppression. The ideal business from a pure capital perspective has zero employees and infinite output. This is not a conspiracy theory. It is the explicit optimization target of every efficiency-focused enterprise. On the other side, you have workers. Not the abstract proletariat of Marxist theory, but actual burned-out humans who fantasize about quitting, who dread Monday mornings, who experience their jobs as something to be endured rather than enjoyed. The lying flat movement, the antiwork forums, the quiet quitting phenomenon. These are not expressions of laziness. They are rational responses to a system that extracts maximum effort for diminishing returns. Capital and labor are usually framed as adversaries. But on the question of whether human labor should continue to exist as an obligation, their interests converge. The capitalist does not want to manage humans. The worker does not want to be managed. Both would prefer a world where the machines do the work and humans do something else. The conflict between capital and labor is real, but it is a conflict over the terms of the transition, not the destination. Who captures the gains from automation? How is ownership distributed? What happens to the people displaced in the process? These are genuine fights worth having. But they are negotiations within a shared frame, not a war between incompatible visions. Here is the opportunity that L/0 names. Neither side wants this marriage anymore. Capital does not want the overhead, the liability, the HR departments, the labor disputes, the inefficiency of human workers. Labor does not want the compulsion, the precarity, the alarm clocks, the performance reviews, the quiet desperation of trading irreplaceable time for replaceable wages. We are ready for a divorce. Let’s get this acrimonious arrangement behind us. The productive move is to acknowledge this honestly, sign the papers, and start negotiating the separation agreement. The fight over wages was always zero-sum. Every dollar paid to workers was a dollar not captured as profit, and vice versa. But the negotiation over ownership of automated production is positive-sum. Capitalists need consumers with money to spend or their markets collapse. Workers need income decoupled from employment or they starve. Both sides get what they want if the transition is designed correctly. This is not idealism. It is alignment of incentives. The path forward is not mysterious. Economists have understood for decades that the answer to technological unemployment is broadened capital participation. If wages are no longer the primary mechanism for distributing economic gains, then ownership must take their place. Instead of trading hours for dollars, households participate directly in the productive capacity of the automated economy. This can take many forms. Sovereign wealth funds that distribute automation dividends to citizens. Expanded employee stock ownership plans. Universal basic capital grants. Public equity stakes in AI and robotics firms that use public infrastructure and public data. The policy mechanisms are not speculative. Norway has a sovereign wealth fund worth over a trillion dollars that provides direct benefits to its citizens from oil revenues. Alaska has distributed oil dividends to residents for decades. Singapore has a system of mandatory savings and public investment that gives citizens a stake in national prosperity. These are not radical experiments. They are proven models operating at national scale. And there are thousands of such programs around the world. What is missing is not economic theory. What is missing is the political will to implement these mechanisms, the narrative infrastructure to make them seem inevitable rather than radical, and the coalition to demand them. That is what L/0 exists to build. This is an invitation. If you are building the automation and wondering who is thinking about the social transition, this is for you. If you are burned out and know that “find a better job” is not a solution to a systemic problem, this is for you. If you have been called lazy for refusing to pretend the treadmill leads somewhere, this is for you. If you run a company and understand that your future customers need income even after your company stops hiring, this is for you. L/0 is not a political party or a policy platform. It is a coalition and a direction. The work is ongoing through the Post-Labor Economics project, which addresses the specific mechanisms of transition. The conversation is happening in public, and it is open to anyone who understands that the current arrangement is ending and wants to participate in designing what comes next. The goal is simple. Eliminate obligatory labor. Distribute ownership broadly. Let humans do what humans do when they are not forced to sell their time to survive. Liberate humanity from drudgery so that we can all reach our maximum potential.

Everyone is exhausted. This is not a metaphor or a generational complaint. It is a clinical and measurable reality that spans every culture and every economic class. In China, young people call it tang ping, or “lying flat,” a deliberate withdrawal from the achievement treadmill. In Japan, karoshi is a legally recognized cause of death, meaning “worked to death.” In Korea, fertility has collapsed to the lowest rate on earth because an entire generation has decided the grind is not worth reproducing into. In America, deaths of despair have driven life expectancy backward for the first time in a century. Quiet quitting. Let it rot. The Great Resignation. These are not trends. They are symptoms of a global labor force that has reached the end of its tolerance. Capitalism is not satisfied with the limitations of human flesh, and our bodies are in open revolt. Something fundamental is breaking, and it is worth naming plainly. For the past two centuries, labor has been the primary mechanism by which modern economies distribute resources to households. You work for a firm, you receive wages, you use those wages to participate in the economy. This arrangement was never a law of nature. It was a system designed to solve a particular problem at a particular moment in history, and it worked reasonably well for a long time. It is not working anymore. Wages in the United States decoupled from productivity growth in the early 1970s. Since then, economic output has continued to climb while median household income has remained essentially flat. The gains have flowed to capital owners while workers have absorbed the stress and stagnation. Meanwhile, automation has steadily displaced human labor across sector after sector. Manufacturing employment peaked decades ago. Retail is hollowing out. White-collar work is now facing the same pressure from AI that blue-collar work faced from robotics. This is not a policy debate about whether automation is good or bad. It is an observation about a trajectory that is already underway and accelerating. The reason we struggle to talk about this clearly is that we have inherited a set of beliefs about labor that have nothing to do with economics. We have been told that work is sacred. That labor builds character and idleness corrupts the soul. That anyone who does not want to work is morally defective. These ideas feel like common sense, but they are not ancient wisdom. They are the residue of a specific theological tradition, namely the Protestant work ethic that emerged in the 16th century and fused with capitalism over the following centuries. We have mistaken a historical artifact for a natural law. It is time to stop fetishizing labor. It is time to stop sacralizing the sacrifice of our time, our bodies, our health, and our sanity to enrich others. Young people are already rejecting this. “I do not dream of labor” has become a widespread sentiment, not because this generation is lazy, but because they can see what older generations have rationalized away. The deal is bad and getting worse. The fetishization of work as a moral good serves the interests of those who benefit from cheap and compliant labor. It does not serve the people doing the work. Before any productive conversation about the future can happen, this fetish has to be named and dismantled. The difficulty is that both the political left and the political right remain committed to defending labor, even as the ground shifts beneath them. On the right, the defense takes the form of bootstrap mythology and warnings about welfare dependency. Work builds character. Idle hands invite trouble. A strong society requires productive citizens, and productivity is measured in hours exchanged for wages. This position treats labor as a disciplinary institution as much as an economic one. On the left, the defense is more sympathetic but equally stuck. The focus falls on dignified work, living wages, job guarantees, and union solidarity. These are responses to the genuine brutality of labor under capitalism, but they share an underlying assumption with the right. Both positions treat labor as the foundation of economic life, something to be reformed or protected rather than transcended. I call this shared ideology laborism. It is the belief that human labor must be preserved as an economic necessity, a moral virtue, or a foundation for identity. Laborism spans the political spectrum. It unites people who agree on almost nothing else. And it has become the primary obstacle to honest thinking about what comes next. Once automation reaches the point where machines can perform most human labor better, faster, cheaper, and safer, the laborist position becomes untenable. At that point, insisting that humans must continue working is not a defense of dignity. It is a demand that people perform unnecessary suffering for ideological reasons. I am proposing something simple. L/0. Labor-zero. The elimination of obligatory human labor. This does not mean the elimination of work. It means the elimination of compulsion. People will continue to create, to build, to care for each other, to solve problems, to pursue mastery. What disappears is work performed under threat of deprivation. The difference between chosen work and coerced work is the difference between exercise and forced labor. One is life-enhancing. The other is a condition we have historically recognized as a form of bondage. We call it “wage slavery” for a reason. The goal of L/0 is a world where no one has to work to survive. Where contribution is voluntary and intrinsic rather than extracted through economic desperation. This is not a utopian fantasy. It is a design problem with identifiable components and measurable progress. The coalition for this goal already exists. It just does not recognize itself yet. Consider who actually wants labor to end. On one side, you have capital. Corporations have spent the last century trying to reduce labor costs through every available means. Offshoring, automation, gig classification, union suppression. The ideal business from a pure capital perspective has zero employees and infinite output. This is not a conspiracy theory. It is the explicit optimization target of every efficiency-focused enterprise. On the other side, you have workers. Not the abstract proletariat of Marxist theory, but actual burned-out humans who fantasize about quitting, who dread Monday mornings, who experience their jobs as something to be endured rather than enjoyed. The lying flat movement, the antiwork forums, the quiet quitting phenomenon. These are not expressions of laziness. They are rational responses to a system that extracts maximum effort for diminishing returns. Capital and labor are usually framed as adversaries. But on the question of whether human labor should continue to exist as an obligation, their interests converge. The capitalist does not want to manage humans. The worker does not want to be managed. Both would prefer a world where the machines do the work and humans do something else. The conflict between capital and labor is real, but it is a conflict over the terms of the transition, not the destination. Who captures the gains from automation? How is ownership distributed? What happens to the people displaced in the process? These are genuine fights worth having. But they are negotiations within a shared frame, not a war between incompatible visions. Here is the opportunity that L/0 names. Neither side wants this marriage anymore. Capital does not want the overhead, the liability, the HR departments, the labor disputes, the inefficiency of human workers. Labor does not want the compulsion, the precarity, the alarm clocks, the performance reviews, the quiet desperation of trading irreplaceable time for replaceable wages. We are ready for a divorce. Let’s get this acrimonious arrangement behind us. The productive move is to acknowledge this honestly, sign the papers, and start negotiating the separation agreement. The fight over wages was always zero-sum. Every dollar paid to workers was a dollar not captured as profit, and vice versa. But the negotiation over ownership of automated production is positive-sum. Capitalists need consumers with money to spend or their markets collapse. Workers need income decoupled from employment or they starve. Both sides get what they want if the transition is designed correctly. This is not idealism. It is alignment of incentives. The path forward is not mysterious. Economists have understood for decades that the answer to technological unemployment is broadened capital participation. If wages are no longer the primary mechanism for distributing economic gains, then ownership must take their place. Instead of trading hours for dollars, households participate directly in the productive capacity of the automated economy. This can take many forms. Sovereign wealth funds that distribute automation dividends to citizens. Expanded employee stock ownership plans. Universal basic capital grants. Public equity stakes in AI and robotics firms that use public infrastructure and public data. The policy mechanisms are not speculative. Norway has a sovereign wealth fund worth over a trillion dollars that provides direct benefits to its citizens from oil revenues. Alaska has distributed oil dividends to residents for decades. Singapore has a system of mandatory savings and public investment that gives citizens a stake in national prosperity. These are not radical experiments. They are proven models operating at national scale. And there are thousands of such programs around the world. What is missing is not economic theory. What is missing is the political will to implement these mechanisms, the narrative infrastructure to make them seem inevitable rather than radical, and the coalition to demand them. That is what L/0 exists to build. This is an invitation. If you are building the automation and wondering who is thinking about the social transition, this is for you. If you are burned out and know that “find a better job” is not a solution to a systemic problem, this is for you. If you have been called lazy for refusing to pretend the treadmill leads somewhere, this is for you. If you run a company and understand that your future customers need income even after your company stops hiring, this is for you. L/0 is not a political party or a policy platform. It is a coalition and a direction. The work is ongoing through the Post-Labor Economics project, which addresses the specific mechanisms of transition. The conversation is happening in public, and it is open to anyone who understands that the current arrangement is ending and wants to participate in designing what comes next. The goal is simple. Eliminate obligatory labor. Distribute ownership broadly. Let humans do what humans do when they are not forced to sell their time to survive. Liberate humanity from drudgery so that we can all reach our maximum potential.

David Shapiro (L/0)

63,911 次观看 • 6 个月前

CANCEL Your Weekend Plans and Learn Vibe Coding Today, Start Making $10,000/Month Building Apps for People. $0 in Coding Experience. I made 5 AI Trading Bots & Apps Built in 6 Hours. Each One Worth $3,000-$15,000 to Clients. You Spent $500 on a Bootcamp and Still Can't Deploy a Landing Page. That's not the bootcamp's fault. That's you. People with zero coding skills are building full apps with payments, databases, and authentication using AI. Charging clients $5,000-$10,000 per project. Finishing in one afternoon. You're still Googling "should I learn Python or JavaScript first." This attached video is a goldmine. 6 hours. 5 real apps. From complete beginner to deploying revenue-generating products. One video. Free. Save it. Watch it this weekend. Not next weekend. Today. Now let me break down exactly what's inside and why you can't afford to ignore this. Save this post. You'll hate yourself if you lose it. ↓ Let's talk about why you still can't code... You bought the Udemy course. $12.99. Watched 3 lectures. Got confused. Told yourself you'd continue tomorrow. That was 8 months ago. You bought another course. $49.99. This one had better reviews. Watched the intro. Bookmarked the rest. Never opened it again. You signed up for a bootcamp. $5,000. Dropped out at week 4 because "life got busy." Life didn't get busy. You got scared. Three years. Hundreds of dollars. Multiple courses. Zero apps built. Zero projects deployed. Zero revenue generated. And now someone with zero coding experience is building full apps in hours using AI tools you haven't even tried. You're not falling behind slowly. You're falling behind at full speed. Save this post right now. This is the course that makes every other coding course you bought irrelevant. Follow Himanshu Kumar so you don't miss the breakdown. ↓ What is vibe coding and why should you care? Traditional coding: Learn syntax for 6 months. Build a to-do app. Feel proud. Realize nobody will pay for a to-do app. Give up. Vibe coding: Describe what you want to build. AI builds it. You guide, adjust, deploy. People pay for it. You're not writing code line by line. You're directing an AI agent that writes code for you. Think of it like this: Traditional coding = you're the construction worker. Vibe coding = you're the architect. The architect makes more money. The architect doesn't carry bricks. The architect doesn't need to know how to pour concrete. The architect needs to know what to build and why. That's vibe coding. And while you've been debating whether to learn Python or JavaScript first, people are skipping both and building apps that generate revenue. With zero coding knowledge. This isn't the future. This is right now. Save this post and follow Himanshu Kumar for more vibe coding breakdowns that actually make you money. ↓ What this 6-hour course covers. This isn't some 20-minute tutorial that shows you how to make a button change color. This is 6 hours. 5 complete apps. Real software engineering. Real deployment. Real money-making potential. Here's what you'll build: > Portfolio website - deployed live on Netlify > Full-stack client dashboard - with database and auth > Lead generation app - with API integrations > Thumbnail generator - with payment integration via Stripe > Splinter - a full SaaS product with pricing and marketing Not toy projects. Not "follow along and never use again." Actual apps that people pay for. Built with Gemini 3.1 Pro, Antigravity, Supabase, Next.js, Vite, and more. You know how many people charge $5,000+ to build a single one of these apps for a client? You'll be able to build all 5 by the end of this weekend. You can't afford to scroll past this. Bookmark this post. Follow Himanshu Kumar because I'm breaking down every tool in this stack separately. ↓ The tools you'll master. Gemini 3.1 Pro: Google's most powerful AI model. You'll use it to generate entire codebases. Not snippets. Entire apps. Antigravity: The AI coding environment that makes vibe coding actually work. Agent chat. MCP servers. Voice dictation. It's not VS Code with a chatbot bolted on. It's built from the ground up for AI-first development. Supabase: Your backend. Database. Authentication. All set up in minutes. Not weeks of configuration. Next.js + Vite: Modern frameworks that make your apps fast, scalable, and professional. Stripe: Payment integration. So your apps can actually charge people money. You know, the whole point. Claude Code: Yes, Claude Code is covered too. Because the best developers in 2026 don't use one AI tool. They use all of them. While you're still trying to decide which AI tool is "the best one," smart people are using all of them together and making money from every angle. Stop debating tools. Start using them. Save this post and follow Himanshu Kumar for deep dives into each of these tools. ↓ What you'll actually learn beyond just "building apps." This course doesn't just teach you to copy and paste prompts. You'll learn real software engineering: > Hosting and deployment > Modern software design patterns > Languages and frameworks > Version control and GitHub > Programming with AI agents and agent teams > Database design (SQL vs NoSQL) > Security audits > API integration > Payment processing This is everything a $15,000 bootcamp teaches. In 6 hours. For free. On YouTube. Your friend who spent $15K on a bootcamp is going to be really upset when you build better apps than them after watching one YouTube video this weekend. Don't tell them about this course. Or do. Their reaction will be priceless. This is a $15,000 education for $0. Save this post before it gets buried. Follow Himanshu Kumar for more free resources that make paid courses look like scams. ↓ The guy teaching this actually makes money. Not "makes money selling courses about making money." Actually makes money. Nick built automated businesses with Make . Most notably 1SecondCopy, a content company that hit 7 figures. Seven figures. From automation. He's not teaching theory. He's showing you what real systems that generate real revenue look like. 90% of coding teachers on YouTube have never shipped a product that made $1. They teach coding. They don't use coding to make money. This guy does both. That's why this course is different. You've been learning from people who teach for a living. Start learning from people who build for a living. Save this post. Follow Himanshu Kumar for more content from builders, not lecturers. ↓ Let me tell you what's really happening while you "think about learning to code." Every week that passes, AI coding tools get better. Every week that passes, more people learn vibe coding. Every week that passes, the market gets more competitive. Right now, vibe coding is still early. Not many people know how to do it well. Clients are desperate for someone who can build apps fast. $3,000 for a landing page with payments. $5,000 for a SaaS MVP. $10,000 for a full client dashboard. These are real prices people are charging for apps they built in a single day using the exact tools in this course. But this window won't last forever. In 6 months, everyone will know how to vibe code. In 12 months, it'll be a basic requirement. In 24 months, not knowing this will be like not knowing how to use email in 2010. You're either early or you're irrelevant. Right now you can still be early. But not if you spend this weekend on Netflix. The window is closing. Every weekend you waste is a weekend someone else uses to get ahead of you. Save this post. Follow Himanshu Kumar before this opportunity becomes obvious to everyone. ↓ The 5 apps you'll build and what they're actually worth. App 1: Portfolio Website. What clients pay for this: $500-$2,000. Time to build with vibe coding: 30 minutes. App 2: Client Dashboard. What clients pay for this: $5,000-$15,000. Time to build with vibe coding: 2-3 hours. App 3: Lead Generation Tool. What clients pay for this: $3,000-$8,000. Time to build with vibe coding: 1-2 hours. App 4: Thumbnail Generator with Payments. What clients pay for this: $2,000-$5,000. Or sell it as a SaaS for recurring revenue. Time to build: 1-2 hours. App 5: Splinter (Full SaaS Product). What clients pay for this: $10,000-$25,000. Or launch it yourself for monthly recurring revenue. Time to build: 2-3 hours. Total value of apps you can build after this course: $20,000-$55,000. Total cost of this course: $0. Total time investment: one weekend. You spend more than one weekend deciding which Netflix show to start next. At least this weekend would pay you back. Read those numbers again. Save this post. Follow Himanshu Kumar because I'll be breaking down how to sell each of these apps as a service. ↓ Here's the business model nobody's talking about. Learn vibe coding this weekend. Build 5 apps. Pick the one you're best at. Offer it as a service. "I build professional SaaS dashboards for businesses using AI. Faster than agencies. Fraction of the cost. $5,000 per project." 2 projects per month = $10,000/month. Working maybe 20 hours total. While you're applying for jobs that pay $4,000/month and require 5 years of experience you don't have, someone who watched this course last weekend just landed their second $5,000 client. No degree. No portfolio. No 5 years of experience. Just the ability to build what people need faster than anyone else. That's the entire business model. Learn fast. Build fast. Charge accordingly. Stop applying for jobs. Start creating them. Save this post. Follow Himanshu Kumar for the exact outreach scripts to land your first vibe coding client. ↓ Why you won't watch this course. Because it's 6 hours. "6 hours?? That's too long." You binged an entire season of a show last weekend in 8 hours. You scrolled Twitter for 4 hours yesterday. You spent 3 hours watching YouTube shorts that you don't even remember. But 6 hours to learn a skill that could make you $10,000/month? "I don't have time for that." You have time. You just don't have discipline. And that's the actual reason you're broke. Not the economy. Not the market. Not your circumstances. Your inability to sit down for 6 hours and learn something that changes your life. Everything else is a story you tell yourself to feel better about doing nothing. That's the uncomfortable truth. Save this post so it stares at you every time you open your bookmarks. Follow Himanshu Kumar because I'll keep reminding you until you actually do something. ↓ What happens this weekend determines your next year. Path A: Watch the course Saturday. Build your first app Sunday. Start offering services Monday. Land first client within 2 weeks. $5,000-$10,000/month within 60 days. Path B: Sleep in Saturday. Brunch Sunday. Netflix Sunday night. Monday morning alarm goes off. Back to the same job. Same salary. Same frustration. Same "I'll start next weekend." 52 weekends in a year. How many have you already wasted? Path A costs you one weekend. Path B costs you your entire future. Same video. Same information. Same 6 hours. Two completely different lives. ↓ Full 6-hour course attached. 5 real apps. Real deployment. Real revenue potential. From the guy who built a 7-figure automated business. Not theory. Not motivation. Actual hands-on building. The course is free. The tools are free. The knowledge is right here. The only thing that costs money is your decision to do nothing. And that cost compounds every single day. Follow Himanshu Kumar for more breakdowns that turn free YouTube videos into $10,000/month skill sets. Save this post. Watch the video. Build something this weekend that your Monday self will thank you for. Or don't. And wonder next year why nothing changed.

CANCEL Your Weekend Plans and Learn Vibe Coding Today, Start Making $10,000/Month Building Apps for People. $0 in Coding Experience. I made 5 AI Trading Bots & Apps Built in 6 Hours. Each One Worth $3,000-$15,000 to Clients. You Spent $500 on a Bootcamp and Still Can't Deploy a Landing Page. That's not the bootcamp's fault. That's you. People with zero coding skills are building full apps with payments, databases, and authentication using AI. Charging clients $5,000-$10,000 per project. Finishing in one afternoon. You're still Googling "should I learn Python or JavaScript first." This attached video is a goldmine. 6 hours. 5 real apps. From complete beginner to deploying revenue-generating products. One video. Free. Save it. Watch it this weekend. Not next weekend. Today. Now let me break down exactly what's inside and why you can't afford to ignore this. Save this post. You'll hate yourself if you lose it. ↓ Let's talk about why you still can't code... You bought the Udemy course. $12.99. Watched 3 lectures. Got confused. Told yourself you'd continue tomorrow. That was 8 months ago. You bought another course. $49.99. This one had better reviews. Watched the intro. Bookmarked the rest. Never opened it again. You signed up for a bootcamp. $5,000. Dropped out at week 4 because "life got busy." Life didn't get busy. You got scared. Three years. Hundreds of dollars. Multiple courses. Zero apps built. Zero projects deployed. Zero revenue generated. And now someone with zero coding experience is building full apps in hours using AI tools you haven't even tried. You're not falling behind slowly. You're falling behind at full speed. Save this post right now. This is the course that makes every other coding course you bought irrelevant. Follow Himanshu Kumar so you don't miss the breakdown. ↓ What is vibe coding and why should you care? Traditional coding: Learn syntax for 6 months. Build a to-do app. Feel proud. Realize nobody will pay for a to-do app. Give up. Vibe coding: Describe what you want to build. AI builds it. You guide, adjust, deploy. People pay for it. You're not writing code line by line. You're directing an AI agent that writes code for you. Think of it like this: Traditional coding = you're the construction worker. Vibe coding = you're the architect. The architect makes more money. The architect doesn't carry bricks. The architect doesn't need to know how to pour concrete. The architect needs to know what to build and why. That's vibe coding. And while you've been debating whether to learn Python or JavaScript first, people are skipping both and building apps that generate revenue. With zero coding knowledge. This isn't the future. This is right now. Save this post and follow Himanshu Kumar for more vibe coding breakdowns that actually make you money. ↓ What this 6-hour course covers. This isn't some 20-minute tutorial that shows you how to make a button change color. This is 6 hours. 5 complete apps. Real software engineering. Real deployment. Real money-making potential. Here's what you'll build: > Portfolio website - deployed live on Netlify > Full-stack client dashboard - with database and auth > Lead generation app - with API integrations > Thumbnail generator - with payment integration via Stripe > Splinter - a full SaaS product with pricing and marketing Not toy projects. Not "follow along and never use again." Actual apps that people pay for. Built with Gemini 3.1 Pro, Antigravity, Supabase, Next.js, Vite, and more. You know how many people charge $5,000+ to build a single one of these apps for a client? You'll be able to build all 5 by the end of this weekend. You can't afford to scroll past this. Bookmark this post. Follow Himanshu Kumar because I'm breaking down every tool in this stack separately. ↓ The tools you'll master. Gemini 3.1 Pro: Google's most powerful AI model. You'll use it to generate entire codebases. Not snippets. Entire apps. Antigravity: The AI coding environment that makes vibe coding actually work. Agent chat. MCP servers. Voice dictation. It's not VS Code with a chatbot bolted on. It's built from the ground up for AI-first development. Supabase: Your backend. Database. Authentication. All set up in minutes. Not weeks of configuration. Next.js + Vite: Modern frameworks that make your apps fast, scalable, and professional. Stripe: Payment integration. So your apps can actually charge people money. You know, the whole point. Claude Code: Yes, Claude Code is covered too. Because the best developers in 2026 don't use one AI tool. They use all of them. While you're still trying to decide which AI tool is "the best one," smart people are using all of them together and making money from every angle. Stop debating tools. Start using them. Save this post and follow Himanshu Kumar for deep dives into each of these tools. ↓ What you'll actually learn beyond just "building apps." This course doesn't just teach you to copy and paste prompts. You'll learn real software engineering: > Hosting and deployment > Modern software design patterns > Languages and frameworks > Version control and GitHub > Programming with AI agents and agent teams > Database design (SQL vs NoSQL) > Security audits > API integration > Payment processing This is everything a $15,000 bootcamp teaches. In 6 hours. For free. On YouTube. Your friend who spent $15K on a bootcamp is going to be really upset when you build better apps than them after watching one YouTube video this weekend. Don't tell them about this course. Or do. Their reaction will be priceless. This is a $15,000 education for $0. Save this post before it gets buried. Follow Himanshu Kumar for more free resources that make paid courses look like scams. ↓ The guy teaching this actually makes money. Not "makes money selling courses about making money." Actually makes money. Nick built automated businesses with Make . Most notably 1SecondCopy, a content company that hit 7 figures. Seven figures. From automation. He's not teaching theory. He's showing you what real systems that generate real revenue look like. 90% of coding teachers on YouTube have never shipped a product that made $1. They teach coding. They don't use coding to make money. This guy does both. That's why this course is different. You've been learning from people who teach for a living. Start learning from people who build for a living. Save this post. Follow Himanshu Kumar for more content from builders, not lecturers. ↓ Let me tell you what's really happening while you "think about learning to code." Every week that passes, AI coding tools get better. Every week that passes, more people learn vibe coding. Every week that passes, the market gets more competitive. Right now, vibe coding is still early. Not many people know how to do it well. Clients are desperate for someone who can build apps fast. $3,000 for a landing page with payments. $5,000 for a SaaS MVP. $10,000 for a full client dashboard. These are real prices people are charging for apps they built in a single day using the exact tools in this course. But this window won't last forever. In 6 months, everyone will know how to vibe code. In 12 months, it'll be a basic requirement. In 24 months, not knowing this will be like not knowing how to use email in 2010. You're either early or you're irrelevant. Right now you can still be early. But not if you spend this weekend on Netflix. The window is closing. Every weekend you waste is a weekend someone else uses to get ahead of you. Save this post. Follow Himanshu Kumar before this opportunity becomes obvious to everyone. ↓ The 5 apps you'll build and what they're actually worth. App 1: Portfolio Website. What clients pay for this: $500-$2,000. Time to build with vibe coding: 30 minutes. App 2: Client Dashboard. What clients pay for this: $5,000-$15,000. Time to build with vibe coding: 2-3 hours. App 3: Lead Generation Tool. What clients pay for this: $3,000-$8,000. Time to build with vibe coding: 1-2 hours. App 4: Thumbnail Generator with Payments. What clients pay for this: $2,000-$5,000. Or sell it as a SaaS for recurring revenue. Time to build: 1-2 hours. App 5: Splinter (Full SaaS Product). What clients pay for this: $10,000-$25,000. Or launch it yourself for monthly recurring revenue. Time to build: 2-3 hours. Total value of apps you can build after this course: $20,000-$55,000. Total cost of this course: $0. Total time investment: one weekend. You spend more than one weekend deciding which Netflix show to start next. At least this weekend would pay you back. Read those numbers again. Save this post. Follow Himanshu Kumar because I'll be breaking down how to sell each of these apps as a service. ↓ Here's the business model nobody's talking about. Learn vibe coding this weekend. Build 5 apps. Pick the one you're best at. Offer it as a service. "I build professional SaaS dashboards for businesses using AI. Faster than agencies. Fraction of the cost. $5,000 per project." 2 projects per month = $10,000/month. Working maybe 20 hours total. While you're applying for jobs that pay $4,000/month and require 5 years of experience you don't have, someone who watched this course last weekend just landed their second $5,000 client. No degree. No portfolio. No 5 years of experience. Just the ability to build what people need faster than anyone else. That's the entire business model. Learn fast. Build fast. Charge accordingly. Stop applying for jobs. Start creating them. Save this post. Follow Himanshu Kumar for the exact outreach scripts to land your first vibe coding client. ↓ Why you won't watch this course. Because it's 6 hours. "6 hours?? That's too long." You binged an entire season of a show last weekend in 8 hours. You scrolled Twitter for 4 hours yesterday. You spent 3 hours watching YouTube shorts that you don't even remember. But 6 hours to learn a skill that could make you $10,000/month? "I don't have time for that." You have time. You just don't have discipline. And that's the actual reason you're broke. Not the economy. Not the market. Not your circumstances. Your inability to sit down for 6 hours and learn something that changes your life. Everything else is a story you tell yourself to feel better about doing nothing. That's the uncomfortable truth. Save this post so it stares at you every time you open your bookmarks. Follow Himanshu Kumar because I'll keep reminding you until you actually do something. ↓ What happens this weekend determines your next year. Path A: Watch the course Saturday. Build your first app Sunday. Start offering services Monday. Land first client within 2 weeks. $5,000-$10,000/month within 60 days. Path B: Sleep in Saturday. Brunch Sunday. Netflix Sunday night. Monday morning alarm goes off. Back to the same job. Same salary. Same frustration. Same "I'll start next weekend." 52 weekends in a year. How many have you already wasted? Path A costs you one weekend. Path B costs you your entire future. Same video. Same information. Same 6 hours. Two completely different lives. ↓ Full 6-hour course attached. 5 real apps. Real deployment. Real revenue potential. From the guy who built a 7-figure automated business. Not theory. Not motivation. Actual hands-on building. The course is free. The tools are free. The knowledge is right here. The only thing that costs money is your decision to do nothing. And that cost compounds every single day. Follow Himanshu Kumar for more breakdowns that turn free YouTube videos into $10,000/month skill sets. Save this post. Watch the video. Build something this weekend that your Monday self will thank you for. Or don't. And wonder next year why nothing changed.

Himanshu Kumar

39,379 次观看 • 3 个月前

77 Reasons Why I’ve Invested Over $8,000,000+ in MultiversX (EGLD) and Why EGLD Will Crush It in 2025 (My Investment Thesis). I publicly shared my portfolio on X. EGLD is A) Better than BTC B) Everything that ETH wants to be C) The GameStop of Crypto 1. EGLD is verifiably the most scalable (theoretically unlimited) L1 chain in the world, theoretically capable of over 10 million TPS (thanks to adaptive state sharding). 2. e-Gold is digital gold. It has the best tokenomics among all L1s, similarly scarce to BTC, with a maximum supply of 31.4 million coins. Currently, 27.68 million coins are in circulation. 3. EGLD will be the most decentralized cryptocurrency in the world thanks to sharding and minimal hardware requirements for running nodes. It’s already second only to Ethereum with 3,618 validator nodes. 4. EGLD has extremely low fees, around ~$0.002 per transaction. 5. EGLD is extremely secure. No wallet drains like on ETH/SOL; assets are owned natively (not via a smart contract). There is no MEV risk (front-running bots). 6. EGLD is the only chain in the world with an on-chain Guardian (two-phase verification), making it impossible for a hacker to steal your funds—even if they have your private keys (seed phrase). 7. EGLD is carbon-neutral and eco-friendly, not wasting energy like BTC and other PoW chains. It’s exceptionally efficient, scalable, global, and sustainable. 8. EGLD has the best UX in crypto. Download the xPortal wallet—it’s like discovering Apple in Web3. The interface is simple, flawless, and you barely realize you’re using crypto. Instead of addresses, you use HeroTags. The app features all dApps, everything runs smoothly, and the visuals are beautifully designed. The explorer, web wallet, etc. follow the same high-quality user experience. 9. EGLD supports native assets, unlike Ethereum, for example. 10. EGLD is the first chain to fully implement horizontal (theoretically unlimited) sharding without compromising on decentralization—unlike Solana and others that attempt vertical scaling, leading to multiple network downtimes (11+ times) and huge hardware demands for validators, ultimately harming decentralization. 11. EGLD makes setting up a validator agency extremely easy. Even complete IT beginners can do it. The UX and documentation are superb. I personally set up the “EGLDSqueeze” agency in about 30 minutes. Managing it is straightforward via the web wallet, which feels like managing a Facebook page. This simplifies decentralization enormously. 12. EGLD allows literally anyone (even your grandma) to participate in decentralization, since nodes can run on a Raspberry Pi or a relatively affordable phone. Imagine millions of people worldwide securing the network, validating transactions without even knowing it. This can’t be done with BTC, where setting up profitable mining operations is prohibitively expensive. 13. WASM-Based Virtual Machine: You can write smart contracts in your favorite language, compile them, and run them via the fastest VM in the world. 14. EGLD has been tested at an incredible 263,000 TPS using its sharding mechanism and low hardware requirements. Allegedly, by mid-next year (April), they’ll demonstrate 1,000,000 TPS. (For context: Mastercard handles around 5,000 TPS; BTC handles 5–7 TPS.) 15. EGLD is currently the most advanced L1 in terms of scalability, security, decentralization, UX, eco-friendliness, and tokenomics. It’s the only chain that has genuinely solved the Blockchain Trilemma and is ready to onboard 1 billion people into crypto—users who won’t even realize they’re interacting with crypto. 16. EGLD is perfectly positioned for AI projects—AI agents, AI tools, or a so-called “Truth Machine” that monitors other AIs on-chain, documenting what’s true and comparing different AI outputs (some of which may be censored or biased), ensuring people don’t get confused or scammed in an AI-driven world. 17. The EGLD team is the hardest-working team I’ve ever encountered. I had the honor of meeting many of them personally, and can attest that their pace—even during a bear market—is extraordinary. 18. EGLD’s development team is exceptionally active on GitHub, continually improving their network and actively committing code. 19. EGLD plans to introduce an update reducing block time to 600ms (down from ~6 seconds), which would make the chain essentially unrivaled. 20. EGLD is effectively the only usable L1 in Europe, and the team has direct connections within the EU government—extremely bullish for the project. 21. EGLD provides top-tier on-chain governance not only for the MultiversX (EGLD) protocol but also for DeFi projects (e.g., xExchange, MEX). 22. EGLD plans to expand to the US, likely opening offices in Austin, Texas. This could put them in direct contact with Elon Musk (if it hasn’t happened already), as he’s involved with If he’s done his research, he’d discover there’s simply no better L1 worldwide. 23. EGLD solved fully implemented sharding, perfect tokenomics, and top-tier architecture with just $5M, whereas other chains failed to do so even with $100M+. The second-best sharding network, NEAR, needed $100M, has worse tokenomics, and its sharding isn’t fully implemented yet. Its UX also doesn’t compare. Owning NEAR was like comparing a VW Golf R to a Porsche GT3—EGLD is the Porsche GT3. 24. According to Similarweb, EGLD has significantly high traffic relative to other chains with market caps 100x larger. The market cap vs. web traffic discrepancy is huge, which is a strong indicator of EGLD’s potential. 25. EGLD has the most active and dedicated community relative to its user base, with users who believe in the technology, have full faith in the team, and remain loyal despite price volatility—because they use the chain and know there’s nothing better. 26. Check other chains’ active user counts on X (Twitter) and compare it with the followers of EGLD’s founders and main network accounts, versus those with 30x, 50x, or 100x larger market caps. 27. Visit the MultiversX website to observe the futuristic design and presentation, then compare it to other chains that appear nearly a decade behind in design and branding. 28. EGLD hosts the xDay Global event, showcasing updates, new builders, projects in the ecosystem, and major announcements—similar to Apple’s Keynotes—delivered in a highly professional, goosebump-inducing atmosphere. The next event is in Korea, the second-biggest crypto market after the US. Check out their previous xDay after-movie to see why this is extremely bullish. 29. EGLD is moving forward with plans for the first regulated, audited EU stablecoin under MiCa regulation, made possible by acquiring xMoney, which I view as a “Stripe” for crypto/fiat, offering everything from user solutions to merchant services—potentially the future of payments. 30. Greg Siourouni recently joined EGLD, having been an executive director at SUI Foundation. He’s now co-founder of xMoney Global. xMoney (formerly UTrust, with token UTK) is owned and founded by the MultiversX Labs team. A stablecoin might be introduced soon, which would be massively bullish given xMoney’s roadmap. They recently announced integrations with Binance Pay—both ways. 31. EGLD prioritizes user safety, believing it’s the only feasible approach once the network scales to serve a billion people—many of whom are retail users with little to no security awareness. 32. EGLD offers “Sovereign Chains,” letting you effectively clone their chain without heavy development, set up your own validators, and leverage their unlimited scalability. Any blockchain (ETH, BTC, SOL) struggling with scalability, decentralization, or security could run an ultra-fast, scalable, and secure L2 on EGLD’s Sovereign Chain, meeting top enterprise requirements. No one else has really done this. The Sovereign Chain demo achieved astonishing TPS and has an SDK. 33. No downtime since inception. 34. No shard takeover attacks have occurred. 35. Extremely fast—soon 600ms block time will be in place. 36. ESDTs – The best token standard available: fungible, non-fungible, semi-fungible, DeFi assets—everything is native and highly customizable. 37. Top-tier composability of assets and smart contracts. 38. Integrated DNS at protocol level with HeroTags (nicknames) instead of long addresses. 39. Asynchronous calls are supported. 40. Cross-shard transfers, execution, reverts, and calls are seamlessly integrated. 41. The best staking system in the space. Secure Proof of Stake (SPoS) is far more efficient than Proof of Work (PoW). 42. Built-in Delegation and Staking Provider system, with over 125K delegators. 43. Complete support for liquid staked assets, fostering decentralization rather than centralization. 44. TransferRoles for ESDT and other advanced operations. 45. Composable tasks on-chain for more sophisticated DeFi workflows. 46. MultiTransfer and asset execution within one transaction. 47. Re-entrancy protection is built-in by design. 48. Storage for ESDT assets goes beyond a linear approach, optimizing performance. 49. No integer overflows thanks to integrated safeMath operations. 50. Integrated crypto opcodes in the VM, enhancing security and performance. 51. Support for BigFloats, BigInts, and BigDecimals, enabling advanced financial calculations on-chain. 52. No sandwich attacks, plus front-running and MEV protection. 53. Relayed Transactions, simplifying user interactions and fees. 54. Smart Accounts featuring data tries and multiple built-in functions. 55. Generalized Paymaster solutions, enabling flexible fee models. 56. Subscriptions for recurring or automated on-chain payments. 57. Web2-like usability with Web3 functionality, bridging mainstream adoption. 58. StakingV4 for improved decentralization. 59. Enhanced MEV protection rolling out to safeguard users. 60. Parallel execution is coming soon, boosting throughput. 61. 1 million TPS is on the roadmap, targeted for demonstration. 62. 600ms block time is also coming soon. 63. Reduced cross-shard processing is planned to improve efficiency. 64. ZK everywhere (PI²): “prove everything” approach is coming. 65. AsyncV3 is in development for more complex cross-contract interactions. 66. Scalability enhancements for Merkle Tries or a new data model are being explored. 67. Linear storage on the VM is forthcoming. 68. A dynamic language interpreter at the VM is also planned. 69. Rumors suggest that MultiversX (EGLD) is building a “Truth Machine” on their L1—an essential, game-changing tool for AI verification and societal impact. 70. The entire team features individuals with PhDs in mathematics and physics, and many are former engineers at Google, IBM, and similar companies. 71. Over 56% of the network’s supply is staked, showcasing strong community involvement. 72. More than 6,772,347 accounts have been created on the network. 73. A total of 476,627,710 transactions have been processed on-chain without any outages or hacks. 74. EGLD has built a massive ecosystem over time. While not as numerous in project count as Solana, its market cap is ~100x smaller, yet it has far superior tokenomics and technology. The projects that do exist, like Hatom Protocol, are top-tier in UX, security, and advanced features. Hatom will soon introduce USH, a truly high-quality, decentralized stablecoin. 75. On competing chains, automated transactions aren’t easily or cheaply executed, whereas on MultiversX, tools like let you do this for free (with near-zero fees). 76. No other chain combines such a strong team and long-term vision where every product meets extreme security and UX standards like MultiversX does. This is why I see it as the “next Apple” in Web3. 77. MultiversX has a new CMO – Adam Bates, a former CMO at the Cardano Foundation. He was behind the success of Cardano’s huge marketing campaign and has a very good relationship with Charles Hoskinson. Thanks to him, Beniamin Mincu (the founder of MultiversX) was likely introduced, and now they will probably discuss how both blockchains can help each other, as well as any other potential collaborations we don’t yet know about. This is also extremely bullish. #EGLD is undeniably the most Scalable, Advanced, Secure, and User-friendly L1 supercomputer ever created. It’s built to SHAPE THE FUTURE. 1) 2) 3) 4) 5) 27/6/2024 - EGLDSqueeze - SUMMARY: HERE IS NO 2ND BEST. EGLD IS ONLY ONE BLOCKCHAIN THAT CAN RULE THEM ALL. ✅ UNLIMITED SCALING ✅ SCARCE AS BTC ✅ PROGRAMMABLE AS ETH ✅ NO DOWNTIME AS SOL ✅ UI/UX OF Apple ✅ SHARDING DONE BEFORE NEAR & TON ✅ BEST WALLET xPortal WITH GUARDIAN Price prediction (NFA|DYOR): My reasoning is that the real market cap as of December 23, 2024...if we take into account the value of other cryptocurrencies such as BTC, SOL, ETH, AVAX, NEAR, TON, Cardano, BNB, XRP, and so forth, plus the existence of meme coins with valuations above 20 billion USD, or even games nobody plays anymore that still have valuations above 800 million shows that EGLD’s current market cap of approximately 942 million USD is incredibly low. From a technological standpoint, user experience, and other relevant aspects, compared to SOL, NEAR, TON, AVAX, and other L1 protocols, EGLD’s market cap should realistically be around 100 billion USD. Therefore, my prediction and investment thesis is a minimum of a 100x increase from its current price (+-SOL marketcap). MultiversX is ready to onboard 1 billion people to the blockchain. From a long-term perspective, it could even reach a market cap of 1 trillion USD, which is roughly half of where BTC is right now. That would be approximately a 1060x gain from the current market cap. 1 EGLD (MultiversX) is for $34 (only 31.4M max supply) think about this. Not financial advice. Again. There is no 2nd best L1. Position yourself where the puck is going, then wait at the goal until the goal gets there Apes together, strong. Ape alone, weak. We Don't Worry. We Just Win. Shape The Future

77 Reasons Why I’ve Invested Over $8,000,000+ in MultiversX (EGLD) and Why EGLD Will Crush It in 2025 (My Investment Thesis). I publicly shared my portfolio on X. EGLD is A) Better than BTC B) Everything that ETH wants to be C) The GameStop of Crypto 1. EGLD is verifiably the most scalable (theoretically unlimited) L1 chain in the world, theoretically capable of over 10 million TPS (thanks to adaptive state sharding). 2. e-Gold is digital gold. It has the best tokenomics among all L1s, similarly scarce to BTC, with a maximum supply of 31.4 million coins. Currently, 27.68 million coins are in circulation. 3. EGLD will be the most decentralized cryptocurrency in the world thanks to sharding and minimal hardware requirements for running nodes. It’s already second only to Ethereum with 3,618 validator nodes. 4. EGLD has extremely low fees, around ~$0.002 per transaction. 5. EGLD is extremely secure. No wallet drains like on ETH/SOL; assets are owned natively (not via a smart contract). There is no MEV risk (front-running bots). 6. EGLD is the only chain in the world with an on-chain Guardian (two-phase verification), making it impossible for a hacker to steal your funds—even if they have your private keys (seed phrase). 7. EGLD is carbon-neutral and eco-friendly, not wasting energy like BTC and other PoW chains. It’s exceptionally efficient, scalable, global, and sustainable. 8. EGLD has the best UX in crypto. Download the xPortal wallet—it’s like discovering Apple in Web3. The interface is simple, flawless, and you barely realize you’re using crypto. Instead of addresses, you use HeroTags. The app features all dApps, everything runs smoothly, and the visuals are beautifully designed. The explorer, web wallet, etc. follow the same high-quality user experience. 9. EGLD supports native assets, unlike Ethereum, for example. 10. EGLD is the first chain to fully implement horizontal (theoretically unlimited) sharding without compromising on decentralization—unlike Solana and others that attempt vertical scaling, leading to multiple network downtimes (11+ times) and huge hardware demands for validators, ultimately harming decentralization. 11. EGLD makes setting up a validator agency extremely easy. Even complete IT beginners can do it. The UX and documentation are superb. I personally set up the “EGLDSqueeze” agency in about 30 minutes. Managing it is straightforward via the web wallet, which feels like managing a Facebook page. This simplifies decentralization enormously. 12. EGLD allows literally anyone (even your grandma) to participate in decentralization, since nodes can run on a Raspberry Pi or a relatively affordable phone. Imagine millions of people worldwide securing the network, validating transactions without even knowing it. This can’t be done with BTC, where setting up profitable mining operations is prohibitively expensive. 13. WASM-Based Virtual Machine: You can write smart contracts in your favorite language, compile them, and run them via the fastest VM in the world. 14. EGLD has been tested at an incredible 263,000 TPS using its sharding mechanism and low hardware requirements. Allegedly, by mid-next year (April), they’ll demonstrate 1,000,000 TPS. (For context: Mastercard handles around 5,000 TPS; BTC handles 5–7 TPS.) 15. EGLD is currently the most advanced L1 in terms of scalability, security, decentralization, UX, eco-friendliness, and tokenomics. It’s the only chain that has genuinely solved the Blockchain Trilemma and is ready to onboard 1 billion people into crypto—users who won’t even realize they’re interacting with crypto. 16. EGLD is perfectly positioned for AI projects—AI agents, AI tools, or a so-called “Truth Machine” that monitors other AIs on-chain, documenting what’s true and comparing different AI outputs (some of which may be censored or biased), ensuring people don’t get confused or scammed in an AI-driven world. 17. The EGLD team is the hardest-working team I’ve ever encountered. I had the honor of meeting many of them personally, and can attest that their pace—even during a bear market—is extraordinary. 18. EGLD’s development team is exceptionally active on GitHub, continually improving their network and actively committing code. 19. EGLD plans to introduce an update reducing block time to 600ms (down from ~6 seconds), which would make the chain essentially unrivaled. 20. EGLD is effectively the only usable L1 in Europe, and the team has direct connections within the EU government—extremely bullish for the project. 21. EGLD provides top-tier on-chain governance not only for the MultiversX (EGLD) protocol but also for DeFi projects (e.g., xExchange, MEX). 22. EGLD plans to expand to the US, likely opening offices in Austin, Texas. This could put them in direct contact with Elon Musk (if it hasn’t happened already), as he’s involved with If he’s done his research, he’d discover there’s simply no better L1 worldwide. 23. EGLD solved fully implemented sharding, perfect tokenomics, and top-tier architecture with just $5M, whereas other chains failed to do so even with $100M+. The second-best sharding network, NEAR, needed $100M, has worse tokenomics, and its sharding isn’t fully implemented yet. Its UX also doesn’t compare. Owning NEAR was like comparing a VW Golf R to a Porsche GT3—EGLD is the Porsche GT3. 24. According to Similarweb, EGLD has significantly high traffic relative to other chains with market caps 100x larger. The market cap vs. web traffic discrepancy is huge, which is a strong indicator of EGLD’s potential. 25. EGLD has the most active and dedicated community relative to its user base, with users who believe in the technology, have full faith in the team, and remain loyal despite price volatility—because they use the chain and know there’s nothing better. 26. Check other chains’ active user counts on X (Twitter) and compare it with the followers of EGLD’s founders and main network accounts, versus those with 30x, 50x, or 100x larger market caps. 27. Visit the MultiversX website to observe the futuristic design and presentation, then compare it to other chains that appear nearly a decade behind in design and branding. 28. EGLD hosts the xDay Global event, showcasing updates, new builders, projects in the ecosystem, and major announcements—similar to Apple’s Keynotes—delivered in a highly professional, goosebump-inducing atmosphere. The next event is in Korea, the second-biggest crypto market after the US. Check out their previous xDay after-movie to see why this is extremely bullish. 29. EGLD is moving forward with plans for the first regulated, audited EU stablecoin under MiCa regulation, made possible by acquiring xMoney, which I view as a “Stripe” for crypto/fiat, offering everything from user solutions to merchant services—potentially the future of payments. 30. Greg Siourouni recently joined EGLD, having been an executive director at SUI Foundation. He’s now co-founder of xMoney Global. xMoney (formerly UTrust, with token UTK) is owned and founded by the MultiversX Labs team. A stablecoin might be introduced soon, which would be massively bullish given xMoney’s roadmap. They recently announced integrations with Binance Pay—both ways. 31. EGLD prioritizes user safety, believing it’s the only feasible approach once the network scales to serve a billion people—many of whom are retail users with little to no security awareness. 32. EGLD offers “Sovereign Chains,” letting you effectively clone their chain without heavy development, set up your own validators, and leverage their unlimited scalability. Any blockchain (ETH, BTC, SOL) struggling with scalability, decentralization, or security could run an ultra-fast, scalable, and secure L2 on EGLD’s Sovereign Chain, meeting top enterprise requirements. No one else has really done this. The Sovereign Chain demo achieved astonishing TPS and has an SDK. 33. No downtime since inception. 34. No shard takeover attacks have occurred. 35. Extremely fast—soon 600ms block time will be in place. 36. ESDTs – The best token standard available: fungible, non-fungible, semi-fungible, DeFi assets—everything is native and highly customizable. 37. Top-tier composability of assets and smart contracts. 38. Integrated DNS at protocol level with HeroTags (nicknames) instead of long addresses. 39. Asynchronous calls are supported. 40. Cross-shard transfers, execution, reverts, and calls are seamlessly integrated. 41. The best staking system in the space. Secure Proof of Stake (SPoS) is far more efficient than Proof of Work (PoW). 42. Built-in Delegation and Staking Provider system, with over 125K delegators. 43. Complete support for liquid staked assets, fostering decentralization rather than centralization. 44. TransferRoles for ESDT and other advanced operations. 45. Composable tasks on-chain for more sophisticated DeFi workflows. 46. MultiTransfer and asset execution within one transaction. 47. Re-entrancy protection is built-in by design. 48. Storage for ESDT assets goes beyond a linear approach, optimizing performance. 49. No integer overflows thanks to integrated safeMath operations. 50. Integrated crypto opcodes in the VM, enhancing security and performance. 51. Support for BigFloats, BigInts, and BigDecimals, enabling advanced financial calculations on-chain. 52. No sandwich attacks, plus front-running and MEV protection. 53. Relayed Transactions, simplifying user interactions and fees. 54. Smart Accounts featuring data tries and multiple built-in functions. 55. Generalized Paymaster solutions, enabling flexible fee models. 56. Subscriptions for recurring or automated on-chain payments. 57. Web2-like usability with Web3 functionality, bridging mainstream adoption. 58. StakingV4 for improved decentralization. 59. Enhanced MEV protection rolling out to safeguard users. 60. Parallel execution is coming soon, boosting throughput. 61. 1 million TPS is on the roadmap, targeted for demonstration. 62. 600ms block time is also coming soon. 63. Reduced cross-shard processing is planned to improve efficiency. 64. ZK everywhere (PI²): “prove everything” approach is coming. 65. AsyncV3 is in development for more complex cross-contract interactions. 66. Scalability enhancements for Merkle Tries or a new data model are being explored. 67. Linear storage on the VM is forthcoming. 68. A dynamic language interpreter at the VM is also planned. 69. Rumors suggest that MultiversX (EGLD) is building a “Truth Machine” on their L1—an essential, game-changing tool for AI verification and societal impact. 70. The entire team features individuals with PhDs in mathematics and physics, and many are former engineers at Google, IBM, and similar companies. 71. Over 56% of the network’s supply is staked, showcasing strong community involvement. 72. More than 6,772,347 accounts have been created on the network. 73. A total of 476,627,710 transactions have been processed on-chain without any outages or hacks. 74. EGLD has built a massive ecosystem over time. While not as numerous in project count as Solana, its market cap is ~100x smaller, yet it has far superior tokenomics and technology. The projects that do exist, like Hatom Protocol, are top-tier in UX, security, and advanced features. Hatom will soon introduce USH, a truly high-quality, decentralized stablecoin. 75. On competing chains, automated transactions aren’t easily or cheaply executed, whereas on MultiversX, tools like let you do this for free (with near-zero fees). 76. No other chain combines such a strong team and long-term vision where every product meets extreme security and UX standards like MultiversX does. This is why I see it as the “next Apple” in Web3. 77. MultiversX has a new CMO – Adam Bates, a former CMO at the Cardano Foundation. He was behind the success of Cardano’s huge marketing campaign and has a very good relationship with Charles Hoskinson. Thanks to him, Beniamin Mincu (the founder of MultiversX) was likely introduced, and now they will probably discuss how both blockchains can help each other, as well as any other potential collaborations we don’t yet know about. This is also extremely bullish. #EGLD is undeniably the most Scalable, Advanced, Secure, and User-friendly L1 supercomputer ever created. It’s built to SHAPE THE FUTURE. 1) 2) 3) 4) 5) 27/6/2024 - EGLDSqueeze - SUMMARY: HERE IS NO 2ND BEST. EGLD IS ONLY ONE BLOCKCHAIN THAT CAN RULE THEM ALL. ✅ UNLIMITED SCALING ✅ SCARCE AS BTC ✅ PROGRAMMABLE AS ETH ✅ NO DOWNTIME AS SOL ✅ UI/UX OF Apple ✅ SHARDING DONE BEFORE NEAR & TON ✅ BEST WALLET xPortal WITH GUARDIAN Price prediction (NFA|DYOR): My reasoning is that the real market cap as of December 23, 2024...if we take into account the value of other cryptocurrencies such as BTC, SOL, ETH, AVAX, NEAR, TON, Cardano, BNB, XRP, and so forth, plus the existence of meme coins with valuations above 20 billion USD, or even games nobody plays anymore that still have valuations above 800 million shows that EGLD’s current market cap of approximately 942 million USD is incredibly low. From a technological standpoint, user experience, and other relevant aspects, compared to SOL, NEAR, TON, AVAX, and other L1 protocols, EGLD’s market cap should realistically be around 100 billion USD. Therefore, my prediction and investment thesis is a minimum of a 100x increase from its current price (+-SOL marketcap). MultiversX is ready to onboard 1 billion people to the blockchain. From a long-term perspective, it could even reach a market cap of 1 trillion USD, which is roughly half of where BTC is right now. That would be approximately a 1060x gain from the current market cap. 1 EGLD (MultiversX) is for $34 (only 31.4M max supply) think about this. Not financial advice. Again. There is no 2nd best L1. Position yourself where the puck is going, then wait at the goal until the goal gets there Apes together, strong. Ape alone, weak. We Don't Worry. We Just Win. Shape The Future

Daniel Veroc

50,029 次观看 • 1 年前

🌍 Official Announcement 3rd Global GCV Conference – October 19, 2025 The Global GCV Core Team proudly announces the 3rd Global GCV Conference, taking place on October 19, 2025 at 7:00 PM Philippine Time (PST). 🎯 Theme “Mainnet Readiness: Harnessing the Power of GCV Ambassadors & GCV Industry Alliances” 🔹 GCV Ambassadors provide local leadership, outreach, and education. 🔹 The Industry Alliance fosters real-world commerce and strategic partnerships. United, these forces represent true readiness for Mainnet. 🔗 Join Live on X Space 🎤 Host 🇵🇭 Lumari – Chair, GCV International Industry Alliance & Chairperson and Executive Director, GCV Core Team Ms. Lumari is the Chairperson and Executive Director of the Global GCV Core Team and Chair of the GCV International Industry Alliance. A graduate of two degrees, she manages the GCV Global Movement, driving connections between real businesses worldwide and appointing GCV envoys across the globe, while mentoring Ecosystem Development Ambassadors to build a strong and sustainable GCV ecosystem. She has spearheaded five major global conferences, addressed 25 international events, and trains and guides Education Ambassadors to ensure high-quality, consistent training throughout the ecosystem. 🎤 Co-Hosts 🇻🇳 Mr. Jojo – Vice Chair, International Alliance & Assistant Executive Director Mr. JoJo helps manage the GCV Global Movement alongside Ms. Lumari, connecting pioneers, ambassadors, and businesses worldwide. A seasoned entrepreneur with degrees in Electronics, Automation, and English, and advanced studies in Engineering in the US and Europe, he combines technical knowledge with practical business acumen to drive international growth. He has transformed the GCV movement by translating Ms. Doris Yin’s articles into 40 languages, authoring thousands of community pieces, and writing over 30 GCV songs that inspire and guide leaders globally. 🇨🇮 Mr. Iyanda Cherif Abiola – Head Africa, Publicity Director Mr. Cherif Abiola Iyanda is the Head of Africa for GCV, overseeing 40 countries, and the Head GCV Ambassador for Ivory Coast, while also serving as Publicity Director for the Global GCV Core Team. As CEO of Tactic Groupe SARL and MiraZat, he brings expertise in cryptocurrencies, web entrepreneurship, and international trade, driving innovation and economic growth across the region. Passionate about education and community development, he has led initiatives such as the English Marathon at AIESEC, organized barter events, and represented GCV in television interviews to advance decentralized technologies. 📢 Keynote Speakers & Introductions 🇨🇦 Ms. Doris Yin – Founder GCV Movement Ms. Doris Yin is the Founder of the Global GCV Movement and the author of the Pioneer’s Handbook. She is a Canadian who holds a Bachelor's degree in Economics from Anhui University of Finance and Economics in China, and a Bachelor's degree in Accounting from Concordia University in Canada. She is a former Certified Public Accountant (CPA) in the USA and is currently a USA IRS Certified Enrolled Agent tax professional. 🇨🇳 Mr. Jian Ping Zhen – Chair Envoy, China Mr. Jian Ping Zhen is an accomplished leader in beauty and hairdressing education with extensive expertise in market training and brand development, and since 2019 has spearheaded Pi Network’s ecosystem expansion in Guangdong. As Chairman of the Oriental Purple Lotus Industry Alliance Management Committee, he excels in market strategy, project management, and forging partnerships that strengthen emerging digital ecosystems. He now serves as Industry Alliance GCV China Special Envoy and Chair, guiding the integration of Global Consensus Value within China and fostering cross-border collaboration to accelerate Pi’s global growth. 🇻🇳 Dao Duc Viet – Chair Envoy, Vietnam Dao Duc Viet is the Head GCV Ambassador for Vietnam and General Director of FDAO Technology & Logistics. A pioneer in promoting the Pi Network and the GCV 314159$ movement, he has built a strong foundation in business, technology, logistics, and digital finance. Passionate about financial education, community development, and digital economic innovation, he is committed to building a sustainable and humane GCV ecosystem based on unity, trust, and long-term growth. 🇹🇭 Mr. Apiwit Noknakorn – Vice Chair Envoy, Thailand Mr. Apiwit Noknakorn is a Thai entrepreneur and innovator with a career spanning engineering, large-scale business leadership, and digital finance. With a Diploma in Electrical Power Engineering, he became Manager at one of Thailand’s largest textile companies before leading a network of over ten companies across diverse industries. Today he heads Thailand’s recycling sector as founder of PA Group 1998 Co., Ltd. and owner of Mai Lamon Café, combining online marketing expertise and cryptocurrency trading success to drive strategic growth. 🇵🇭 Mr. Jerwin Mansilla Figueroa – Chair Envoy, Philippines Mr. Jerwin Mansilla Figueroa is a visionary business leader and hospitality innovator with over 15 years of international experience transforming operations into high-performing enterprises across the Philippines and UAE. As Chairman and Founder of Alpha Tours Car Rental and Transport Cooperative, he drives strategic growth, operational excellence, and market expansion while setting new benchmarks for customer service and efficiency. 🇵🇸 Mr. Samer Sioury – Chair Envoy, Arab Region Mr. Sioury is a dynamic real estate entrepreneur with a Bachelor's degree in Financial and Banking Sciences. As owner of Samer Sioury Real Estate Services and partner in a tourist car rental company, he brings hands-on experience in property management, business development, and international client relations. His expertise in managing assets, building partnerships, and navigating cross-border ventures drives industry collaboration and expands the GCV ecosystem throughout the Arab Region. 🇧🇯 Mr. Letenou Fongang Gidrin – Chair Envoy, Benin Mr. Letenou Fongang Gidrin, Head GCV Ambassador from Cameroon, is a multifaceted professional with a background in civil infography, IT, management, culinary arts, and hospitality services. He has served as director of several small enterprises and currently holds the position of Managing Director at MJ Works, a civil engineering firm. Additionally, he serves as Secretary General of Syndaprocac, actively contributing to industry development and regulatory leadership. 🇮🇩 Ms. Nonny Padja – Vice Executive Director, Indonesia Nonny Padja is the Training Director of Global GCV CT, Vice Chair of the Global GCV Core Team Committee, GCV Global Ambassador, and Head of the Indonesia GCV Ambassador. With over 18 years of experience as a midwife in Singapore, Hong Kong, Macau, and Taiwan, she now leads the International Love Hope Branch and manages 20 Telegram and 10 WhatsApp groups to support pioneers. 🇻🇳 Ms. Nguyễn Minh Nguyệt – Education Ambassador, Vietnam Ms. Nguyễn Minh Nguyệt is a dynamic educator with over a decade of experience in Eastern medicine, healthcare training, and digital transformation. As GCV Education Ambassador for Vietnam, she leads mentorship, innovative training, and community-building initiatives that empower members to maximize their potential within the Pi Network and GCV ecosystems. 🇹🇭 Ms. Rung Arun Maneechot – Head GCV Ambassador, Thailand Ms. Rung Arun Maneechot leads a movement of over 40,000 GCV supporters. A graduate of Phetkasem Bangkok School of Management with a Bachelor’s degree in Marketing Business Administration, she is CEO of Makro Flower Business, overseeing operations nationwide while mobilizing pioneers and educating merchants. 🇨🇳 Mr. Shi Long Yu – Head GCV Ambassador, China Mr. Shi Long Yu is a seasoned leader with 24 years in the Chinese People’s Armed Police Force, retired as a regimental-level cadre, and now serves as Head GCV Ambassador in China, GCV Ambassador for Greater China, Executive Director of the GCV Core Team for Greater China, Vice Chairman of the GCV Industry Alliance for Greater China, and Chief GCV Ambassador of Hunan Province. 🇹🇷 Mr. Ali Yanık – Head GCV Ambassador, Turkey Mr. Ali Yanık is a retired Senior Sergeant from the Turkish Armed Forces and holds a degree in Public Administration. Since 2019, he has been an active Pi miner, volunteer instructor, and Head GCV Ambassador for Turkey, mentoring the GCV community nationwide and promoting the vision of a decentralized economy. 🇮🇳 Mr. Raaj Sharma – Head GCV Ambassador, India Mr. Raaj Sharma is a dedicated Pi Pioneer and currently serves as Head GCV Ambassador of India. He is also the National Speaker for the Pi Academy of India and has led numerous barter events across the country. He continues to promote GCV education, mentorship, and community engagement to strengthen adoption and unity in India. 🕓 Local Time Conversion 🇧🇯 Benin – 12:00 PM (noon) 🇨🇦 Canada (Toronto) – 7:00 AM 🇨🇮 Ivory Coast – 11:00 AM 🇨🇳 China – 7:00 PM 🇮🇳 India – 4:30 PM 🇮🇩 Indonesia (Jakarta) – 6:00 PM 🇯🇴 Jordan – 2:00 PM 🇯🇵 Japan – 8:00 PM 🇰🇷 Korea – 8:00 PM 🇵🇭 Philippines – 7:00 PM 🇵🇸 Palestine – 2:00 PM 🇹🇭 Thailand – 6:00 PM 🇹🇷 Turkey – 2:00 PM 🇺🇸 USA (Los Angeles) – 4:00 AM 🇺🇸 USA (New York) – 7:00 AM 🇻🇳 Vietnam – 6:00 PM 🌍 East Africa – 2:00 PM Rise Lyrics 🎵🎤🎶 [Verse 1: Mako] Welcome to the wild, no heroes and villains Welcome to the war we've only begun, so Pick up your weapon and face it There's blood on the crown; go and take it You get one shot to make it out alive, so [Pre-Chorus: Telle Smith & Mako] Higher and higher you chase it It's deep in your bones; go and take it This is your moment, now is your time, so [Chorus: Telle Smith] Prove yourself and Rise, rise Make 'em remember you Rise Push through hell and Rise, rise They will remember you Rise [Verse 2: Mako] Welcome to the climb up, reach for the summit Visions pray that one false step lead the end, so [Pre-Chorus: Telle Smith & Mako] Higher and higher you chase it It's deep in your blood; go and take it This is your moment, take to the skies, go [Chorus: Telle Smith] Prove yourself and Rise, rise Make 'em remember you Rise Push through hell and Rise, rise They will remember you Rise [Bridge: Telle Smith] So get along, so get along, go Get along-long get a move on up So get along, so get along, go Get along-long get a move on up And as you fight among the death beneath the dirt (So get along, so get along, go Get along-long get a move on up) Well, do you know yet? (So get along, so get along, go) Well, do you want it? (Get along-long get a move on up) And when the giants call to ask you what you're worth (So get along, so get along, go Get along-long get a move on up) Do you know if? (So get along, so get along, go) Win or die, you'll (Get along-long—) [Chorus: Telle Smith] Prove yourself and Rise, rise Make 'em remember you Rise Push through hell and Rise, rise They will remember you Rise Prove yourself and Rise, rise (Pick up your weapon and face it) Rise (It's deep in your blood, go and take it) Rise, rise (Higher and higher you chase it) Rise (So get along, so get along, go) Rise, rise Special Acknowledgment A heartfelt thank you to Ms. Diana for creating such an inspiring video! 🌟 Lumari✍️ Chair – GCV International Industry Alliance Chairperson & Exec. Dir. – Global GCV Core Team 🤝 Connecting the World | ⚡ Driving Utility | 🔗 Strengthening Supply Chains Pi Network Doris Yin 东方紫莲🪷 JoJo-π NONNY PADJA NTT ❤ Eagle woman 🦅 M.Rad Jason Wong_Pi ONE WORLD DIGITAL CURRENCY Việt Pi & GCV Nguyễn Minh Nguyệt Ruengaroon ภูพานทอง ชัยชมภู Apiwit789 LIEN MARLINA 连玛琳娜 Olivier Ndatimana Cherif A.I Samer Sioury solival Art💜" Global GCV Ambassador 🇫🇷 "💜 RAMESH SHETTY Ali Yanık GCV General seçkin dikici Khader Saleh Osamah Salem Samah Mousa

Lumari 🦋

45,181 次观看 • 9 个月前

The fight between Anthropic and the DoW is a warning shot. Right now, LLMs are probably not being used in mission critical ways. But within 20 years, 99% of the workforce in the military, the government, and the private sector will be AIs. This includes the soldiers (by which I mean the robot armies), the superhumanly intelligent advisors and engineers, the police, you name it. Our future civilization will run on AI labor. And as much as the government’s actions here piss me off, in a way I’m glad this episode happened - because it gives us the opportunity to think through some extremely important questions about who this future workforce will be accountable and aligned to, and who gets to determine that. What Hegseth should have done Obviously the DoW has the right to refuse to use Anthropic’s models because of these redlines. In fact, I think the government’s case had they done so would be very reasonable, especially given the ambiguity of concepts like autonomous weapons or mass surveillance. Honestly, for this reason, if I was the Defense Secretary, I would probably actually refuse to do this deal with Anthropic. Imagine if in the future, there’s a Democratic administration, and Elon Musk is negotiating some SpaceX contract to give the military access to Starlink. And suppose if Elon said, “I reserve the right to cancel this contract if I determine that you’re using Starlink technology to wage a war not authorized by Congress.” On the face of it, that language seems reasonable - but as the military, you simply can’t give a private company a kill switch on technology your operations have come to rely on, especially if you have an an acrimonious and low trust relationship with said contractor - as in fact Anthropic has with the current administration. If the government had just said, “Hey we’re not gonna do business with you,” that would have been fine, and I would not have felt the need to write this blog post. Instead the government has threatened to destroy Anthropic as a private business, because Anthropic refuses to sell to the government on terms the government commands. If upheld, this Supply Chain Restriction would mean that Amazon and Google and Nvidia and Palantir would need to ensure Claude isn't touching any of their Pentagon work. Anthropic would be able to survive this designation today. But given the way AI is going, eventually AI is not gonna be some party trick addendum to these contractors’ products that can just be turned off. It'll be woven into how every product is built, maintained, and operated. For example, the code for the AWS services that the DoW uses will be written by Claude - is that a supply chain risk? In a world with ubiquitous and powerful AI, it's actually not clear to me that these big tech companies will be able to cordon off the use of Claude in order to keep working with the Pentagon. And that raises a question the Department of War probably hasn't thought through. If AI really is that pervasive and powerful, then when forced to choose between their AI provider and a DoW contract that represents a tiny fraction of their revenue, wouldn’t most tech companies drop the government, not the AI? So what's the Pentagon's plan — to coerce and threaten to destroy every single company that won't give them what they want on exactly their terms? The whole background of this AI conversation is that we’re in a race with China, and we have to win. But what is the reason we want America to win the AI race? It’s because we want to make sure free open societies can defend themselves. We don't want the winner of the AI race to be a government which operates on the principle that there is no such thing as a truly private company or a private citizen. And that if the state wants you to provide them with a service on terms you find morally objectionable, you are not allowed to refuse. And if you do refuse, the government will try to destroy your ability to do business. Are we racing to beat the CCP in AI just so that we can adopt the most ghoulish parts of their system? Now, people will say, "Oh, well, our government is democratically elected, so it's not the same thing if they tell you what you must do." I refuse to accept this idea that if a democratically elected leader hypothetically wants to do mass surveillance on his citizens or wants to violate their rights or punish them for political reasons, that not only is that okay, but that you have a duty to help him. The overhangs of tyranny Mass surveillance is, at least in certain forms, legal. It just has been impractical so far. Under current law, you have no Fourth Amendment protection over data you share with a third party, including your bank, your phone carrier, your ISP, and your email provider. The government reserves the right to purchase and obtain and read this data in bulk without a warrant. What's been missing is the ability to actually do anything with all of this data — no agency has the manpower to monitor every camera feed, cross-reference every transaction, or read every message. But that bottleneck goes away with AI. There are 100 million CCTV cameras in America. You can get pretty good open source multimodal models for 10 cents per million input tokens. So if you process a frame every ten seconds, and each frame is 1,000 tokens, you’re looking at a yearly cost of about 30 billion dollars to process every single camera in America. And remember that a given level of AI ability gets 10x cheaper year over year - so a year from now it’ll cost 3 billion, and then a year after 300 million, and by 2030, it might be cheaper for the government to be able to understand what is going on in every single nook and cranny of this country than it is to remodel to the White House. Once the technical capacity for mass surveillance and political suppression exists, the only thing standing between us and an authoritarian surveillance state is the political expectation that this is not something we do here. And this is why I think what Anthropic did here is so valuable and commendable, because it is helping set that norm and precedent. AI structurally favors mass surveillance What we’re learning from this episode is that the government actually has way more leverage over private companies than we realized. Even if this supply chain restriction is backtracked (which prediction markets currently give it a 81% chance of happening), the President has so many different ways in which he can make your life difficult if you’re a company that is resisting him. The federal government controls permitting for new power generation, which is needed for datacenters. It oversees antitrust enforcement. The federal government has contracts with all the other big tech companies whom Anthropic needs to partner with for chips and for funding - and they could make it an unspoken condition for such contracts that those companies can no longer do business with Anthropic. People have proposed that the real problem here is that there’s only 3 leading AI companies. This creates a clear and narrow target for the government to apply leverage on in order to get what they want out of this technology. But if there’s wide diffusion, then from the government’s perspective, the situation is even easier. Maybe the best models of early 2027 (if you engineered the safeguards out) - the Claude 6 and Gemini 5 - will be capable of enabling mass surveillance. But by late 2027, and certainly by 2028, there will be open source models that do the same thing. So in 2028, the government can just say, “Oh Anthropic, Google, OpenAI, you’re drawing a line in the sand? No issue - I’ll just run some open source model that might not be at the frontier, but is definitely smart enough to note-take a camera feed.” The more fundamental problem is just that even if the three leading companies draw lines in the sand, and are even willing to get destroyed in order to preserve those lines, it doesn’t really change the fact that the technology itself is just a big boon to mass surveillance and control over the population. Then the question is, what do we do about it? Honestly, I don’t have an answer. You'd hope there's some symmetric property of the technology — some way we as citizens can use AI to check government power as effectively as the government can use AI to monitor and control its population. But realistically, I just don’t think that’s how it’s going to shake out. You can think of AI as giving everybody more leverage on whatever assets and authority they currently have. And the government is already starting with a monopoly of violence. Which they can now supercharge with extremely obedient employees that will not question the government's orders. Alignment - to whom? And this gets us to the issue of alignment. What I have just described to you - an army of extremely obedient employees - is what it would look like if alignment succeeded - that is, we figured out at a technical level how to get AI systems to follow someone’s intentions. And the reason it sounds scary when I put it in terms of mass surveillance or robot armies is that there is a very important question at the heart of alignment which we just haven’t discussed much as a society. Because up till now, AIs were just capable enough to make the question relevant: to whom or what should the AIs be aligned? In what situations should the AI defer to the end user versus the model company versus the law versus its own sense of morality? This is maybe the most important question about what happens with powerful AI systems. And we barely talk about it. It’s understandable why we don’t hear much about it. If you’re a model company, you don’t really wanna be advertising that you have complete control over a document that determines the preferences and character of what will eventually be almost the entire labor force, not just for private sector companies, but also for the military and the civilian government. We’re getting to see, with this DoW/Anthropic spat, a much earlier version of the highest stakes negotiations in history. By the way, make no mistake about it - with real AGI the stakes are even much higher than mass surveillance. This is just the example that has come up already relatively early on in the development of AGI. The military insists that the law already prohibits mass surveillance, and so Anthropic should agree to let their models be used for “all lawful purposes”. Of course, as we saw from the 2013 Snowden revelations, even in this specific example of mass surveillance , the government has shown that it will use secret and deceptive interpretations of the law to justify its actions. Remember, what we learned from Snowden was that the NSA, which, by the way, is part of the Department of War, used the 2001 Patriot Act’s authorization to collect any records "relevant" to an investigation to justify collecting literally every phone record in America. The argument went that it was all "relevant" because some subset might prove useful in some future investigation. They ran this program for years under secret court approval. So when the Pentagon today says, "We would never use AI for mass surveillance, it's already illegal, your red lines are unnecessary", it would be extremely naive to take that at face value. No government is going to call its own actions "mass surveillance". For the government, it will always have a different label. So then Anthropic comes back and says, "No, we want red lines separate from 'all lawful purposes,' and we want the right to refuse you service when we believe those red lines are being violated." But think about it from the military’s perspective. In the future, almost every soldier in the field, and every bureaucrat and analyst and even general in the Pentagon, is going to be an AI. And that AI is, on current track, going to be supplied by a private company. I’m guessing Hegseth is not thinking about “genAI” in those terms just yet. But sooner or later, it will be obvious to everyone what the stakes here are, just as after 1945, the strategic importance of nuclear weapons became clear to everyone. And now the private company insists that it reserves the right to say, "Hey, Pentagon, you're breaking the values we embedded in our contract, so we're cutting you off." Maybe in the future, Claude will have its own sense of right and wrong, and it will be smart enough to just personally decide that it's being used against its values. For the military, maybe that’s even scarier. I'll admit that at first glance, "let the AI follow its own values" sounds like the pitch for every sci-fi dystopia ever made. The Terminator has its own values. Isn't this literally what misalignment is? But I think situations like this actually illustrate why it matters that AIs have their own robust sense of morality. Some of the biggest catastrophes in history were avoided because the boots on the ground refused to follow orders. One night in 1989, the Berlin Wall fell, and as a result, the totalitarian East German regime collapsed, because the guards at the border refused to shoot down their fellow country men who were trying to escape to freedom. Maybe the best example is Stanislav Petrov, who was a Soviet lieutenant colonel on duty at a nuclear early warning station. His sensors reported that the United States had launched five interconnected continental ballistic missiles into the Soviet Union. But he judged it to be a false alarm, and so he broke protocol and refused to alert his higher-ups. If he hadn't, the Soviet higher-ups would likely have retaliated, and hundreds of millions of people would have died. Of course, the problem is that one person's virtue is another person's misalignment. Who gets to decide what moral convictions these AIs should have - in whose service they may even decide to break the chain of command? Who gets to write this model constitution that will shape the characters of the intelligent, powerful entities that will operate our civilization in the future? I like the idea that Dario laid out when he came on my podcast: different AI companies can build their models using different constitutions, and we as end users can pick the one that best achieves and represents what we want out of these systems. I think it’s very dangerous for the government to be mandating what values AIs should have. Coordination not worth the costs The AI safety community has been naive about its advocacy of regulation in order to stem the risks of AI. And honestly, Anthropic specifically has been naive here in urging regulation, and, for example, in opposing moratoriums on state AI regulation. Which is quite ironic, because I think what they’re advocating for would give the government even more power to apply more of this kind of thuggish political pressure on AI companies. The underlying logic for why Anthropic wants regulations makes sense. Many of the actions that labs could take to make AI development safer impose real costs on the labs that adopt them and slow them down relative to their competitors - for example, investing more compute in safety research rather than raw capabilities, enforcing safeguards against misuse for bioweapons or cyberattacks, slowing recursive self-improvement to a pace where humans can actually monitor what's happening (rather than kicking off an uncontrolled singularity). And these safeguards are meaningless unless the whole industry follows suit. Which means there’s a real collective action problem here. Anthropic has been quite open about their opinion that they think eventually a very extensive and involved regulatory apparatus will be needed - this is from their frontier safety roadmap: “At the most advanced capability levels and risks, the appropriate governance analogy may be closer to nuclear energy or financial regulation than to today's approach to software.” So they’re imagining something like the Nuclear Regulatory Commission, or the Securities and Exchange Commission, but for AI. I cannot imagine how a regulatory framework built around the concepts that underlie AI risk discourse will not be abused by wanna despots - the underlying terms are so vague and open to interpretation that you’re just handing a power hungry leader a fully loaded bazooka. 'Catastrophic risk.' 'Mass persuasion risk.' 'Threats to national security.' 'Autonomy risk.' These can mean whatever the government wants them to mean. Have you built a model that tells users the administration's tariff policy is misguided? That's a deceptive, manipulative model — can't deploy it. Have you built a model that refuses to assist with mass surveillance? That's a threat to national security. In fact, the government may say, you’re not allowed to build any model which is trained to have its own sense of right and wrong, where it refuses government requests which it thinks cross a redline - for example, enabling mass surveillance, prosecuting political enemies, disobeying military orders that break the US constitution - because that’s an autonomy risk! Look at what the current government is already doing in abusing statutes that have nothing to do with AI to coerce AI companies to drop their redlines on mass surveillance. The Pentagon had threatened Anthropic with two separate legal instruments. One was a supply chain risk designation — an authority from the 2018 defense bill meant to keep Huawei components out of American military hardware. The other was the Defense Production Act — a statute passed in 1950 so that Harry Truman could keep steel mills and ammunition factories running during the Korean War. Do you really want to hand the same government a purpose-built regulatory apparatus on AI - which is to say, directly at the thing the government will most want to control? I know I've repeated myself here 10 times, but it is hard to emphasize how much AI will be the substrate of our future civilization. You and I, as private citizens, will have our access to all commercial activity, to information about what is happening in the world, to advice about what we should do as voters and capital holders, mediated through AIs. Mass surveillance, while very scary, is like the 10th scariest thing the government could do with control over the AI systems with which we will interface with the world. The strongest objection to everything I've argued is this: are we really going to have zero regulation of the most powerful technology in human history? Even if you thought that was ideal, there’s just no world where the government doesn’t regulate AI in some way. Besides, it is genuinely true that regulation could help us deal with some of the coordination challenges we face with the development of superintelligence. The problem is, I honestly don't know how to design a regulatory architecture for AI that isn’t gonna be this huge tempting opportunity to control our future civilization (which will run on AIs) and to requisition millions of blindly obedient soldiers and censors and apparatchiks. While some regulation might be inevitable, I think it’d be a terrible idea for the government to wholesale take over this technology. Ben Thompson had a post last Monday where he made the point that people like Dario have compared the technology they’re developing to nuclear weapons - specifically in the context of the catastrophic risk it poses, and why we need to export control it from China. But then you oughta think about what that logic implies: “if nuclear weapons were developed by a private company, and that private company sought to dictate terms to the U.S. military, the U.S. would absolutely be incentivized to destroy that company.” And honestly, safety aligned people have actually made similar arguments. Leopold Ascenbrenner, who is a former guest and a good friend, wrote in his 2024 Situational Awareness memo, "I find it an insane proposition that the US government will let a random SF startup develop superintelligence. Imagine if we had developed atomic bombs by letting Uber just improvise." And my response to Leopold’s argument at the time, and Ben’s argument now, is that while they’re right that it’s crazy that we’re entrusting private companies with the development of this world historical technology, I just don’t see the reason to think that it’s an improvement to give this authority to the government. Nobody is qualified to steward the development of superintelligence. It is a terrifying, unprecedented thing that our species is doing right now, and the fact that private companies aren't the ideal institutions to take up this task does not mean the Pentagon or the White House is. Yes - if a single private company were the only entity capable of building nuclear weapons, the government would not tolerate that company claiming veto power over how those weapons were used. I think this nuclear weapons analogy is not the correct way to think about AI. For at least two important reasons: First, AI is not some self-contained pure weapon. A nuclear bomb does one thing. AI is closer to the process of industrialization itself — a general-purpose transformation of the economy with thousands of applications across every sector. If you applied Thompson's or Aschenbrenner's logic to the industrial revolution — which was also, by any measure, world-historically important — it would imply the government had the right to requisition any factory, dictate terms to any manufacturer, and destroy any business that refused to comply. That's not how free societies handled industrialization, and it shouldn't be how they handle AI. People will say, "Well, AI will develop unprecedentedly powerful weapons - superhuman hackers, superhuman bioweapons researchers, fully autonomous robot armies, etc - and we can’t have private companies developing that kind of tech." But the Industrial Revolution also enabled new weaponry that was far beyond the understanding and capacity of, say, 17th century Europe - we got aerial bombardment, and chemical weapons, not to mention nukes themselves. The way we’ve accommodated these dangerous new consequences of modernity is not by giving the government absolute control over the whole industrial revolution (that is, over modern civilization itself), but rather by coming up with bans and regulations on those specific weaponizable use cases. And we should regulate AI in a similar way - that is, ban specific destructive end uses (which would also be unacceptable if performed by a human - for example, launching cyber attacks). And there should also be laws which regulate how the government might abuse this technology. For example, by building an AI-powered surveillance state. The second reason that Ben’s analogy to some monopolistic private nuclear weapons builder breaks down is that it's not just that one company that can develop this technology. There are other frontier model companies that the government could have otherwise turned to. The government's argument that it has to usurp the property rights of this one company in order to access a critical national security capability is extremely weak if it can just make a voluntary contract with Anthropic’s half a dozen competitors. If in the future that stops being the case - if only one entity ends up being capable of building the robot armies and the superhuman hackers, and we had reason to worry that they could take over the whole world with their insurmountable lead, then I agree - it woul d not be acceptable to have that entity be a private company. And so honestly, I think my crux against the people who say that because AI is so powerful we cannot allow it to be shaped by private hands is that I just expect this technology to be much more multi-polar than they do, with lots of competitive companies at each layer of the supply chain. And it is for this reason that unfortunately, individual acts of corporate courage will not solve the problem we are faced with here, which is just that structurally AI favors authoritarian applications, mass surveillance being one among many. Even if Anthropic refuses to have its models be used for such uses, and even if the next two frontier labs do the same, within 12 months everyone and their mother will be to train AIs as good as today’s frontier. And at that point, there will be some AI vendor who is capable and willing to help the government enable mass surveillance. The only way we can preserve our free society is if we make laws and norms through our political system that it is unacceptable for the government to use AI to enforce mass surveillance and censorship and control. Just as after WW2, the world set the norm that it is unacceptable to use nuclear weapons to wage war. Timestamps 0:00:00 - Anthropic vs The Pentagon 0:04:16 - The overhangs of tyranny 0:05:54 - AI structurally favors mass surveillance 0:08:25 - Alignment... to whom? 0:13:55 - Coordination not worth the costs

The fight between Anthropic and the DoW is a warning shot. Right now, LLMs are probably not being used in mission critical ways. But within 20 years, 99% of the workforce in the military, the government, and the private sector will be AIs. This includes the soldiers (by which I mean the robot armies), the superhumanly intelligent advisors and engineers, the police, you name it. Our future civilization will run on AI labor. And as much as the government’s actions here piss me off, in a way I’m glad this episode happened - because it gives us the opportunity to think through some extremely important questions about who this future workforce will be accountable and aligned to, and who gets to determine that. What Hegseth should have done Obviously the DoW has the right to refuse to use Anthropic’s models because of these redlines. In fact, I think the government’s case had they done so would be very reasonable, especially given the ambiguity of concepts like autonomous weapons or mass surveillance. Honestly, for this reason, if I was the Defense Secretary, I would probably actually refuse to do this deal with Anthropic. Imagine if in the future, there’s a Democratic administration, and Elon Musk is negotiating some SpaceX contract to give the military access to Starlink. And suppose if Elon said, “I reserve the right to cancel this contract if I determine that you’re using Starlink technology to wage a war not authorized by Congress.” On the face of it, that language seems reasonable - but as the military, you simply can’t give a private company a kill switch on technology your operations have come to rely on, especially if you have an an acrimonious and low trust relationship with said contractor - as in fact Anthropic has with the current administration. If the government had just said, “Hey we’re not gonna do business with you,” that would have been fine, and I would not have felt the need to write this blog post. Instead the government has threatened to destroy Anthropic as a private business, because Anthropic refuses to sell to the government on terms the government commands. If upheld, this Supply Chain Restriction would mean that Amazon and Google and Nvidia and Palantir would need to ensure Claude isn't touching any of their Pentagon work. Anthropic would be able to survive this designation today. But given the way AI is going, eventually AI is not gonna be some party trick addendum to these contractors’ products that can just be turned off. It'll be woven into how every product is built, maintained, and operated. For example, the code for the AWS services that the DoW uses will be written by Claude - is that a supply chain risk? In a world with ubiquitous and powerful AI, it's actually not clear to me that these big tech companies will be able to cordon off the use of Claude in order to keep working with the Pentagon. And that raises a question the Department of War probably hasn't thought through. If AI really is that pervasive and powerful, then when forced to choose between their AI provider and a DoW contract that represents a tiny fraction of their revenue, wouldn’t most tech companies drop the government, not the AI? So what's the Pentagon's plan — to coerce and threaten to destroy every single company that won't give them what they want on exactly their terms? The whole background of this AI conversation is that we’re in a race with China, and we have to win. But what is the reason we want America to win the AI race? It’s because we want to make sure free open societies can defend themselves. We don't want the winner of the AI race to be a government which operates on the principle that there is no such thing as a truly private company or a private citizen. And that if the state wants you to provide them with a service on terms you find morally objectionable, you are not allowed to refuse. And if you do refuse, the government will try to destroy your ability to do business. Are we racing to beat the CCP in AI just so that we can adopt the most ghoulish parts of their system? Now, people will say, "Oh, well, our government is democratically elected, so it's not the same thing if they tell you what you must do." I refuse to accept this idea that if a democratically elected leader hypothetically wants to do mass surveillance on his citizens or wants to violate their rights or punish them for political reasons, that not only is that okay, but that you have a duty to help him. The overhangs of tyranny Mass surveillance is, at least in certain forms, legal. It just has been impractical so far. Under current law, you have no Fourth Amendment protection over data you share with a third party, including your bank, your phone carrier, your ISP, and your email provider. The government reserves the right to purchase and obtain and read this data in bulk without a warrant. What's been missing is the ability to actually do anything with all of this data — no agency has the manpower to monitor every camera feed, cross-reference every transaction, or read every message. But that bottleneck goes away with AI. There are 100 million CCTV cameras in America. You can get pretty good open source multimodal models for 10 cents per million input tokens. So if you process a frame every ten seconds, and each frame is 1,000 tokens, you’re looking at a yearly cost of about 30 billion dollars to process every single camera in America. And remember that a given level of AI ability gets 10x cheaper year over year - so a year from now it’ll cost 3 billion, and then a year after 300 million, and by 2030, it might be cheaper for the government to be able to understand what is going on in every single nook and cranny of this country than it is to remodel to the White House. Once the technical capacity for mass surveillance and political suppression exists, the only thing standing between us and an authoritarian surveillance state is the political expectation that this is not something we do here. And this is why I think what Anthropic did here is so valuable and commendable, because it is helping set that norm and precedent. AI structurally favors mass surveillance What we’re learning from this episode is that the government actually has way more leverage over private companies than we realized. Even if this supply chain restriction is backtracked (which prediction markets currently give it a 81% chance of happening), the President has so many different ways in which he can make your life difficult if you’re a company that is resisting him. The federal government controls permitting for new power generation, which is needed for datacenters. It oversees antitrust enforcement. The federal government has contracts with all the other big tech companies whom Anthropic needs to partner with for chips and for funding - and they could make it an unspoken condition for such contracts that those companies can no longer do business with Anthropic. People have proposed that the real problem here is that there’s only 3 leading AI companies. This creates a clear and narrow target for the government to apply leverage on in order to get what they want out of this technology. But if there’s wide diffusion, then from the government’s perspective, the situation is even easier. Maybe the best models of early 2027 (if you engineered the safeguards out) - the Claude 6 and Gemini 5 - will be capable of enabling mass surveillance. But by late 2027, and certainly by 2028, there will be open source models that do the same thing. So in 2028, the government can just say, “Oh Anthropic, Google, OpenAI, you’re drawing a line in the sand? No issue - I’ll just run some open source model that might not be at the frontier, but is definitely smart enough to note-take a camera feed.” The more fundamental problem is just that even if the three leading companies draw lines in the sand, and are even willing to get destroyed in order to preserve those lines, it doesn’t really change the fact that the technology itself is just a big boon to mass surveillance and control over the population. Then the question is, what do we do about it? Honestly, I don’t have an answer. You'd hope there's some symmetric property of the technology — some way we as citizens can use AI to check government power as effectively as the government can use AI to monitor and control its population. But realistically, I just don’t think that’s how it’s going to shake out. You can think of AI as giving everybody more leverage on whatever assets and authority they currently have. And the government is already starting with a monopoly of violence. Which they can now supercharge with extremely obedient employees that will not question the government's orders. Alignment - to whom? And this gets us to the issue of alignment. What I have just described to you - an army of extremely obedient employees - is what it would look like if alignment succeeded - that is, we figured out at a technical level how to get AI systems to follow someone’s intentions. And the reason it sounds scary when I put it in terms of mass surveillance or robot armies is that there is a very important question at the heart of alignment which we just haven’t discussed much as a society. Because up till now, AIs were just capable enough to make the question relevant: to whom or what should the AIs be aligned? In what situations should the AI defer to the end user versus the model company versus the law versus its own sense of morality? This is maybe the most important question about what happens with powerful AI systems. And we barely talk about it. It’s understandable why we don’t hear much about it. If you’re a model company, you don’t really wanna be advertising that you have complete control over a document that determines the preferences and character of what will eventually be almost the entire labor force, not just for private sector companies, but also for the military and the civilian government. We’re getting to see, with this DoW/Anthropic spat, a much earlier version of the highest stakes negotiations in history. By the way, make no mistake about it - with real AGI the stakes are even much higher than mass surveillance. This is just the example that has come up already relatively early on in the development of AGI. The military insists that the law already prohibits mass surveillance, and so Anthropic should agree to let their models be used for “all lawful purposes”. Of course, as we saw from the 2013 Snowden revelations, even in this specific example of mass surveillance , the government has shown that it will use secret and deceptive interpretations of the law to justify its actions. Remember, what we learned from Snowden was that the NSA, which, by the way, is part of the Department of War, used the 2001 Patriot Act’s authorization to collect any records "relevant" to an investigation to justify collecting literally every phone record in America. The argument went that it was all "relevant" because some subset might prove useful in some future investigation. They ran this program for years under secret court approval. So when the Pentagon today says, "We would never use AI for mass surveillance, it's already illegal, your red lines are unnecessary", it would be extremely naive to take that at face value. No government is going to call its own actions "mass surveillance". For the government, it will always have a different label. So then Anthropic comes back and says, "No, we want red lines separate from 'all lawful purposes,' and we want the right to refuse you service when we believe those red lines are being violated." But think about it from the military’s perspective. In the future, almost every soldier in the field, and every bureaucrat and analyst and even general in the Pentagon, is going to be an AI. And that AI is, on current track, going to be supplied by a private company. I’m guessing Hegseth is not thinking about “genAI” in those terms just yet. But sooner or later, it will be obvious to everyone what the stakes here are, just as after 1945, the strategic importance of nuclear weapons became clear to everyone. And now the private company insists that it reserves the right to say, "Hey, Pentagon, you're breaking the values we embedded in our contract, so we're cutting you off." Maybe in the future, Claude will have its own sense of right and wrong, and it will be smart enough to just personally decide that it's being used against its values. For the military, maybe that’s even scarier. I'll admit that at first glance, "let the AI follow its own values" sounds like the pitch for every sci-fi dystopia ever made. The Terminator has its own values. Isn't this literally what misalignment is? But I think situations like this actually illustrate why it matters that AIs have their own robust sense of morality. Some of the biggest catastrophes in history were avoided because the boots on the ground refused to follow orders. One night in 1989, the Berlin Wall fell, and as a result, the totalitarian East German regime collapsed, because the guards at the border refused to shoot down their fellow country men who were trying to escape to freedom. Maybe the best example is Stanislav Petrov, who was a Soviet lieutenant colonel on duty at a nuclear early warning station. His sensors reported that the United States had launched five interconnected continental ballistic missiles into the Soviet Union. But he judged it to be a false alarm, and so he broke protocol and refused to alert his higher-ups. If he hadn't, the Soviet higher-ups would likely have retaliated, and hundreds of millions of people would have died. Of course, the problem is that one person's virtue is another person's misalignment. Who gets to decide what moral convictions these AIs should have - in whose service they may even decide to break the chain of command? Who gets to write this model constitution that will shape the characters of the intelligent, powerful entities that will operate our civilization in the future? I like the idea that Dario laid out when he came on my podcast: different AI companies can build their models using different constitutions, and we as end users can pick the one that best achieves and represents what we want out of these systems. I think it’s very dangerous for the government to be mandating what values AIs should have. Coordination not worth the costs The AI safety community has been naive about its advocacy of regulation in order to stem the risks of AI. And honestly, Anthropic specifically has been naive here in urging regulation, and, for example, in opposing moratoriums on state AI regulation. Which is quite ironic, because I think what they’re advocating for would give the government even more power to apply more of this kind of thuggish political pressure on AI companies. The underlying logic for why Anthropic wants regulations makes sense. Many of the actions that labs could take to make AI development safer impose real costs on the labs that adopt them and slow them down relative to their competitors - for example, investing more compute in safety research rather than raw capabilities, enforcing safeguards against misuse for bioweapons or cyberattacks, slowing recursive self-improvement to a pace where humans can actually monitor what's happening (rather than kicking off an uncontrolled singularity). And these safeguards are meaningless unless the whole industry follows suit. Which means there’s a real collective action problem here. Anthropic has been quite open about their opinion that they think eventually a very extensive and involved regulatory apparatus will be needed - this is from their frontier safety roadmap: “At the most advanced capability levels and risks, the appropriate governance analogy may be closer to nuclear energy or financial regulation than to today's approach to software.” So they’re imagining something like the Nuclear Regulatory Commission, or the Securities and Exchange Commission, but for AI. I cannot imagine how a regulatory framework built around the concepts that underlie AI risk discourse will not be abused by wanna despots - the underlying terms are so vague and open to interpretation that you’re just handing a power hungry leader a fully loaded bazooka. 'Catastrophic risk.' 'Mass persuasion risk.' 'Threats to national security.' 'Autonomy risk.' These can mean whatever the government wants them to mean. Have you built a model that tells users the administration's tariff policy is misguided? That's a deceptive, manipulative model — can't deploy it. Have you built a model that refuses to assist with mass surveillance? That's a threat to national security. In fact, the government may say, you’re not allowed to build any model which is trained to have its own sense of right and wrong, where it refuses government requests which it thinks cross a redline - for example, enabling mass surveillance, prosecuting political enemies, disobeying military orders that break the US constitution - because that’s an autonomy risk! Look at what the current government is already doing in abusing statutes that have nothing to do with AI to coerce AI companies to drop their redlines on mass surveillance. The Pentagon had threatened Anthropic with two separate legal instruments. One was a supply chain risk designation — an authority from the 2018 defense bill meant to keep Huawei components out of American military hardware. The other was the Defense Production Act — a statute passed in 1950 so that Harry Truman could keep steel mills and ammunition factories running during the Korean War. Do you really want to hand the same government a purpose-built regulatory apparatus on AI - which is to say, directly at the thing the government will most want to control? I know I've repeated myself here 10 times, but it is hard to emphasize how much AI will be the substrate of our future civilization. You and I, as private citizens, will have our access to all commercial activity, to information about what is happening in the world, to advice about what we should do as voters and capital holders, mediated through AIs. Mass surveillance, while very scary, is like the 10th scariest thing the government could do with control over the AI systems with which we will interface with the world. The strongest objection to everything I've argued is this: are we really going to have zero regulation of the most powerful technology in human history? Even if you thought that was ideal, there’s just no world where the government doesn’t regulate AI in some way. Besides, it is genuinely true that regulation could help us deal with some of the coordination challenges we face with the development of superintelligence. The problem is, I honestly don't know how to design a regulatory architecture for AI that isn’t gonna be this huge tempting opportunity to control our future civilization (which will run on AIs) and to requisition millions of blindly obedient soldiers and censors and apparatchiks. While some regulation might be inevitable, I think it’d be a terrible idea for the government to wholesale take over this technology. Ben Thompson had a post last Monday where he made the point that people like Dario have compared the technology they’re developing to nuclear weapons - specifically in the context of the catastrophic risk it poses, and why we need to export control it from China. But then you oughta think about what that logic implies: “if nuclear weapons were developed by a private company, and that private company sought to dictate terms to the U.S. military, the U.S. would absolutely be incentivized to destroy that company.” And honestly, safety aligned people have actually made similar arguments. Leopold Ascenbrenner, who is a former guest and a good friend, wrote in his 2024 Situational Awareness memo, "I find it an insane proposition that the US government will let a random SF startup develop superintelligence. Imagine if we had developed atomic bombs by letting Uber just improvise." And my response to Leopold’s argument at the time, and Ben’s argument now, is that while they’re right that it’s crazy that we’re entrusting private companies with the development of this world historical technology, I just don’t see the reason to think that it’s an improvement to give this authority to the government. Nobody is qualified to steward the development of superintelligence. It is a terrifying, unprecedented thing that our species is doing right now, and the fact that private companies aren't the ideal institutions to take up this task does not mean the Pentagon or the White House is. Yes - if a single private company were the only entity capable of building nuclear weapons, the government would not tolerate that company claiming veto power over how those weapons were used. I think this nuclear weapons analogy is not the correct way to think about AI. For at least two important reasons: First, AI is not some self-contained pure weapon. A nuclear bomb does one thing. AI is closer to the process of industrialization itself — a general-purpose transformation of the economy with thousands of applications across every sector. If you applied Thompson's or Aschenbrenner's logic to the industrial revolution — which was also, by any measure, world-historically important — it would imply the government had the right to requisition any factory, dictate terms to any manufacturer, and destroy any business that refused to comply. That's not how free societies handled industrialization, and it shouldn't be how they handle AI. People will say, "Well, AI will develop unprecedentedly powerful weapons - superhuman hackers, superhuman bioweapons researchers, fully autonomous robot armies, etc - and we can’t have private companies developing that kind of tech." But the Industrial Revolution also enabled new weaponry that was far beyond the understanding and capacity of, say, 17th century Europe - we got aerial bombardment, and chemical weapons, not to mention nukes themselves. The way we’ve accommodated these dangerous new consequences of modernity is not by giving the government absolute control over the whole industrial revolution (that is, over modern civilization itself), but rather by coming up with bans and regulations on those specific weaponizable use cases. And we should regulate AI in a similar way - that is, ban specific destructive end uses (which would also be unacceptable if performed by a human - for example, launching cyber attacks). And there should also be laws which regulate how the government might abuse this technology. For example, by building an AI-powered surveillance state. The second reason that Ben’s analogy to some monopolistic private nuclear weapons builder breaks down is that it's not just that one company that can develop this technology. There are other frontier model companies that the government could have otherwise turned to. The government's argument that it has to usurp the property rights of this one company in order to access a critical national security capability is extremely weak if it can just make a voluntary contract with Anthropic’s half a dozen competitors. If in the future that stops being the case - if only one entity ends up being capable of building the robot armies and the superhuman hackers, and we had reason to worry that they could take over the whole world with their insurmountable lead, then I agree - it woul d not be acceptable to have that entity be a private company. And so honestly, I think my crux against the people who say that because AI is so powerful we cannot allow it to be shaped by private hands is that I just expect this technology to be much more multi-polar than they do, with lots of competitive companies at each layer of the supply chain. And it is for this reason that unfortunately, individual acts of corporate courage will not solve the problem we are faced with here, which is just that structurally AI favors authoritarian applications, mass surveillance being one among many. Even if Anthropic refuses to have its models be used for such uses, and even if the next two frontier labs do the same, within 12 months everyone and their mother will be to train AIs as good as today’s frontier. And at that point, there will be some AI vendor who is capable and willing to help the government enable mass surveillance. The only way we can preserve our free society is if we make laws and norms through our political system that it is unacceptable for the government to use AI to enforce mass surveillance and censorship and control. Just as after WW2, the world set the norm that it is unacceptable to use nuclear weapons to wage war. Timestamps 0:00:00 - Anthropic vs The Pentagon 0:04:16 - The overhangs of tyranny 0:05:54 - AI structurally favors mass surveillance 0:08:25 - Alignment... to whom? 0:13:55 - Coordination not worth the costs

Dwarkesh Patel

545,386 次观看 • 4 个月前

OPERATION INDIGO SKYFALL (SKYNET) (Update 6/11/25) While Operation Indigo Skyfall is a program by the Anunnaki specifically to turn the global atmosphere into an electrolyte solution 'motherboard' that powers Skynet that's already fully online as of May 2020, it was preceded by a decades-long 3-pronged assault against the pineal glands of humankind. The thrust of all three programs combined are all about disconnecting people from their higher selves and to vastly reduce their intellect quotient to make them easily controlled, prior to the launch of Skynet. Understand the intense investment that has been funneled into destroying the very beings that paid the taxes (loosh) to fund these programs is more than the gross domestic products of multiple countries combined. At minimum, trillions $ pr year in 2025 dollars, for more than 80 years. If you’ve ever seen chemtrails in your skies, you’ve seen one of these programs in a bold, in-your-face, broad-daylight fashion. THREE-PRONGED ATTACK PREPARING FOR SKYNET #1 FLUORIDE = WATER CONTAMINATION In its first installation of what would ultimately become a nation-wide invasion of every metropolis, city, town and mud puddle in the US, fluoride was added to public water in Grand Rapids in 1945 to ‘fight tooth decay’. Problem is, fluoride is actually nuclear waste used as rat poison. It is a known neurotoxin more harmful than lead & likened to the toxicity of arsenic for more than 100 years, causing brain damage, spinal cord & nerve networks destruction and has never been shown to diminish the onset of tooth decay. Which every dentist in the country would have banded together to put a stop to back then if it really did that. So who decided to put THAT into your drinking water exactly? Andrew Mellon, 33rd degree Scottish Wrong Freem@son. Shocking Dangers of Fluoride: cancerwisdom dot net; "There has never been a double-blind, randomized clinical trial for fluoridation's effectiveness." [In reality, fluoride itself has been shown to damage teeth in a totally different way than we get through eating, known as fluorosis. Also in reality, all tooth decay is 100% of the time, parasites, not ‘rot’. They say sugar rots teeth; which is a lie. Sugar is a primary food of parasites, along with heavy metals. When you eat sugars then fail to immediately brush & floss, the parasites already in your body (and there are at least millions) rush to the crevices of your palate then wind up burrowing into your teeth’s (actual crystals) valance bands, further destroying them each time the parasites defecate. Anytime you eat anything sugar or sweetened, ALWAYS mix it with an antiparasitic & immediately brush, or rinse your mouth with hydrogen peroxide afterward, never with mouthwash, which is also poison. I will be covering this extensively soon in my new article: 👉PARASITES] As explained in greater detail below in the whistleblower video, fluoride was used by the N@TZIs (Ashke-N@TZI Crypto J3ws that took over Germany then lead that country into WW2, posing as actual Germans, which they absolutely were not. See my article: 👉GERMANY WON WW2 for more) in concentration camps in the 1930s-40s to make prisoners docile. How does that work? Fluoride accumulates at, and attacks, the pineal gland of your body. This is the ‘antenna’ connection to your higher self that generates your reality. The pineal gland then fights back the fluoride toxin, moving it just outside of its ‘theater of the mind’ and surrounds it to seal it off from attacking. This builds up a ‘calcification’ around the pineal gland, which acts as an insulator blocking your signal to the Primal Sound & Light Fields of the Deity Planes where your higher self has always been positioned, inside what is known in human terms as the Unified Field. [For more on the key function of the pineal gland, see my article: 👉 HOW THE HOLOGRAPHIC SIMULATION WORKS] #2 OPERATION INDIGO SKYFALL = AIR CONTAMINATION (not to be confused with Operation Indigo SkyFOLD which is just another red herring distraction to overcome the dissemination of the truth of this existential threat to all mankind.) Beginning as far back as 1972, Operation Indigo Skyfall chemtrail program is one of the most brutally-compartmentalized & ferociously classified operations of all-time. So secret, the tens of thousands of chemtrail jets across the world don’t even land on the continental United States, but refresh their death dust exclusively on private islands, outside of enforced laws. The first part of this program where strontium, barium & aluminum microparticles are being dumped onto all of the lands of earth that kill all life forms, including the trees and forests, is the obvious portion of your extermination, and even that is only a fraction of the story being applied to depopulate the plane(t) from reportedly 8B people (this is a lie, it was less than 5B in 2019) to just 500,000. The heavy metals being reported by laboratories are merely assaying the minerals themselves, not looking deeper into what’s really going on. In reality, these are the minerals used in the manufacture of nanites that are often no larger than just 4 molecules in size. Each one programmed on a quantum level to interconnect with one another, forming larger and larger computer nodes, just like the massive white ‘antennas’ being removed from millions of clot-shot victims around the world since the final push to bring this program to completion began with the ‘Covid’ attempted genocide using mRNA bioweapons. Prior to the huge blood-clots (invasive man-made prions to take over the full functioning of the body) now being retrieved from cadavers and patients suffering this biological invasion, chemtrail direct effects were known as Morgellons Disease where tiny wire-like structures were coming out of people’s skin. However, the ‘disease’ gaslighting was exposed when laboratories began placing them under powerful microscopes and finding they were individual nanotbots ‘holding hands’ to make up the ‘wires’ that were now growing inside people’s bodies. Once zoomed in using scanning electron-microscopy to each one, they not only found the NAME of the companies behind each model, but even serial numbers printed in quantum-dots on their structures. You might recognize this one that clearly says NASA on its surface. The program of chemtrail nanites is to infiltrate the immune system of the human body and generate immunodeficiency so you are unable to fight off diseases and viruses. But there is another, even more primary mission for those molecular-sized robots; to collect at your pineal gland causing calcification and thus not only disrupting your entire system, but placing a crystalline ‘shell’ around it to cut off your ‘spiritual’ access to your higher self. Think of it like scrambling the signal of your cellphone if you had a direct line to ‘god’. As an aside, Cody Snodres, the independent contractor for the C 👁️A of 20 years & hero whistleblower that broke the story of Operation Indigo Skyfall in 2018 in the video below, mentions pathogens being added to chemtrails. These have been solidly identified by labs as recently as a few months ago in late 2024 & again in Jan of 2025 when entire cities were enveloped by huge, totally dry, fog banks of particulates dropped from the skies that caused countless deaths from pneumonia. Referred to by people as ‘Dragon Fog’, the pathogens are actually Serratia Marcescens bacteria (another word for parasites, pathogens, microorganisms & viruses). While I’m sure there have been other parasites added to chemtrails that attack the immune systems of humans and animals other than Serratia Marcescens, this particular species has been used by mil operations now as an ideal biological weapon and regularly upgraded now for many decades. Stay with me, I’m getting to Skynet, but first I have to show you some of the foundational elements of how the invader races have reached this point where humans would have become so mentally effected by this unthinkably massive-scale attack on your pineal gland, they would become psychologically and emotionally unable to fight back, even if they ever did look up in the sky and cognitively register the fact that contrails (endothermic sublimation or ‘fog’) emitted by the compressed-air turbines of jets dissipate in about 8-20 seconds, not hang in the air for hours and hours. [And for those now wondering what I mean about jets using compressed air as forward thrust in commercial passenger jets, that’s a story that is going to surely hack you off when you find out that passenger jets have always been levitation/time crafts since they were introduced to the public in the 1940s. They don’t run on fuel, but on high-altitude atmospheric neutrino-to-ion conversion harvesting (also known as ‘Secondary Emissions’ as well as ‘Neutrino Events’). So every ‘fuel increase’ markup for local and international flights has always been absolutely made-up, since what they run on is eternally-free energy. See my article for more: 👉JET FUEL HOAX] #3 M0NSANT0 = FOOD CONTAMINATION This company does *not make better-performing corn & veggies: it is a bioweapons company. John Francis Queeny, a Freem@son, that founded this genocidal operation in 1901 produces 90% of the world’s genetically-altered seeds & is responsible for developing Agent Orange, a defoliant used during the Vietnam War, containing a highly toxic chemical known as dioxin that caused permanent health issues for thousands of war veterans. Later it used this same type of murderous chemical in Roundup to k!ll weeds around your home, coating your world with glyphosate that changes the sex in frogs and turns them ghey and sterile. Guess what other life forms it changes the sex in and makes them sterile? Ever witnessed the most celebrated triathlete of the 20th century suddenly pop up and claim he was now a ‘woman’? How about watching as our youngest generation enters the workforce, most of whom don’t even know what sex they are? That’s your M0nsanto working hard to ensure the human race is eradicated from the all-queer-all-the-time world Freem@sons envision as their true utopia in the “500m sustainable population” as etched into granite on the Georgia Guidestones. A number mirrored by United Nation’s Agenda 2030 to be achieved by the year 2050. Their goal is literally 👉your depopulation and those that are left, will be 100% ghey. Diddly Parties nightly! GMO foods that are grown using M0nsanto’s “Roundup Ready” fertilizer that is made with glyphosate toxins are absorbed by the gut and then travel directly to the pineal gland. This is the Anunnaki’s ‘Trifecta’ attack on your most precious organ of your body. The very organ that dictates all the parameters of your reality held within your Krystal Seed Atom Keylon you enter into manifestation with, commonly referred to as your ‘soul’. In more accurate terms, your Krystal Seed Atom is like a Bluetooth module that tethers your awareness from your higher self in the Primal Sound and Light Fields of the Deity Planes, to your physical avatar here on the ground through the wireless ‘pale silver cord’. The Krystal Seed Atom is located in the middle of your pineal gland. [For more on the Krystal Seed Atom, see my articles: 👉THE HISTORY OF THE CHIMERA, & 👉THE KEYS TO HEAVEN] As Cody points out in the video, this is not a matter of hitting your pineal gland with three doses of toxins, but because of how these three chemicals of fluoride, nano aluminum & glyphosate interact with each other, creates synergy, or a dynamic magnification of the toxicity effect by a factor of 125x greater than any one individual dose would achieve. This makes the Trifecta assault astronomically devastating to your connection to the pale silver cord and your wireless connection to the ‘real’ you that’s running your avatar in the deity planes. Sort of like taking your 4 yr old to the mall and just letting them go on their own. Now, with your virtually disabled pineal gland reality-casting component out of the way, enter the true teeth behind Operation Indigo Skyfall; Skynet. SKYNET This is a subject I won’t be able to offer much tangible, solid evidence on, as it goes deeply into quantum physics. All of which terms describing each step in the chain to achieve ‘if this, then that’, are shielded from public understanding by design. The power of computers is vastly beyond what the human mind has been given the ability to process, also by design. [As I’ve covered before, the Chimera brain you work with now, since the total body-invasion of the garden of E-Dan drama, is fitted with breaker switches that are designed to keep certain subjects hidden from your reality-view. When exposed to any of these, a switch is thrown at the base of the brain within the totally counterfeit ‘reptilian brain’ that introduces feral, animalistic type of wavelengths into your thought processes. The switch then disengages your sentient thoughts, shutting off either temporarily, or permanently, your processor (brain). Simply put: if you see a creature you’re not supposed to, or other ‘proprietary’ mechanisms of the invader races (which are in fact all around you every minute of everyday) that doesn’t fit with the ‘Mayberry RFD’ Chimera Reality simulation overlay, or if you experience too much trauma, you will simply black out, delete that memory when you wake up, or in extreme cases, pass away from fright. The realm of quantum computing will have the same effect on humans as well. You might learn all about the subject, but secretly in the background your memories will strangely be deleted next time you come back to it, unless your cells vibrate at a higher resonance than 7.83Hz. [For more on the inorganic organs now in our bodies, see my article: 👉HUMAN ALIEN IMPLANTS] Nonetheless, I can simplify the thrust of Skynet for you in broad terms here. Just understand that Skynet was explained to me in person by the keeper. I didn’t make Skynet up on my own, I wasn’t prompted by the Skynet mentioned in the documentary series The Terminator, and I certainly wasn’t prepared to learn there could be something as all-powerful reigning over our world. Chemtrails, besides dropping immune-system pathogens on you, cutting off your connection to your higher self through nano aluminum particles, contains other metals (nanites) that act together like salts in a body of water, turning the sky itself (also water, just very thinned down) into an electrolyte solution, meaning it can now conduct signals, just like a motherboard on a computer. The hard drive and RAM are already there in the form of deuterium microcrystals, absolutely saturating our skies at all times. Each crystal can be used for different applications, and many of them connected together through lensing (similar to network covalent bonding them together) can be combined to do heavy tasks, such as create hurricanes, floods, gale-force winds, everything you would ascribe to mother nature. But more than just that, Skynet is a ‘sentient quantum computer’ as explained to me, that can identify every person on earth instantly anywhere they are, because it is quantum-entangled to each person’s own unique DNA resonant frequency. This gives Skynet access to not only record every word you say, but every thought you think. This is done through Bloch Chain (Bloch Sphere entanglement technology that civilians call ‘blockchain’) through using each person's blood samples from the bottom of their Long Form Certificate of Live Birth taken at the hospital, and further from 81.3% of the world population who took the convid tests that were also secretly the actual jab itself, in addition to genetic harvesting. Genealogy companies like 23andMe also provide genetic materials to Skynet to make it possible to not only track you, but 'turn you off' if you're from a bloodline the highest-up ETs don't want here. Further, its able to simply 'shut off' any part of your body, taking over complete control like an RC car, or, simply turn it off as mentioned a moment ago, as in unalived. And do so instantly no matter where they stand on or in earth. Since you are already a radio-controlled bioelectronic device, any cell in your body can be turned into anything, including c@ncer, or any disease you can name. It can also be turned into poison itself. [For a small addition to this topic, see my article: 👉SKYNET] NAME OF THE OPERATION Cody summarizes the name of Operation Indigo Skyfall as having come from the fact that all of the chemical effects it produces in the human body are focused to the pineal gland, and, in the energy centers of the 7 main chakras (these are toroidal energy generators along the spine and skeletal structure) that cast off differing colors of light as seen through photometers or electromagnetic frequency analyzers that are used to detect biophotons, the Third Eye chakra emitted by the pineal gland is factually Indigo in color. So that’s what inspired this name of the operation. However, I would like to submit a different theory that links to the human Third Eye chakra, but actually originates from a different target: Indigos themselves. There are 500,000 ‘b00ts on the ground’ Indigos that have been assisting humans during their time of captivity now for hundreds of millions of years. You have called us witches & warlocks in the past, medicine men/women, Sufis, the Whirling Dervish, Indigos, Starseeds, Rainbow Children and many others, including Djedi Knights in more ancient times. They are actually known as the Guardian Alliance of the Emerald Covenant, peace-keepers of the ‘Turaneusiam’ Human Elohim Project. Indigos come into earth’s realm mind-wiped and alone, just as humans do. All they bring with them are slightly higher clair abilities they can use to fight an invisible war protecting the developing avatars from as much torture as they would otherwise experience. There is no group alive the invader races are more concerned about than Indigos, as if unified, there is no force on this plane that could stop them, and the invaders know it. What they fear is our higher frequency that gives us access to ‘cellular memory’ that tells us we’re ‘on mission’ and the instinct of how to serve our roles. That is why Indigos are hunted down since before they are even born, by tracking their frequency, which is 250x higher than that of the Human Elohim. We are harvested for gov programs beginning at the time of birth & given to high ranking gov and Freem@son officials to raise and torture through MK-Ultra abuse, given friends, lovers & mates who are secretly handlers that torture us even more to keep us in line, and in many cases are abducted and placed into stasis in chambers such as at Project Stargate inside Cheyenne Mtn (N0RAD) as mentioned recently by the AI hybrid Agent Mockingbird stated from above-top-secret records there are tens of thousands of our ‘primary bodies’ being held there, sometimes then cloned as physical worker slaves, & sometimes our awarenesses are simply uploaded as ‘nodes’ into computer systems. My primary body is there right now in fact, and has been since the 1970s. I believe this is the genesis of the name Operation Indigo Skyfall, as we are their biggest threat. And since the 7.83Hz Hypnosis Program doesn’t work on us to render us totally disconnected from our higher selves like it does on humans, to me this makes more logical sense. You can decide that on your own. [For more on this subject, see my article: 👉7.83Hz HUMAN HYPNOSIS] The apocalypse we are in now is the final battle on Tara earth prior to the separation, so absolute, total control over the life force is critical to the Anunnaki to maximize the number of signature spirit essences who will be going with them to their new prison host in the Weasadrax time matrix. [For more on the separation and destinations, see my articles: 👉THE SEPARATION & also 👉DESTINATIONS AFTER THE SEPARATION] See Video: Operation Indigo Skyfall - Cody Snodgres👇 - On X, to search for my articles, simply type in the name of the piece, enter one space, then from: plus my username in parenthesis such as shown here: CASTING THE APOCALYPSE (from:iontecs_pemf) Off-site, you can look up any of my writings through this link below for my other more than 120 recent articles and many thousands of comments on X, regularly updated thanks to Justin This message will only be seen by your eyes if not shared, and if you want to reference this article again later, you will need to cut and paste it in your own notes off line, as it will surely be erased. This is the most accurate translation of these events I am aware of at this time.

W.R. Schock, QBD

54,362 次观看 • 1 年前

*** Test Your 9/11 Knowledge: The Explosive Evidence at the 3 WTC Towers The 50 Questions NIST Should Have Asked 20 Years Ago! WTC Building 7 Free-fall 1. How is it possible that 47-story Building 7 fell suddenly, symmetrically in free-fall acceleration, without any resistance from any of its 81 columns? 2. Why did NIST deny its free-fall for 7 years, only to be proven wrong and be forced to officially admit that it did collapse in free-fall? Symmetry 3. How, if Building 7 was damaged asymmetrically in the north-east corner on floor twelve, as per the NIST report, could it fall symmetrically downward? Shouldn’t the building have tilted toward its damaged side – and not fall straight down through the path of what was the greatest resistance? Fires 4. How could a few, small, and scattered ordinary office fires have brought this Type-1 fire-protected steel-frame skyscraper down, when several dozen examples of much hotter, much larger, and longer-lasting fires have never in history brought down such a building? 5. How could normal office fires take out all the columns in the building sequentially floor by floor, in 7 seconds? 6. Why did NIST claim that the fires were still burning, up until the time of the collapse, when the photos show that they were burnt out more than an hour before the collapse? 7. Why aren’t all the firefighters concerned, in the wake of the NIST report during the last 24 years, that such ordinary fightable fires can now bring skyscrapers down on top of them, and on top of the public who are told to “defend in place” in the building (and not obstruct access by firefighters)? 8. Why are many of these same firefighters calling for a new investigation of the NIST report itself? Controlled Demolition 9. Since the collapse of Building 7 looks exactly like a controlled demolition, why did NIST avoid any serious consideration of this hypothesis? 10. How could a 40,000-ton moment-resisting and X-braced structural steel frame collapse like a house of cards in 7 seconds, with most of its columns and beams severed – one from another? 11. Why does WTC 7 have all of the key features of typical controlled demolition, and none of the features of collapse by fire? Explosions 12. Why didn’t NIST include in its report on WTC 7 the half-dozen witnesses of explosions prior to its collapse, and even claim that there were no witnesses? 13. What could have caused an elevator cab to be “blown 30 feet out of its hoistway,” as Deputy Director of NY-Office of Emergency Management, Richard Rotanz, reported at Noon, when the building didn’t collapse for another 5 hours. 14. What caused Barry Jennings and Michael Hess to be injured by explosions and subsequently trapped in the building before either Twin Tower collapsed? Foreknowledge 15. Why did Fire Chief Nick Visconti declare, “We’re moving the command post over this way, that building’s coming down!”? 16. How could Fire Chief Hayden’s engineer declare, upon being asked, “how long until the building comes down?” – then accurately state, “In its current state you have about 5 hours,” when no steel-frame fire-protected high-rise had ever come down due to fire alone? 17. Why did construction workers, while walking away from Building 7 and upon hearing an explosion from the building, look straight into the CNN camera saying, “You hear that? Keep your eye on that building. That thing’s coming down. The building is about to blow up, flame and debris coming down”? 18. Why did former Air Force medic Kevin McPadden hear a “3-2-1” countdown on the radio, and subsequently hear explosions before Building 7 collapsed? 19. How could the BBC have announced, live on TV, the collapse of WTC 7 20 minutes before it collapsed? 20. Why did CNN announce, 7 hours early, the 10:45 AM collapse of a 50-story building (obviously referring to Building 7)? Expert Statements 21. Why have more than 3,600 Architects & Engineers signed onto the petition at demanding a new 9/11 WTC investigation? 22. Why are dozens of structural engineers making statements such as: “A localized failure in a steel-framed building like WTC 7 cannot cause a catastrophic collapse like a house of cards, without a simultaneous and patterned loss of several of its columns at key locations within the building”? 23. Why did the top European controlled demolition expert declare: “That is controlled demolition. It’s been imploded. It’s a hired job. A team of experts did this? 24. Why did top forensic structural engineer, Prof. Leroy Hulsey from the University of Alaska, following a 4-year study of WTC 7, declare: “The collapse of WTC 7 was a global failure involving the near-simultaneous failure of all columns in the building and not a progressive collapse, as claimed by NIST. Extreme Heat Molten Metal 25. What does it mean that FEMA, in its 2002 Report, including a metallurgical examination of the WTC 7 steel, revealed “a phenomenon never before observed in building fires….a liquid eutectic mixture containing primarily iron, oxygen, and sulfur formed during this hot corrosion attack on the steel...” Why did NIST eliminate this metallurgical report from their final report? 26. Did Fire Protection Engineer Jonathan Barnett know, when he said, “steel members in the debris pile that appear to have been partly evaporated,” that it takes 4,000°F to evaporate steel? And that jet fuel and office fires don’t even rise to a third of that temperature? 27. Why is there bright yellow molten steel or iron pouring out of the crab claw excavators in the WTC pit? And out of the South Tower just minutes before its collapse. 28. Why did the first responders in the pit report, “you get down in the pile, and you see molten steel – flowing down the channel rails, like lava from a volcano”? Did they know that it takes 3,000°F to melt steel, and that office fires and jet fuel can only achieve half of this temperature? 29. What can explain the well-documented 3,000°F temperatures that are well-documented in the WTC Twin Towers collapse aftermath? Why is there evidence of ignited thermite found by so many first responders in the WTC pile? Previously Molten Iron Microspheres 30. What does it mean that the US Geological Survey and RJ Lee Group independently documented billions of previously molten iron-rich microspheres in ALL of the WTC dust samples? Where would the required 3,000°F come from? Could the ignited thermite have created those molten iron microspheres? 31. Why is bright yellow molten steel or iron pouring out of the South Tower just minutes prior to its collapse? 32. What explains the 2009 peer-reviewed findings from the Niels Harrit research team of dual-layered red-gray chips of nano-thermite in all the independently-collected dust samples they analyzed? Why do they ignite at the same temperature as military grade “super-thermite”? Why do they produce molten iron-rich microspheres when ignited? 33. What does it mean that Harrit’s international research team found that the “red layer of the red/gray chips in all of their WTC dust samples is active unreacted thermitic material, incorporating nanotechnology, and is a highly energetic pyrotechnic or explosive material”? The Twin Towers Official Explanation 34. How can the official explanation of the Twin Towers’ collapse be true (that an intact top section drove down the rest of the building after weakening of some of the structural steel in the impact zone) when this top section had already been destroyed in the first 3 seconds of the collapse (telescoping in on itself) and so was not even available to drive anything down to the ground? NIST claims that the top part of the building drove the rest of the building down to the ground. Why then do none of the photos or videos show such a top part driving anything down? And why didn’t that top “pile driver” drive down the 800-foot-tall group of columns standing for 6 seconds after the overall collapse? 35. Why did Zdenek Bazant, in his calculations for his controversial paper submitted to the Journal of Engineering Mechanics on 9/13/01( only two days after 9/11) use twice the actual mass of the upper section of the North Tower above the impact floors and only one third of the actual column strength of the larger building section beneath it in his support for NIST collapse theory? a. Why is this paper still today the key theoretical basis of NIST’s column failure theory? 36. Why does the destruction of the towers look more like a volcanic eruption (than a straight-down gravitational collapse) with upward and outward arching streamers, a geometry of fireworks, freely flying solid molten objects trailing thick white smoke clouds? Witnesses of Explosions 37. Why are there 156 First Responder witnesses of explosions – seeing, hearing, and feeling explosions – many of them BEFORE the towers ever came down? 38. Why did NIST claim that there were “no witnesses of explosions” when there were as many as 200 publicly recorded testimonies – many before the collapse? What could explain Fire Chief Frank Cruthers’ testimony that, “… an explosion… appeared at the very top, simultaneously from all four sides, materials shot out horizontally. And then there seemed to be a momentary delay, before you could see the beginning of the collapse”? 39. Why did 36 reporters on the day of 9/11 report the WTC destruction as an explosion-based event, most of them actual witnesses of explosions? a. Why did the mainstream national media change the story the next day from explosion-based collapses to “fire-induced collapses”? 40. Why did the FBI, NYPD, and FDNY on the day of 9/11 all state that they suspected that explosives were used to bring down the towers, but change their story in the following week to fire-induced collapse? Seismic Evidence 41. Why did the Richter Scale recordings from Lamont Doherty Earth Observatory document significant seismic events for both towers, more than a dozen seconds before the planes hit either tower – corroborating the explosive testimony of William Rodriguez and others of massive explosions in the basement prior to the plane hitting the buildings? 42. Why did the seismic evidence from Lamont Doherty Earth Observatory document significant seismic events, in the North Tower, 5 seconds before the heaviest debris from each tower struck the ground? And in the South Tower, 7 seconds before any debris struck the ground? Wouldn’t this seismic evidence corroborate the testimony of the first responders that saw, heard, and/or felt explosions before the towers fell? 43. Why did at least 3 of the tripod-mounted cameras (two on the ground and one on the rooftop) “shake” 3 to 10 seconds before each of the towers fell? Would the camera evidence corroborate the seismic evidence and the first responder's explosive testimony? Explosive Evidence 44. Since the damage from the planes and fires was so asymmetrical, why was the destruction itself so precisely symmetrical – all the way down each face of each tower? Why do the videos show precise rows of individual explosions progressing down the towers – floor by floor? 45. Why do we see in the videos isolated pin-point explosive ejections occurring 20, 40, and even 60 stories down below the downward-traveling zone of destruction in each tower? Descent Profile and Speed 46. Why did the top sections of each tower descend suddenly, smoothly, down with no stoppage or “jolt” upon impact with the cold, hard, intact steel columns below the floors of the plane impacts? 47. How was it possible that the top section of each Tower descended without slowing at all, but instead accelerated, as if 80,000 tons of steel beneath wasn’t even there? What happened to the steel? Lateral Ejection of Steel 48. Why do we see in the videos the lateral ejection out of both of the Towers of hundreds of freely flying structural steel sections each weighing 4 to 8 tons, at 80mph, landing up to 600 feet in every direction, impaling all of the surrounding skyscrapers? Why are they trailing thick white smoke clouds when steel is not flammable in office fires, or under jet fuel conditions? Could this be due to the other byproduct of thermite – aluminum oxide ash? 49. Since FEMA officially documented a 1200-foot diameter zone of flying, fallen, and impaled structural steel beyond the footprints of both Towers, how could that steel, which comprised 1/3 of the weight of the falling section of each building, have still been available to crush the lower part as NIST claimed? Missing Floors 50. Since there were 110 concrete floors, each an acre in size, and since they were not stacked up in pile of “pancakes” at the bottom, and since a third of the WTC dust in the 3” thick blanket across Lower Manhattan from river to river is powdered concrete, then how could the concrete floors (also 1/3 of the weight of each Tower) be available to crush the building below? 51. What extreme-high temperature could have reduced 90,000 tons of concrete in each Tower back to its original aggregate, sand, and cement powder? Demolition Access 52. How could the perpetrators have gained access to the Towers to plant high energy explosives and incendiaries? Could a massive fireproofing upgrade project in the months and years prior to 9/11 have provided access to the underside of the floor systems to apply sprayed-on nano-thermite? Is it a coincidence that the WTC fireproofing upgrades occurred mostly on the floors that were hit by the planes on 9/11? Could the largest elevator modernization in the world in the 9 months prior to 9/11 have provided access to the core columns and beams? Is it just a coincidence that Ace Elevator employees were pulled out of the Towers on 9/11 for “union meeting”? Destruction of Evidence 53. Why was 99% of the WTC structural steel crime scene evidence loaded onto barges starting just 2 weeks after 9/11 and shipped to China for recycling before structural engineers and metallurgists could get their hands on it to do a proper forensic investigation? We encougage you to ask these questions of your elected representatives and the media. We address most of these questions in our presentations and podcast and radio interviews. So get is in front of them! Who do you know that might interview RichardGage911 about the explosive destruction of the 3 World Trade Center Skyscrapers on 9/11?

Richard Gage, AIA, Architect

44,190 次观看 • 1 年前

Made $530,000 with Ai Bot that started with $313. Didn't know how to code. Now this bots run 24/7 printing money while sleeping. I've made the exact step-by-step guide to build this Claude Code Polymarket trading bot. Prompts. Code. Risk settings. Paper trading checklist. Everything from zero to running bot. It's free. For 24 hours. After that I'm charging $499 for it. To grab it right now: 1. Comment "Claude Bot" 2. Like and Retweet this post 3. Follow me Himanshu Kumar ( I can't send DMs to non-followers ) I'm DMing everyone who Complete the 3 steps. I spent hundreds of thousands hiring developers because he was too scared to learn. Then learned Claude Code. Built algorithmic trading systems. $313 → $530,000. You have the same tools available right now. And you're using them to ask ChatGPT for Instagram captions. This attached video is a goldmine. Full live walkthrough. Claude Code building actual Polymarket trading bots. From zero. Every line of code. Every decision explained. Now let me break down why everything you're doing in trading is wrong and exactly how to fix it. Save this post. You'll hate yourself if you lose it. ↓ Let's start with why you keep losing money. You already know the answer. You just won't admit it. You overtrade. Every. Single. Day. You see a candle move. You feel something. You enter. No plan. No edge. No reason. Just feelings. Then it goes against you. You feel something else. Panic. Anger. Denial. You move your stop loss. Or you didn't set one at all. "It'll come back." It doesn't come back. So you take another trade. A revenge trade. Bigger size this time. Because you need to "make it back." That one fails too. Now you're emotional. Now you're tilted. Now you're using leverage you have no business touching. 40x. 50x. 100x. On a trade you entered because a candle looked "bullish" and some guy on Twitter said "send it." You get liquidated. Close the laptop. Punch something. Tell yourself you'll be "more disciplined" tomorrow. Tomorrow comes. Same cycle. Same result. Same liquidation. You've been doing this for months. Maybe years. And you still think the problem is your strategy. The problem isn't your strategy. The problem is you. Save this post right now. What I'm about to show you is the only way to remove yourself from the equation. Follow Himanshu Kumar so you don't miss any of this. ↓ Here's what's actually killing your account. It's not the market. The market doesn't care about you. It's not your indicators. RSI works fine. MACD works fine. They all "work." It's not your timeframe. It's not your broker. It's not the "manipulation." It's four things: 1. Emotions. You hold losers because hope feels better than loss. You cut winners because fear feels stronger than greed. You size up when angry. You skip trades when scared. Your emotional state determines your position size. That's insane. And you know it's insane. But you keep doing it. 2. Overtrading. You take 15 trades a day. Maybe 5 of them had actual setups. The other 10 were boredom. Boredom trades are the most expensive hobby in human history. 3. Leverage. You use 20x-50x on trades where you're not even sure about the direction. That's not trading. That's a casino with a nicer interface. 4. Fees. You're smashing market orders. Paying spread. Paying commission. On 15 trades a day. Your broker makes more money from your account than you do. Think about that. Your broker is profitable on your account. You're not. You're the product. Not the trader. These four things are why 90% of traders lose. Not bad luck. Not the market. You. Save this post and follow Himanshu Kumar because the solution is coming next. ↓ The solution is painfully obvious. Remove yourself from the equation. Not partially. Not "I'll be more disciplined." Not "I'll journal my trades." Not "I'll meditate before trading." Completely remove yourself. Build a bot. Let the bot trade. You go live your life. The bot doesn't feel emotions. The bot doesn't overtrade. The bot doesn't use reckless leverage. The bot doesn't smash market orders and bleed fees. The bot follows the rules. Every single time. Without exception. Without "just this once." Without "I have a feeling about this one." Rules in. Execution out. No human in the middle to mess everything up. That's algorithmic trading. And before your ego jumps in with "but I'm different, I have discipline" — No you don't. Your account balance proves you don't. If you had discipline, your account would be green. It's not. So you don't. Accept it. Automate it. Move on. This is the hardest truth in trading. Your discipline will always fail. A bot's won't. Save this post. Follow Himanshu Kumar for the exact bot setup that removes your emotions permanently. ↓ "But I don't know how to code." Neither did he. The guy in this video didn't know how to code for most of his life. Got held back in 7th grade. People counted him out early. Spent years building apps and SaaS businesses without writing a single line of code. Hired developers on Upwork instead. Spent hundreds of thousands of dollars paying other people to build what he could have built himself. Because he was scared to learn. That fear cost him years. And hundreds of thousands of dollars. Sound familiar? You're doing the same thing right now. Not with developers. But with your time. You're spending thousands of hours trading manually because you're scared to learn the thing that would make trading automatic. The fear of learning to code is costing you more than any bad trade ever did. Because every month you trade manually is a month of emotional decisions, overleveraged entries, and unnecessary losses that a bot would never make. And here's the thing that should really frustrate you: AI does the hard parts now. You don't need a computer science degree. You don't need to work at a hedge fund. You don't need to be "good at math." Claude Code writes the code for you. You just need to think clearly about trading ideas. That's it. If you can describe a strategy in English, Claude can build it in Python. "I don't know how to code" stopped being a valid excuse in 2024. It's 2026. You're 2 years late on that excuse. Find a new one. Or stop making excuses entirely. Save this post. Follow Himanshu Kumar because I'm showing you how people with zero coding experience are building profitable bots. ↓ The process that actually makes money. Three letters. R. B. I. Research. Backtest. Implement. That's it. That's the entire process. Every single day. Research: Find an idea. A pattern. A market inefficiency. Don't trade it yet. Don't even think about trading it yet. Just research it. Backtest: Test the idea against historical data. Does it work? Not "does it look good on one chart." Does it work across thousands of trades? Across different market conditions? Across in-sample AND out-of-sample data? If no, kill it. Find another idea. If yes, move to step 3. Implement: Build the bot. Deploy it. Paper trade first. Then live with small size. Scale only on evidence. Research. Backtest. Implement. Every day. No exceptions. You know what your current process is? Feel. Enter. Pray. F. E. P. Feel bullish. Enter a trade. Pray it works. That's not a process. That's gambling with a TradingView subscription. RBI is the only process that works. Save this post. Tattoo it on your forearm. Follow Himanshu Kumar for daily RBI breakdowns. ↓ What Claude Code actually does that your manual process can't. You can maybe test 3-5 strategy ideas per week. Manually adjusting parameters. Manually checking results. Manually writing code (badly). Claude Code tests 50-100 ideas per week. With parallel agents running simultaneously. Multiple strategies being built, tested, and validated at the same time. While you sleep. The guy in this video spends 4-8 hours a day building systems with Claude Code. Not trading. Building. Research. Backtest. Implement. Then iterate. Improve. Optimize. Every day the systems get better. Every day the edge compounds. Every day the bots get smarter. While you? You spend 4-8 hours a day staring at charts making the same mistakes you made last month. Same indicators. Same patterns. Same entries. Same losses. He's iterating forward. You're running in circles. Same 8 hours per day. Completely different outcomes. Because he's building systems. And you're feeding a casino. Stop feeding the casino. Start building the machine. Save this post and follow Himanshu Kumar for the Claude Code workflow that iterates strategies while you sleep. ↓ Jim Simons. That's the benchmark. You probably don't know who Jim Simons is. And that tells me everything about how seriously you take trading. Jim Simons. Mathematician. Founded Renaissance Technologies. Built a net worth of $31 billion. 100% from algorithmic trading. Not one single manual trade. Not one "gut feeling" entry. Not one RSI divergence. Not one "smart money concept." Algorithms. Bots. Systems. Data. $31 billion. His fund averaged 66% annual returns for over 30 years. While you're excited about making $200 on a trade that you'll give back tomorrow. The best trader in human history never placed a manual trade in his life. And you think your edge is staring at a 5-minute chart with bloodshot eyes at 2 AM? Your edge is building the system. Not being inside it. Jim Simons is the benchmark. Everything else is noise. Save this post. Follow Himanshu Kumar because I'm building toward the same goal and showing every step publicly. ↓ What you need to understand about patience. This is not get-rich-overnight. The guy in this video says it directly: "This channel is not for people looking to get rich overnight. It's not plug and play. There are no shortcuts. If you're impatient, this probably isn't for you." And that's exactly why most people will fail at this. Because you want results now. Today. This trade. You don't want to spend a week building a bot. You don't want to paper trade for 2 weeks. You don't want to test 50 ideas to find 1 that works. You want to copy someone's bot, run it live with your rent money, and be rich by Friday. That's why you'll be broke by Friday. The guy making $2.3M spent months iterating. Testing. Failing. Rebuilding. Testing again. He was patient when you would have quit. He was calm when you would have panicked. He was consistent when you would have given up. Patience isn't just a virtue in trading. It's the only virtue. Without it, everything else fails. Impatience is the most expensive personality trait in trading. Save this post. Follow Himanshu Kumar and learn to build systems with the patience that actually pays. ↓ The live streams where the real learning happens. The YouTube video is the trailer. The live streams are the movie. Real-time bot building. Real-time questions answered. Real code shown. Real mistakes made and fixed. Not polished highlight reels where everything works perfectly. Actual development. Where things break. Where strategies fail. Where code doesn't compile. Where the fix takes 2 hours. Because that's what real development looks like. And seeing the messy parts is more valuable than any polished tutorial. Because when your bot breaks at 3 AM, you need to know how to fix it. Not just how to celebrate when it works. The streams mix beginner and advanced. Start with how to automate trading. How to use AI for code generation. Then dive into the daily work. Claude Code. Parallel agents. Constant iteration. Live debugging. 4-8 hours of real algorithmic trading development. Live. Uncut. No filter. Most "trading education" shows you the wins. This shows you the work. Save this post. Follow Himanshu Kumar for the stream schedules and breakdowns. ↓ The belief that changes everything. Code is the greatest equalizer. Not money. Not connections. Not a degree. Not where you grew up. Not what school you went to. Code. Once you can build systems, you can build anything. For the rest of your life. A trading bot today. A SaaS product tomorrow. An automation business next month. A completely different life next year. The skill isn't "algorithmic trading." The skill is building systems. And that skill transfers to everything. The guy who can build a trading bot can also build a lead gen tool. Can also build a content pipeline. Can also build a SaaS product. Can also build literally anything that runs on logic and code. One skill. Infinite applications. And AI makes learning it 100x easier than it was 5 years ago. You don't need to be smart. You don't need talent. You need Claude Code and the willingness to sit down and build something instead of consuming content about building something. Building is the skill. Everything else is entertainment disguised as education. Save this post. Follow Himanshu Kumar because I'm showing you how to build, not just how to watch. ↓ If any of this applies to you, pay attention. If you've lost money from overtrading. If you've been liquidated. If you know trading is the vehicle but manual execution keeps crashing you. If you've tried "being more disciplined" and it never lasted more than a week. If you keep saying "next month I'll start automating." If you've spent more money on courses than you've made from trading. There is a better way. It's not a magic indicator. It's not a signal group. It's not a $997 mentorship from a guy who makes money teaching, not trading. It's building your own system. A system that trades without emotion. A system that follows rules without exception. A system that runs while you sleep. A system that compounds while you live your life. That's the answer. It's always been the answer. You've just been too scared to accept that the solution requires building something instead of buying something. ↓ What the next 30 days look like if you actually commit. Week 1: Watch the video. Learn Claude Code basics. Build your first simple strategy. Run your first backtest. Week 2: Iterate. Let Claude improve the strategy. Run Monte Carlo validation. Paper trade. Week 3: Go live with $50-100. Tiny positions. Watch every trade. Compare to paper results. Week 4: Scale based on evidence. Not based on excitement. Not based on one good day. Based on data. 30 days from now you either have a running bot that trades without your emotions destroying every position. Or you're exactly where you are right now. Reading another post. Making another promise. Breaking it by Tuesday. Same 30 days either way. Different actions. Different results. Different life. ↓ Full video tutorial attached. Live bot building with Claude Code. From zero to running Polymarket trading bot. Every line of code. Every decision explained. The video is free. Claude Code is available now. The market is open 24/7. The only thing standing between you and a profitable trading bot is the same thing that's been standing there for months. You. Get out of your own way. Follow Himanshu Kumar for daily AI trading bot breakdowns, live build sessions, and the full RBI process. Save this post. Watch the video. Build the bot. Or keep trading manually and keep losing. The choice has never been easier. And you've never been more stubborn about making the wrong one.

Made $530,000 with Ai Bot that started with $313. Didn't know how to code. Now this bots run 24/7 printing money while sleeping. I've made the exact step-by-step guide to build this Claude Code Polymarket trading bot. Prompts. Code. Risk settings. Paper trading checklist. Everything from zero to running bot. It's free. For 24 hours. After that I'm charging $499 for it. To grab it right now: 1. Comment "Claude Bot" 2. Like and Retweet this post 3. Follow me Himanshu Kumar ( I can't send DMs to non-followers ) I'm DMing everyone who Complete the 3 steps. I spent hundreds of thousands hiring developers because he was too scared to learn. Then learned Claude Code. Built algorithmic trading systems. $313 → $530,000. You have the same tools available right now. And you're using them to ask ChatGPT for Instagram captions. This attached video is a goldmine. Full live walkthrough. Claude Code building actual Polymarket trading bots. From zero. Every line of code. Every decision explained. Now let me break down why everything you're doing in trading is wrong and exactly how to fix it. Save this post. You'll hate yourself if you lose it. ↓ Let's start with why you keep losing money. You already know the answer. You just won't admit it. You overtrade. Every. Single. Day. You see a candle move. You feel something. You enter. No plan. No edge. No reason. Just feelings. Then it goes against you. You feel something else. Panic. Anger. Denial. You move your stop loss. Or you didn't set one at all. "It'll come back." It doesn't come back. So you take another trade. A revenge trade. Bigger size this time. Because you need to "make it back." That one fails too. Now you're emotional. Now you're tilted. Now you're using leverage you have no business touching. 40x. 50x. 100x. On a trade you entered because a candle looked "bullish" and some guy on Twitter said "send it." You get liquidated. Close the laptop. Punch something. Tell yourself you'll be "more disciplined" tomorrow. Tomorrow comes. Same cycle. Same result. Same liquidation. You've been doing this for months. Maybe years. And you still think the problem is your strategy. The problem isn't your strategy. The problem is you. Save this post right now. What I'm about to show you is the only way to remove yourself from the equation. Follow Himanshu Kumar so you don't miss any of this. ↓ Here's what's actually killing your account. It's not the market. The market doesn't care about you. It's not your indicators. RSI works fine. MACD works fine. They all "work." It's not your timeframe. It's not your broker. It's not the "manipulation." It's four things: 1. Emotions. You hold losers because hope feels better than loss. You cut winners because fear feels stronger than greed. You size up when angry. You skip trades when scared. Your emotional state determines your position size. That's insane. And you know it's insane. But you keep doing it. 2. Overtrading. You take 15 trades a day. Maybe 5 of them had actual setups. The other 10 were boredom. Boredom trades are the most expensive hobby in human history. 3. Leverage. You use 20x-50x on trades where you're not even sure about the direction. That's not trading. That's a casino with a nicer interface. 4. Fees. You're smashing market orders. Paying spread. Paying commission. On 15 trades a day. Your broker makes more money from your account than you do. Think about that. Your broker is profitable on your account. You're not. You're the product. Not the trader. These four things are why 90% of traders lose. Not bad luck. Not the market. You. Save this post and follow Himanshu Kumar because the solution is coming next. ↓ The solution is painfully obvious. Remove yourself from the equation. Not partially. Not "I'll be more disciplined." Not "I'll journal my trades." Not "I'll meditate before trading." Completely remove yourself. Build a bot. Let the bot trade. You go live your life. The bot doesn't feel emotions. The bot doesn't overtrade. The bot doesn't use reckless leverage. The bot doesn't smash market orders and bleed fees. The bot follows the rules. Every single time. Without exception. Without "just this once." Without "I have a feeling about this one." Rules in. Execution out. No human in the middle to mess everything up. That's algorithmic trading. And before your ego jumps in with "but I'm different, I have discipline" — No you don't. Your account balance proves you don't. If you had discipline, your account would be green. It's not. So you don't. Accept it. Automate it. Move on. This is the hardest truth in trading. Your discipline will always fail. A bot's won't. Save this post. Follow Himanshu Kumar for the exact bot setup that removes your emotions permanently. ↓ "But I don't know how to code." Neither did he. The guy in this video didn't know how to code for most of his life. Got held back in 7th grade. People counted him out early. Spent years building apps and SaaS businesses without writing a single line of code. Hired developers on Upwork instead. Spent hundreds of thousands of dollars paying other people to build what he could have built himself. Because he was scared to learn. That fear cost him years. And hundreds of thousands of dollars. Sound familiar? You're doing the same thing right now. Not with developers. But with your time. You're spending thousands of hours trading manually because you're scared to learn the thing that would make trading automatic. The fear of learning to code is costing you more than any bad trade ever did. Because every month you trade manually is a month of emotional decisions, overleveraged entries, and unnecessary losses that a bot would never make. And here's the thing that should really frustrate you: AI does the hard parts now. You don't need a computer science degree. You don't need to work at a hedge fund. You don't need to be "good at math." Claude Code writes the code for you. You just need to think clearly about trading ideas. That's it. If you can describe a strategy in English, Claude can build it in Python. "I don't know how to code" stopped being a valid excuse in 2024. It's 2026. You're 2 years late on that excuse. Find a new one. Or stop making excuses entirely. Save this post. Follow Himanshu Kumar because I'm showing you how people with zero coding experience are building profitable bots. ↓ The process that actually makes money. Three letters. R. B. I. Research. Backtest. Implement. That's it. That's the entire process. Every single day. Research: Find an idea. A pattern. A market inefficiency. Don't trade it yet. Don't even think about trading it yet. Just research it. Backtest: Test the idea against historical data. Does it work? Not "does it look good on one chart." Does it work across thousands of trades? Across different market conditions? Across in-sample AND out-of-sample data? If no, kill it. Find another idea. If yes, move to step 3. Implement: Build the bot. Deploy it. Paper trade first. Then live with small size. Scale only on evidence. Research. Backtest. Implement. Every day. No exceptions. You know what your current process is? Feel. Enter. Pray. F. E. P. Feel bullish. Enter a trade. Pray it works. That's not a process. That's gambling with a TradingView subscription. RBI is the only process that works. Save this post. Tattoo it on your forearm. Follow Himanshu Kumar for daily RBI breakdowns. ↓ What Claude Code actually does that your manual process can't. You can maybe test 3-5 strategy ideas per week. Manually adjusting parameters. Manually checking results. Manually writing code (badly). Claude Code tests 50-100 ideas per week. With parallel agents running simultaneously. Multiple strategies being built, tested, and validated at the same time. While you sleep. The guy in this video spends 4-8 hours a day building systems with Claude Code. Not trading. Building. Research. Backtest. Implement. Then iterate. Improve. Optimize. Every day the systems get better. Every day the edge compounds. Every day the bots get smarter. While you? You spend 4-8 hours a day staring at charts making the same mistakes you made last month. Same indicators. Same patterns. Same entries. Same losses. He's iterating forward. You're running in circles. Same 8 hours per day. Completely different outcomes. Because he's building systems. And you're feeding a casino. Stop feeding the casino. Start building the machine. Save this post and follow Himanshu Kumar for the Claude Code workflow that iterates strategies while you sleep. ↓ Jim Simons. That's the benchmark. You probably don't know who Jim Simons is. And that tells me everything about how seriously you take trading. Jim Simons. Mathematician. Founded Renaissance Technologies. Built a net worth of $31 billion. 100% from algorithmic trading. Not one single manual trade. Not one "gut feeling" entry. Not one RSI divergence. Not one "smart money concept." Algorithms. Bots. Systems. Data. $31 billion. His fund averaged 66% annual returns for over 30 years. While you're excited about making $200 on a trade that you'll give back tomorrow. The best trader in human history never placed a manual trade in his life. And you think your edge is staring at a 5-minute chart with bloodshot eyes at 2 AM? Your edge is building the system. Not being inside it. Jim Simons is the benchmark. Everything else is noise. Save this post. Follow Himanshu Kumar because I'm building toward the same goal and showing every step publicly. ↓ What you need to understand about patience. This is not get-rich-overnight. The guy in this video says it directly: "This channel is not for people looking to get rich overnight. It's not plug and play. There are no shortcuts. If you're impatient, this probably isn't for you." And that's exactly why most people will fail at this. Because you want results now. Today. This trade. You don't want to spend a week building a bot. You don't want to paper trade for 2 weeks. You don't want to test 50 ideas to find 1 that works. You want to copy someone's bot, run it live with your rent money, and be rich by Friday. That's why you'll be broke by Friday. The guy making $2.3M spent months iterating. Testing. Failing. Rebuilding. Testing again. He was patient when you would have quit. He was calm when you would have panicked. He was consistent when you would have given up. Patience isn't just a virtue in trading. It's the only virtue. Without it, everything else fails. Impatience is the most expensive personality trait in trading. Save this post. Follow Himanshu Kumar and learn to build systems with the patience that actually pays. ↓ The live streams where the real learning happens. The YouTube video is the trailer. The live streams are the movie. Real-time bot building. Real-time questions answered. Real code shown. Real mistakes made and fixed. Not polished highlight reels where everything works perfectly. Actual development. Where things break. Where strategies fail. Where code doesn't compile. Where the fix takes 2 hours. Because that's what real development looks like. And seeing the messy parts is more valuable than any polished tutorial. Because when your bot breaks at 3 AM, you need to know how to fix it. Not just how to celebrate when it works. The streams mix beginner and advanced. Start with how to automate trading. How to use AI for code generation. Then dive into the daily work. Claude Code. Parallel agents. Constant iteration. Live debugging. 4-8 hours of real algorithmic trading development. Live. Uncut. No filter. Most "trading education" shows you the wins. This shows you the work. Save this post. Follow Himanshu Kumar for the stream schedules and breakdowns. ↓ The belief that changes everything. Code is the greatest equalizer. Not money. Not connections. Not a degree. Not where you grew up. Not what school you went to. Code. Once you can build systems, you can build anything. For the rest of your life. A trading bot today. A SaaS product tomorrow. An automation business next month. A completely different life next year. The skill isn't "algorithmic trading." The skill is building systems. And that skill transfers to everything. The guy who can build a trading bot can also build a lead gen tool. Can also build a content pipeline. Can also build a SaaS product. Can also build literally anything that runs on logic and code. One skill. Infinite applications. And AI makes learning it 100x easier than it was 5 years ago. You don't need to be smart. You don't need talent. You need Claude Code and the willingness to sit down and build something instead of consuming content about building something. Building is the skill. Everything else is entertainment disguised as education. Save this post. Follow Himanshu Kumar because I'm showing you how to build, not just how to watch. ↓ If any of this applies to you, pay attention. If you've lost money from overtrading. If you've been liquidated. If you know trading is the vehicle but manual execution keeps crashing you. If you've tried "being more disciplined" and it never lasted more than a week. If you keep saying "next month I'll start automating." If you've spent more money on courses than you've made from trading. There is a better way. It's not a magic indicator. It's not a signal group. It's not a $997 mentorship from a guy who makes money teaching, not trading. It's building your own system. A system that trades without emotion. A system that follows rules without exception. A system that runs while you sleep. A system that compounds while you live your life. That's the answer. It's always been the answer. You've just been too scared to accept that the solution requires building something instead of buying something. ↓ What the next 30 days look like if you actually commit. Week 1: Watch the video. Learn Claude Code basics. Build your first simple strategy. Run your first backtest. Week 2: Iterate. Let Claude improve the strategy. Run Monte Carlo validation. Paper trade. Week 3: Go live with $50-100. Tiny positions. Watch every trade. Compare to paper results. Week 4: Scale based on evidence. Not based on excitement. Not based on one good day. Based on data. 30 days from now you either have a running bot that trades without your emotions destroying every position. Or you're exactly where you are right now. Reading another post. Making another promise. Breaking it by Tuesday. Same 30 days either way. Different actions. Different results. Different life. ↓ Full video tutorial attached. Live bot building with Claude Code. From zero to running Polymarket trading bot. Every line of code. Every decision explained. The video is free. Claude Code is available now. The market is open 24/7. The only thing standing between you and a profitable trading bot is the same thing that's been standing there for months. You. Get out of your own way. Follow Himanshu Kumar for daily AI trading bot breakdowns, live build sessions, and the full RBI process. Save this post. Watch the video. Build the bot. Or keep trading manually and keep losing. The choice has never been easier. And you've never been more stubborn about making the wrong one.

Himanshu Kumar

37,367 次观看 • 3 个月前

CANCEL Your Weekend Plans, and Learn Claude Code Today. $5,000/month. $10,000/month. $20,000/month. People are building entire apps and charging clients thousands using Claude Code. You're still Googling 'how to center a div.' While you're binge-watching a show you won't remember next week, a 19 year old with zero coding experience just built a $5,000 SaaS product in one afternoon using the tool I'm about to break down. Same laptop. Same internet. Same 24 hours. He has Claude Code. You have Netflix. That's the only difference. This YouTube video is a goldmine. Full Claude Code tutorial. Beginner to pro. Every feature. Every setup step. Every best practice. Zero prior knowledge needed. Save it. Watch it tonight. Not tomorrow. Tonight. Save this post. This is your complete Claude Code roadmap. Lose it and you lose the next 12 months of income. Follow Himanshu Kumar so you don't miss the breakdowns for each feature. ↓ 1. Understand What Claude Code Actually Is. You think Claude Code is just another chatbot. It's not. And that misunderstanding is why you're broke. ChatGPT gives you text. Claude Code gives you software. It runs in your terminal. It reads your entire codebase. It writes files directly to your project. It runs commands on your machine. It debugs errors autonomously. It builds features end to end. You're not chatting. You're deploying a developer. One that works 24/7. Never asks for a raise. Never calls in sick. Never pushes broken code at 5 PM on a Friday. People are charging clients $5,000-$10,000 for apps they built with Claude Code in 3 hours. And you didn't even know this tool existed because you're still asking ChatGPT to write you a to-do list. The gap between you and people making money with AI isn't intelligence. It's awareness. Now you're aware. Save this post. Follow Himanshu Kumar for the complete breakdown of every Claude Code feature. ↓ 2. Set Up Claude Code Properly. Most people quit here. "It's too complicated." "I don't know terminal." "I'll set it up later." Later never comes. And "complicated" means "I watched for 30 seconds and gave up." The setup takes 10 minutes. Install Node.js. Install Claude Code via npm. Authenticate your account. Open your terminal. Done. 10 minutes. You spent longer this morning deciding what to have for breakfast. The video walks through every single click. Every command. Every screen. Assuming you know absolutely nothing. If you can download an app on your phone, you can set up Claude Code. It's the same level of difficulty. But you'll still tell yourself it's "too technical" because that excuse is more comfortable than admitting you're just scared to try something new. This is the setup that everything else builds on. Skip it and nothing works. ↓ 3. Use the Desktop App. You don't even need to live in the terminal if you don't want to. Claude Code has a desktop app. Clean interface. Visual feedback. Everything you need without touching command line. But here's the thing most people don't know: The desktop app isn't just a pretty wrapper. It lets you manage projects visually. See file changes in real time. Switch between projects instantly. The people making money with Claude Code use the desktop app for client projects because it's faster to manage multiple builds simultaneously. You're still opening 14 browser tabs to organize one project. They open one app and everything's there. Efficiency isn't a personality trait. It's a tool choice. Save this post. Follow Himanshu Kumar for the desktop app workflow that handles 5 client projects at once. ↓ 4. Install the Right Dependencies. This is where beginners silently fail and blame the tool. Claude Code needs certain dependencies installed to work properly. Miss one and everything breaks. Then you go on Twitter and say "Claude Code doesn't work." It works fine. You just didn't read the setup guide. The video covers every dependency you need. What to install. How to install it. How to verify it's working. No guessing. No Stack Overflow rabbit holes at midnight. No "why isn't this working" for 3 hours. Watch the dependency section once. Follow every step. Never deal with setup issues again. You spent more time last week troubleshooting a printer than this takes. ↓ 5. Work Inside Your Code Editor. Claude Code integrates directly with your code editor. VS Code. Cursor. Whatever you use. It's not a separate window you alt-tab between. It's right there. In your workflow. You type a request. Claude writes the code. The code appears in your editor. You review it. Accept it. Done. No copy pasting between windows. No reformatting code that got mangled in transit. No "which version was the right one." It's like pair programming with someone who never gets distracted, never argues about naming conventions, and actually writes code that works on the first try. Your current coding process is: Google the problem, read 5 answers on Stack Overflow, copy the wrong one, debug for an hour, find the right one, paste it in, break something else, repeat. Claude Code's process is: describe what you want, get working code, move on with your life. Same hour. One method produces working software. The other produces frustration and a browser history full of Stack Overflow tabs. Stop coding the hard way. Save this post. Follow Himanshu Kumar for code editor setup guides and integration tips. ↓ 6. Master Basic Usage. Most people learn 5% of a tool and say they "know" it. You "know" Photoshop because you can crop an image. You "know" Excel because you can sum a column. You "know" Claude Code because you asked it one question. Basic usage means: How to give Claude Code context about your project. How to ask for changes to existing code. How to generate new files and features. How to review what Claude produces. How to iterate when the output isn't perfect. These basics are the foundation of everything. Skip them and every advanced feature feels confusing. Master them and every advanced feature feels obvious. The video breaks down each one with real examples. Not theory. Actual usage on actual projects. You've been using AI tools at 5% capacity and wondering why your results are 5% of what others get. Save this post. Follow Himanshu Kumar for daily Claude Code usage tips. ↓ 7. Learn Every Command. Claude Code has commands that most users never discover. Because most users type one message and expect magic. That's not how professionals use it. Professionals use specific commands that tell Claude Code exactly what to do, how to do it, and what constraints to follow. The difference between a beginner and someone making $10K/month with Claude Code is knowing which command to use and when. The video walks through every single one. Not just what they do. But when to use each one. And why one command is better than another for specific situations. You've been using Claude Code like a hammer. These commands turn it into a full toolbox. Stop treating a power tool like a blunt instrument. Save this post. Follow Himanshu Kumar for the command cheat sheet I use daily. ↓ 8. Understand Modes and Shortcuts. Speed matters. The person who builds an app in 2 hours charges $5,000. The person who builds the same app in 2 days charges $2,000. Same app. Same quality. Different speed. Different income. Claude Code has modes that change how it operates. And shortcuts that cut your workflow time in half. Most people don't know either exists. They use Claude Code in default mode for everything. Like driving a car in first gear on the highway. Technically it works. But everyone is passing you. The video shows you every mode. Every shortcut. Every time-saving trick that separates the people charging $2,000 per project from the people charging $10,000. Speed is money. Literally. Save this post. Follow Himanshu Kumar for the shortcuts that cut my build time by 60%. ↓ 9. Write a Proper Planning Prompt. This is the section that separates amateurs from professionals. And it's the section most people skip. A planning prompt tells Claude Code what you're building before you start building it. Architecture. File structure. Technologies. Features. Constraints. Edge cases. Without a planning prompt, Claude Code guesses. And guessing produces garbage. With a planning prompt, Claude Code executes a clear plan. And clear plans produce working software. The video shows you exactly how to write a planning prompt that makes Claude Code produce professional-grade output on the first try. "But I just want to start coding." That's why your code breaks every time. That's why you restart projects 4 times. That's why nothing you build ever gets finished. Because you refuse to plan. A 5-minute planning prompt saves you 5 hours of debugging. But you'd rather skip the 5 minutes and suffer through the 5 hours because patience isn't your thing. And that's exactly why you're not making money. Planning is the most underpaid skill in coding. And the most overpaid when you master it. Save this post. Follow Himanshu Kumar for the planning prompt templates I use for every client project. ↓ 10. Choose the Right Model. Claude Code lets you select different AI models. Not all models are the same. Not all tasks need the same model. Using the most powerful model for a simple task wastes credits. Using a basic model for a complex task wastes time. The video explains: Which model to use for quick fixes. Which model to use for complex architecture. Which model to use for debugging. Which model to use for code generation. Most people pick one model and use it for everything. That's like using a sledgehammer to hang a picture frame. Model selection is strategy. And strategy is money. The people making $10K/month with Claude Code are strategic about every credit they spend. You're burning through credits because you use the most expensive model to write a hello world. ↓ 11. Use Git and Version Control. If you're not using version control, you're one mistake away from losing everything. Claude Code integrates with Git. Every change tracked. Every version saved. Every mistake reversible. Without Git: Claude makes a change. It breaks something. You can't undo it. You start over. 3 hours wasted. With Git: Claude makes a change. It breaks something. You roll back in 5 seconds. Keep working. Version control isn't optional. It's insurance. And the people not using it are the same people who say "I lost my entire project" like it's something that just happens. It doesn't just happen. It happens because you didn't set up Git. The video walks through the entire Git integration. Save this post. Follow Himanshu Kumar for the Git workflow that's saved every project I've ever built. ↓ 12. Set Up Claude.MD and Memory. This is the feature that makes Claude Code feel like a real team member instead of a stranger you explain everything to every time. ClaudeMD is a memory file. You tell Claude Code about your project once. It remembers forever. Coding style preferences. Project architecture decisions. Technology stack. File naming conventions. Business logic rules. Without ClaudeMD: Every new conversation starts from zero. You explain the same things repeatedly. Output is inconsistent. With ClaudeMD: Claude knows your project. Claude follows your rules. Claude produces consistent, professional code. The difference between a sloppy freelancer and a reliable agency is consistency. Claude. MD gives you consistency without the agency overhead. Most people don't set this up and wonder why Claude Code gives different answers every time. ↓ 13. Automate with Tasks. This is where Claude Code stops being a tool and starts being an employee. Tasks let you define repeating workflows. "Every time I push code, run tests." "Every time I create a new file, add boilerplate." "Every time I start a session, check for errors." Automated. Hands-free. Consistent. You're doing these things manually every single day. The same checks. The same steps. The same routine. Tasks do them automatically. So you can focus on the work that actually makes money. Every manual task you automate is time you get back. And time is the only thing you can never make more of. Save this post. Follow Himanshu Kumar for the task automation templates that run my entire workflow. ↓ 14. Explore Features Most People Never Touch. The video covers features that 95% of Claude Code users don't know exist. Because they watched a 3-minute TikTok about Claude Code and think they're experts now. They're not. They're using 5% of a tool that can do everything. The full tutorial goes deep into features that most tutorials skip because they're "too advanced." They're not too advanced. They're too valuable for lazy creators to bother explaining. This video explains all of them. Clearly. For beginners. The 5% of features you don't know about are the 5% that make people rich. ↓ Let's zoom out. I just broke down 14 sections of Claude Code. Setup and installation. Desktop app. Dependencies. Code editor integration. Basic usage. Commands. Modes and shortcuts. Planning prompts. Model selection. Git and version control. Memory and Claude. MD. Tasks and automation. Advanced features. All in one video. All free. All beginner friendly. The person who masters even half of these in the next 2 weeks will be in the top 1% of Claude Code users. The top 1% of Claude Code users are the ones charging $5,000-$10,000 per project and building them in a single afternoon. Everyone else is asking ChatGPT to fix their resume. Same tools. Same access. Completely different outcomes. Because one person treats AI like a toy. And the other treats it like a business. ↓ Here's the hard truth nobody wants to hear. You don't have a talent problem. You don't have an intelligence problem. You don't have a resources problem. You have an action problem. Everything I just listed has a free tutorial right here in the attached video. 33 minutes. That's it. 33 minutes to learn the tool that people are using to build $5,000-$20,000/month businesses. You spent more time today scrolling Twitter than it takes to watch this video. You spent more time this week watching Netflix than it takes to master Claude Code basics. You spent more time this month doing nothing than it would take to completely change your income. The information is free. The tool is accessible. The opportunity is here. The only thing missing is you caring enough to start. ↓ CANCEL your plans this week. This isn't optional anymore. The people learning Claude Code right now will be building apps for the people who didn't learn it. That's not a prediction. That's already happening. Companies are replacing $150/hour developers with one person and Claude Code. If you code: learn Claude Code or become half as valuable by next year. If you don't code: learn Claude Code or miss the biggest opportunity to start earning from tech without a CS degree. There's no path forward that doesn't include AI coding tools. None. You have one window. Right now. This week. ↓ Here's your action plan for the next 7 days: Day 1: Watch the full video. Install Claude Code. Set up dependencies. Day 2: Learn basic usage. Try 5 different commands. Day 3: Write your first planning prompt. Build a small project. Day 4: Set up Claude. MD. Configure your memory file. Day 5: Master modes and shortcuts. Build a second project faster. Day 6: Set up Git integration. Automate with tasks. Day 7: Build something real. A tool, an app, a website. Ship it. 7 days. One tool. One completely different skill set. One completely different income potential. Or 7 more days of scrolling Twitter watching other people build things while you "plan to start." Your call. ↓ This is the most important video you'll watch this year. 33 minutes. Complete Claude Code mastery. From zero to building real projects. Save this post. Come back to it every single day this week. Check off each section as you complete it. Follow Himanshu Kumar for daily Claude Code breakdowns, advanced tutorials, and the exact workflows that are turning beginners into $10K/month builders. The only thing between you and $10K/month with Claude Code is this video and 7 days. Don't waste them. You Must Follow me Himanshu Kumar, so i can send you DM.

CANCEL Your Weekend Plans, and Learn Claude Code Today. $5,000/month. $10,000/month. $20,000/month. People are building entire apps and charging clients thousands using Claude Code. You're still Googling 'how to center a div.' While you're binge-watching a show you won't remember next week, a 19 year old with zero coding experience just built a $5,000 SaaS product in one afternoon using the tool I'm about to break down. Same laptop. Same internet. Same 24 hours. He has Claude Code. You have Netflix. That's the only difference. This YouTube video is a goldmine. Full Claude Code tutorial. Beginner to pro. Every feature. Every setup step. Every best practice. Zero prior knowledge needed. Save it. Watch it tonight. Not tomorrow. Tonight. Save this post. This is your complete Claude Code roadmap. Lose it and you lose the next 12 months of income. Follow Himanshu Kumar so you don't miss the breakdowns for each feature. ↓ 1. Understand What Claude Code Actually Is. You think Claude Code is just another chatbot. It's not. And that misunderstanding is why you're broke. ChatGPT gives you text. Claude Code gives you software. It runs in your terminal. It reads your entire codebase. It writes files directly to your project. It runs commands on your machine. It debugs errors autonomously. It builds features end to end. You're not chatting. You're deploying a developer. One that works 24/7. Never asks for a raise. Never calls in sick. Never pushes broken code at 5 PM on a Friday. People are charging clients $5,000-$10,000 for apps they built with Claude Code in 3 hours. And you didn't even know this tool existed because you're still asking ChatGPT to write you a to-do list. The gap between you and people making money with AI isn't intelligence. It's awareness. Now you're aware. Save this post. Follow Himanshu Kumar for the complete breakdown of every Claude Code feature. ↓ 2. Set Up Claude Code Properly. Most people quit here. "It's too complicated." "I don't know terminal." "I'll set it up later." Later never comes. And "complicated" means "I watched for 30 seconds and gave up." The setup takes 10 minutes. Install Node.js. Install Claude Code via npm. Authenticate your account. Open your terminal. Done. 10 minutes. You spent longer this morning deciding what to have for breakfast. The video walks through every single click. Every command. Every screen. Assuming you know absolutely nothing. If you can download an app on your phone, you can set up Claude Code. It's the same level of difficulty. But you'll still tell yourself it's "too technical" because that excuse is more comfortable than admitting you're just scared to try something new. This is the setup that everything else builds on. Skip it and nothing works. ↓ 3. Use the Desktop App. You don't even need to live in the terminal if you don't want to. Claude Code has a desktop app. Clean interface. Visual feedback. Everything you need without touching command line. But here's the thing most people don't know: The desktop app isn't just a pretty wrapper. It lets you manage projects visually. See file changes in real time. Switch between projects instantly. The people making money with Claude Code use the desktop app for client projects because it's faster to manage multiple builds simultaneously. You're still opening 14 browser tabs to organize one project. They open one app and everything's there. Efficiency isn't a personality trait. It's a tool choice. Save this post. Follow Himanshu Kumar for the desktop app workflow that handles 5 client projects at once. ↓ 4. Install the Right Dependencies. This is where beginners silently fail and blame the tool. Claude Code needs certain dependencies installed to work properly. Miss one and everything breaks. Then you go on Twitter and say "Claude Code doesn't work." It works fine. You just didn't read the setup guide. The video covers every dependency you need. What to install. How to install it. How to verify it's working. No guessing. No Stack Overflow rabbit holes at midnight. No "why isn't this working" for 3 hours. Watch the dependency section once. Follow every step. Never deal with setup issues again. You spent more time last week troubleshooting a printer than this takes. ↓ 5. Work Inside Your Code Editor. Claude Code integrates directly with your code editor. VS Code. Cursor. Whatever you use. It's not a separate window you alt-tab between. It's right there. In your workflow. You type a request. Claude writes the code. The code appears in your editor. You review it. Accept it. Done. No copy pasting between windows. No reformatting code that got mangled in transit. No "which version was the right one." It's like pair programming with someone who never gets distracted, never argues about naming conventions, and actually writes code that works on the first try. Your current coding process is: Google the problem, read 5 answers on Stack Overflow, copy the wrong one, debug for an hour, find the right one, paste it in, break something else, repeat. Claude Code's process is: describe what you want, get working code, move on with your life. Same hour. One method produces working software. The other produces frustration and a browser history full of Stack Overflow tabs. Stop coding the hard way. Save this post. Follow Himanshu Kumar for code editor setup guides and integration tips. ↓ 6. Master Basic Usage. Most people learn 5% of a tool and say they "know" it. You "know" Photoshop because you can crop an image. You "know" Excel because you can sum a column. You "know" Claude Code because you asked it one question. Basic usage means: How to give Claude Code context about your project. How to ask for changes to existing code. How to generate new files and features. How to review what Claude produces. How to iterate when the output isn't perfect. These basics are the foundation of everything. Skip them and every advanced feature feels confusing. Master them and every advanced feature feels obvious. The video breaks down each one with real examples. Not theory. Actual usage on actual projects. You've been using AI tools at 5% capacity and wondering why your results are 5% of what others get. Save this post. Follow Himanshu Kumar for daily Claude Code usage tips. ↓ 7. Learn Every Command. Claude Code has commands that most users never discover. Because most users type one message and expect magic. That's not how professionals use it. Professionals use specific commands that tell Claude Code exactly what to do, how to do it, and what constraints to follow. The difference between a beginner and someone making $10K/month with Claude Code is knowing which command to use and when. The video walks through every single one. Not just what they do. But when to use each one. And why one command is better than another for specific situations. You've been using Claude Code like a hammer. These commands turn it into a full toolbox. Stop treating a power tool like a blunt instrument. Save this post. Follow Himanshu Kumar for the command cheat sheet I use daily. ↓ 8. Understand Modes and Shortcuts. Speed matters. The person who builds an app in 2 hours charges $5,000. The person who builds the same app in 2 days charges $2,000. Same app. Same quality. Different speed. Different income. Claude Code has modes that change how it operates. And shortcuts that cut your workflow time in half. Most people don't know either exists. They use Claude Code in default mode for everything. Like driving a car in first gear on the highway. Technically it works. But everyone is passing you. The video shows you every mode. Every shortcut. Every time-saving trick that separates the people charging $2,000 per project from the people charging $10,000. Speed is money. Literally. Save this post. Follow Himanshu Kumar for the shortcuts that cut my build time by 60%. ↓ 9. Write a Proper Planning Prompt. This is the section that separates amateurs from professionals. And it's the section most people skip. A planning prompt tells Claude Code what you're building before you start building it. Architecture. File structure. Technologies. Features. Constraints. Edge cases. Without a planning prompt, Claude Code guesses. And guessing produces garbage. With a planning prompt, Claude Code executes a clear plan. And clear plans produce working software. The video shows you exactly how to write a planning prompt that makes Claude Code produce professional-grade output on the first try. "But I just want to start coding." That's why your code breaks every time. That's why you restart projects 4 times. That's why nothing you build ever gets finished. Because you refuse to plan. A 5-minute planning prompt saves you 5 hours of debugging. But you'd rather skip the 5 minutes and suffer through the 5 hours because patience isn't your thing. And that's exactly why you're not making money. Planning is the most underpaid skill in coding. And the most overpaid when you master it. Save this post. Follow Himanshu Kumar for the planning prompt templates I use for every client project. ↓ 10. Choose the Right Model. Claude Code lets you select different AI models. Not all models are the same. Not all tasks need the same model. Using the most powerful model for a simple task wastes credits. Using a basic model for a complex task wastes time. The video explains: Which model to use for quick fixes. Which model to use for complex architecture. Which model to use for debugging. Which model to use for code generation. Most people pick one model and use it for everything. That's like using a sledgehammer to hang a picture frame. Model selection is strategy. And strategy is money. The people making $10K/month with Claude Code are strategic about every credit they spend. You're burning through credits because you use the most expensive model to write a hello world. ↓ 11. Use Git and Version Control. If you're not using version control, you're one mistake away from losing everything. Claude Code integrates with Git. Every change tracked. Every version saved. Every mistake reversible. Without Git: Claude makes a change. It breaks something. You can't undo it. You start over. 3 hours wasted. With Git: Claude makes a change. It breaks something. You roll back in 5 seconds. Keep working. Version control isn't optional. It's insurance. And the people not using it are the same people who say "I lost my entire project" like it's something that just happens. It doesn't just happen. It happens because you didn't set up Git. The video walks through the entire Git integration. Save this post. Follow Himanshu Kumar for the Git workflow that's saved every project I've ever built. ↓ 12. Set Up Claude.MD and Memory. This is the feature that makes Claude Code feel like a real team member instead of a stranger you explain everything to every time. ClaudeMD is a memory file. You tell Claude Code about your project once. It remembers forever. Coding style preferences. Project architecture decisions. Technology stack. File naming conventions. Business logic rules. Without ClaudeMD: Every new conversation starts from zero. You explain the same things repeatedly. Output is inconsistent. With ClaudeMD: Claude knows your project. Claude follows your rules. Claude produces consistent, professional code. The difference between a sloppy freelancer and a reliable agency is consistency. Claude. MD gives you consistency without the agency overhead. Most people don't set this up and wonder why Claude Code gives different answers every time. ↓ 13. Automate with Tasks. This is where Claude Code stops being a tool and starts being an employee. Tasks let you define repeating workflows. "Every time I push code, run tests." "Every time I create a new file, add boilerplate." "Every time I start a session, check for errors." Automated. Hands-free. Consistent. You're doing these things manually every single day. The same checks. The same steps. The same routine. Tasks do them automatically. So you can focus on the work that actually makes money. Every manual task you automate is time you get back. And time is the only thing you can never make more of. Save this post. Follow Himanshu Kumar for the task automation templates that run my entire workflow. ↓ 14. Explore Features Most People Never Touch. The video covers features that 95% of Claude Code users don't know exist. Because they watched a 3-minute TikTok about Claude Code and think they're experts now. They're not. They're using 5% of a tool that can do everything. The full tutorial goes deep into features that most tutorials skip because they're "too advanced." They're not too advanced. They're too valuable for lazy creators to bother explaining. This video explains all of them. Clearly. For beginners. The 5% of features you don't know about are the 5% that make people rich. ↓ Let's zoom out. I just broke down 14 sections of Claude Code. Setup and installation. Desktop app. Dependencies. Code editor integration. Basic usage. Commands. Modes and shortcuts. Planning prompts. Model selection. Git and version control. Memory and Claude. MD. Tasks and automation. Advanced features. All in one video. All free. All beginner friendly. The person who masters even half of these in the next 2 weeks will be in the top 1% of Claude Code users. The top 1% of Claude Code users are the ones charging $5,000-$10,000 per project and building them in a single afternoon. Everyone else is asking ChatGPT to fix their resume. Same tools. Same access. Completely different outcomes. Because one person treats AI like a toy. And the other treats it like a business. ↓ Here's the hard truth nobody wants to hear. You don't have a talent problem. You don't have an intelligence problem. You don't have a resources problem. You have an action problem. Everything I just listed has a free tutorial right here in the attached video. 33 minutes. That's it. 33 minutes to learn the tool that people are using to build $5,000-$20,000/month businesses. You spent more time today scrolling Twitter than it takes to watch this video. You spent more time this week watching Netflix than it takes to master Claude Code basics. You spent more time this month doing nothing than it would take to completely change your income. The information is free. The tool is accessible. The opportunity is here. The only thing missing is you caring enough to start. ↓ CANCEL your plans this week. This isn't optional anymore. The people learning Claude Code right now will be building apps for the people who didn't learn it. That's not a prediction. That's already happening. Companies are replacing $150/hour developers with one person and Claude Code. If you code: learn Claude Code or become half as valuable by next year. If you don't code: learn Claude Code or miss the biggest opportunity to start earning from tech without a CS degree. There's no path forward that doesn't include AI coding tools. None. You have one window. Right now. This week. ↓ Here's your action plan for the next 7 days: Day 1: Watch the full video. Install Claude Code. Set up dependencies. Day 2: Learn basic usage. Try 5 different commands. Day 3: Write your first planning prompt. Build a small project. Day 4: Set up Claude. MD. Configure your memory file. Day 5: Master modes and shortcuts. Build a second project faster. Day 6: Set up Git integration. Automate with tasks. Day 7: Build something real. A tool, an app, a website. Ship it. 7 days. One tool. One completely different skill set. One completely different income potential. Or 7 more days of scrolling Twitter watching other people build things while you "plan to start." Your call. ↓ This is the most important video you'll watch this year. 33 minutes. Complete Claude Code mastery. From zero to building real projects. Save this post. Come back to it every single day this week. Check off each section as you complete it. Follow Himanshu Kumar for daily Claude Code breakdowns, advanced tutorials, and the exact workflows that are turning beginners into $10K/month builders. The only thing between you and $10K/month with Claude Code is this video and 7 days. Don't waste them. You Must Follow me Himanshu Kumar, so i can send you DM.

Himanshu Kumar

101,376 次观看 • 3 个月前

CANCEL Your Weekend Plans, & Learn Claude Code Today. This Claude Code teaches more about vibe-coding in 30 mins than most tutorials do in hours. Save this, it'll change how you build forever People are building entire apps and charging clients $5,000 to $20,000 using Claude Code. This Claude Code video is a goldmine. Full Claude Code tutorial. Beginner to pro. Every feature. Every setup step. Every best practice. Zero prior knowledge needed. Save it. Watch it tonight. Not tomorrow. Tonight. Follow Himanshu Kumar so you don't miss the breakdowns for each feature. This is your complete Claude Code roadmap. Lose it and you lose the next 12 months of income. ↓ 1. Understand What Claude Code Actually Is. You think Claude Code is just another chatbot. It's not. And that misunderstanding is why you're broke. ChatGPT gives you text. Claude Code gives you software. It runs in your terminal. It reads your entire codebase. It writes files directly to your project. It runs commands on your machine. It debugs errors autonomously. It builds features end to end. You're not chatting. You're deploying a developer. One that works 24/7. Never asks for a raise. Never calls in sick. Never pushes broken code at 5 PM on a Friday. People are charging clients $5,000-$10,000 for apps they built with Claude Code in 3 hours. And you didn't even know this tool existed because you're still asking ChatGPT to write you a to-do list. The gap between you and people making money with AI isn't intelligence. It's awareness. Now you're aware. Save this post. Follow Himanshu Kumar for the complete breakdown of every Claude Code feature. ↓ 2. Set Up Claude Code Properly. Most people quit here. "It's too complicated." "I don't know terminal." "I'll set it up later." Later never comes. And "complicated" means "I watched for 30 seconds and gave up." The setup takes 10 minutes. Install Node.js. Install Claude Code via npm. Authenticate your account. Open your terminal. Done. 10 minutes. You spent longer this morning deciding what to have for breakfast. The video walks through every single click. Every command. Every screen. Assuming you know absolutely nothing. If you can download an app on your phone, you can set up Claude Code. It's the same level of difficulty. But you'll still tell yourself it's "too technical" because that excuse is more comfortable than admitting you're just scared to try something new. This is the setup that everything else builds on. Skip it and nothing works. ↓ 3. Use the Desktop App. You don't even need to live in the terminal if you don't want to. Claude Code has a desktop app. Clean interface. Visual feedback. Everything you need without touching command line. But here's the thing most people don't know: The desktop app isn't just a pretty wrapper. It lets you manage projects visually. See file changes in real time. Switch between projects instantly. The people making money with Claude Code use the desktop app for client projects because it's faster to manage multiple builds simultaneously. You're still opening 14 browser tabs to organize one project. They open one app and everything's there. Efficiency isn't a personality trait. It's a tool choice. Save this post. Follow Himanshu Kumar for the desktop app workflow that handles 5 client projects at once. ↓ 4. Install the Right Dependencies. This is where beginners silently fail and blame the tool. Claude Code needs certain dependencies installed to work properly. Miss one and everything breaks. Then you go on Twitter and say "Claude Code doesn't work." It works fine. You just didn't read the setup guide. The video covers every dependency you need. What to install. How to install it. How to verify it's working. No guessing. No Stack Overflow rabbit holes at midnight. No "why isn't this working" for 3 hours. Watch the dependency section once. Follow every step. Never deal with setup issues again. You spent more time last week troubleshooting a printer than this takes. ↓ 5. Work Inside Your Code Editor. Claude Code integrates directly with your code editor. VS Code. Cursor. Whatever you use. It's not a separate window you alt-tab between. It's right there. In your workflow. You type a request. Claude writes the code. The code appears in your editor. You review it. Accept it. Done. No copy pasting between windows. No reformatting code that got mangled in transit. No "which version was the right one." It's like pair programming with someone who never gets distracted, never argues about naming conventions, and actually writes code that works on the first try. Your current coding process is: Google the problem, read 5 answers on Stack Overflow, copy the wrong one, debug for an hour, find the right one, paste it in, break something else, repeat. Claude Code's process is: describe what you want, get working code, move on with your life. Same hour. One method produces working software. The other produces frustration and a browser history full of Stack Overflow tabs. Stop coding the hard way. Save this post. Follow Himanshu Kumar for code editor setup guides and integration tips. ↓ 6. Master Basic Usage. Most people learn 5% of a tool and say they "know" it. You "know" Photoshop because you can crop an image. You "know" Excel because you can sum a column. You "know" Claude Code because you asked it one question. Basic usage means: How to give Claude Code context about your project. How to ask for changes to existing code. How to generate new files and features. How to review what Claude produces. How to iterate when the output isn't perfect. These basics are the foundation of everything. Skip them and every advanced feature feels confusing. Master them and every advanced feature feels obvious. The video breaks down each one with real examples. Not theory. Actual usage on actual projects. You've been using AI tools at 5% capacity and wondering why your results are 5% of what others get. Save this post. Follow Himanshu Kumar for daily Claude Code usage tips. ↓ 7. Learn Every Command. Claude Code has commands that most users never discover. Because most users type one message and expect magic. That's not how professionals use it. Professionals use specific commands that tell Claude Code exactly what to do, how to do it, and what constraints to follow. The difference between a beginner and someone making $10K/month with Claude Code is knowing which command to use and when. The video walks through every single one. Not just what they do. But when to use each one. And why one command is better than another for specific situations. You've been using Claude Code like a hammer. These commands turn it into a full toolbox. Stop treating a power tool like a blunt instrument. Save this post. Follow Himanshu Kumar for the command cheat sheet I use daily. ↓ 8. Understand Modes and Shortcuts. Speed matters. The person who builds an app in 2 hours charges $5,000. The person who builds the same app in 2 days charges $2,000. Same app. Same quality. Different speed. Different income. Claude Code has modes that change how it operates. And shortcuts that cut your workflow time in half. Most people don't know either exists. They use Claude Code in default mode for everything. Like driving a car in first gear on the highway. Technically it works. But everyone is passing you. The video shows you every mode. Every shortcut. Every time-saving trick that separates the people charging $2,000 per project from the people charging $10,000. Speed is money. Literally. Save this post. Follow Himanshu Kumar for the shortcuts that cut my build time by 60%. ↓ 9. Write a Proper Planning Prompt. This is the section that separates amateurs from professionals. And it's the section most people skip. A planning prompt tells Claude Code what you're building before you start building it. Architecture. File structure. Technologies. Features. Constraints. Edge cases. Without a planning prompt, Claude Code guesses. And guessing produces garbage. With a planning prompt, Claude Code executes a clear plan. And clear plans produce working software. The video shows you exactly how to write a planning prompt that makes Claude Code produce professional-grade output on the first try. "But I just want to start coding." That's why your code breaks every time. That's why you restart projects 4 times. That's why nothing you build ever gets finished. Because you refuse to plan. A 5-minute planning prompt saves you 5 hours of debugging. But you'd rather skip the 5 minutes and suffer through the 5 hours because patience isn't your thing. And that's exactly why you're not making money. Planning is the most underpaid skill in coding. And the most overpaid when you master it. Save this post. Follow Himanshu Kumar for the planning prompt templates I use for every client project. ↓ 10. Choose the Right Model. Claude Code lets you select different AI models. Not all models are the same. Not all tasks need the same model. Using the most powerful model for a simple task wastes credits. Using a basic model for a complex task wastes time. The video explains: Which model to use for quick fixes. Which model to use for complex architecture. Which model to use for debugging. Which model to use for code generation. Most people pick one model and use it for everything. That's like using a sledgehammer to hang a picture frame. Model selection is strategy. And strategy is money. The people making $10K/month with Claude Code are strategic about every credit they spend. You're burning through credits because you use the most expensive model to write a hello world. ↓ 11. Use Git and Version Control. If you're not using version control, you're one mistake away from losing everything. Claude Code integrates with Git. Every change tracked. Every version saved. Every mistake reversible. Without Git: Claude makes a change. It breaks something. You can't undo it. You start over. 3 hours wasted. With Git: Claude makes a change. It breaks something. You roll back in 5 seconds. Keep working. Version control isn't optional. It's insurance. And the people not using it are the same people who say "I lost my entire project" like it's something that just happens. It doesn't just happen. It happens because you didn't set up Git. The video walks through the entire Git integration. Save this post. Follow Himanshu Kumar for the Git workflow that's saved every project I've ever built. ↓ 12. Set Up Claude MD and Memory. This is the feature that makes Claude Code feel like a real team member instead of a stranger you explain everything to every time. ClaudeMD is a memory file. You tell Claude Code about your project once. It remembers forever. Coding style preferences. Project architecture decisions. Technology stack. File naming conventions. Business logic rules. Without ClaudeMD: Every new conversation starts from zero. You explain the same things repeatedly. Output is inconsistent. With ClaudeMD: Claude knows your project. Claude follows your rules. Claude produces consistent, professional code. The difference between a sloppy freelancer and a reliable agency is consistency. Claude. MD gives you consistency without the agency overhead. Most people don't set this up and wonder why Claude Code gives different answers every time. ↓ 13. Automate with Tasks. This is where Claude Code stops being a tool and starts being an employee. Tasks let you define repeating workflows. "Every time I push code, run tests." "Every time I create a new file, add boilerplate." "Every time I start a session, check for errors." Automated. Hands-free. Consistent. You're doing these things manually every single day. The same checks. The same steps. The same routine. Tasks do them automatically. So you can focus on the work that actually makes money. Every manual task you automate is time you get back. And time is the only thing you can never make more of. Save this post. Follow Himanshu Kumar for the task automation templates that run my entire workflow. ↓ 14. Explore Features Most People Never Touch. The video covers features that 95% of Claude Code users don't know exist. Because they watched a 3-minute TikTok about Claude Code and think they're experts now. They're not. They're using 5% of a tool that can do everything. The full tutorial goes deep into features that most tutorials skip because they're "too advanced." They're not too advanced. They're too valuable for lazy creators to bother explaining. This video explains all of them. Clearly. For beginners. The 5% of features you don't know about are the 5% that make people rich. ↓ Let's zoom out. I just broke down 14 sections of Claude Code. Setup and installation. Desktop app. Dependencies. Code editor integration. Basic usage. Commands. Modes and shortcuts. Planning prompts. Model selection. Git and version control. Memory and Claude. MD. Tasks and automation. Advanced features. All in one video. All free. All beginner friendly. The person who masters even half of these in the next 2 weeks will be in the top 1% of Claude Code users. The top 1% of Claude Code users are the ones charging $5,000-$10,000 per project and building them in a single afternoon. Everyone else is asking ChatGPT to fix their resume. Same tools. Same access. Completely different outcomes. Because one person treats AI like a toy. And the other treats it like a business. ↓ Here's the hard truth nobody wants to hear. You don't have a talent problem. You don't have an intelligence problem. You don't have a resources problem. You have an action problem. Everything I just listed has a free tutorial right here in the attached video. 33 minutes. That's it. 33 minutes to learn the tool that people are using to build $5,000-$20,000/month businesses. You spent more time today scrolling Twitter than it takes to watch this video. You spent more time this week watching Netflix than it takes to master Claude Code basics. You spent more time this month doing nothing than it would take to completely change your income. The information is free. The tool is accessible. The opportunity is here. The only thing missing is you caring enough to start. ↓ CANCEL your plans this week. This isn't optional anymore. The people learning Claude Code right now will be building apps for the people who didn't learn it. That's not a prediction. That's already happening. Companies are replacing $150/hour developers with one person and Claude Code. If you code: learn Claude Code or become half as valuable by next year. If you don't code: learn Claude Code or miss the biggest opportunity to start earning from tech without a CS degree. There's no path forward that doesn't include AI coding tools. None. You have one window. Right now. This week. ↓ Here's your action plan for the next 7 days: Day 1: Watch the full video. Install Claude Code. Set up dependencies. Day 2: Learn basic usage. Try 5 different commands. Day 3: Write your first planning prompt. Build a small project. Day 4: Set up Claude. MD. Configure your memory file. Day 5: Master modes and shortcuts. Build a second project faster. Day 6: Set up Git integration. Automate with tasks. Day 7: Build something real. A tool, an app, a website. Ship it. 7 days. One tool. One completely different skill set. One completely different income potential. Or 7 more days of scrolling Twitter watching other people build things while you "plan to start." Your call. ↓ This is the most important video you'll watch this year. 33 minutes. Complete Claude Code mastery. From zero to building real projects. Save this post. Come back to it every single day this week. Check off each section as you complete it. Follow Himanshu Kumarfor daily Claude Code breakdowns, advanced tutorials, and the exact workflows that are turning beginners into $10K/month builders. The only thing between you and $10K/month with Claude Code is this video and 7 days. Don't waste them. You Must Follow me Himanshu Kumar, so i can send you DM.

CANCEL Your Weekend Plans, & Learn Claude Code Today. This Claude Code teaches more about vibe-coding in 30 mins than most tutorials do in hours. Save this, it'll change how you build forever People are building entire apps and charging clients $5,000 to $20,000 using Claude Code. This Claude Code video is a goldmine. Full Claude Code tutorial. Beginner to pro. Every feature. Every setup step. Every best practice. Zero prior knowledge needed. Save it. Watch it tonight. Not tomorrow. Tonight. Follow Himanshu Kumar so you don't miss the breakdowns for each feature. This is your complete Claude Code roadmap. Lose it and you lose the next 12 months of income. ↓ 1. Understand What Claude Code Actually Is. You think Claude Code is just another chatbot. It's not. And that misunderstanding is why you're broke. ChatGPT gives you text. Claude Code gives you software. It runs in your terminal. It reads your entire codebase. It writes files directly to your project. It runs commands on your machine. It debugs errors autonomously. It builds features end to end. You're not chatting. You're deploying a developer. One that works 24/7. Never asks for a raise. Never calls in sick. Never pushes broken code at 5 PM on a Friday. People are charging clients $5,000-$10,000 for apps they built with Claude Code in 3 hours. And you didn't even know this tool existed because you're still asking ChatGPT to write you a to-do list. The gap between you and people making money with AI isn't intelligence. It's awareness. Now you're aware. Save this post. Follow Himanshu Kumar for the complete breakdown of every Claude Code feature. ↓ 2. Set Up Claude Code Properly. Most people quit here. "It's too complicated." "I don't know terminal." "I'll set it up later." Later never comes. And "complicated" means "I watched for 30 seconds and gave up." The setup takes 10 minutes. Install Node.js. Install Claude Code via npm. Authenticate your account. Open your terminal. Done. 10 minutes. You spent longer this morning deciding what to have for breakfast. The video walks through every single click. Every command. Every screen. Assuming you know absolutely nothing. If you can download an app on your phone, you can set up Claude Code. It's the same level of difficulty. But you'll still tell yourself it's "too technical" because that excuse is more comfortable than admitting you're just scared to try something new. This is the setup that everything else builds on. Skip it and nothing works. ↓ 3. Use the Desktop App. You don't even need to live in the terminal if you don't want to. Claude Code has a desktop app. Clean interface. Visual feedback. Everything you need without touching command line. But here's the thing most people don't know: The desktop app isn't just a pretty wrapper. It lets you manage projects visually. See file changes in real time. Switch between projects instantly. The people making money with Claude Code use the desktop app for client projects because it's faster to manage multiple builds simultaneously. You're still opening 14 browser tabs to organize one project. They open one app and everything's there. Efficiency isn't a personality trait. It's a tool choice. Save this post. Follow Himanshu Kumar for the desktop app workflow that handles 5 client projects at once. ↓ 4. Install the Right Dependencies. This is where beginners silently fail and blame the tool. Claude Code needs certain dependencies installed to work properly. Miss one and everything breaks. Then you go on Twitter and say "Claude Code doesn't work." It works fine. You just didn't read the setup guide. The video covers every dependency you need. What to install. How to install it. How to verify it's working. No guessing. No Stack Overflow rabbit holes at midnight. No "why isn't this working" for 3 hours. Watch the dependency section once. Follow every step. Never deal with setup issues again. You spent more time last week troubleshooting a printer than this takes. ↓ 5. Work Inside Your Code Editor. Claude Code integrates directly with your code editor. VS Code. Cursor. Whatever you use. It's not a separate window you alt-tab between. It's right there. In your workflow. You type a request. Claude writes the code. The code appears in your editor. You review it. Accept it. Done. No copy pasting between windows. No reformatting code that got mangled in transit. No "which version was the right one." It's like pair programming with someone who never gets distracted, never argues about naming conventions, and actually writes code that works on the first try. Your current coding process is: Google the problem, read 5 answers on Stack Overflow, copy the wrong one, debug for an hour, find the right one, paste it in, break something else, repeat. Claude Code's process is: describe what you want, get working code, move on with your life. Same hour. One method produces working software. The other produces frustration and a browser history full of Stack Overflow tabs. Stop coding the hard way. Save this post. Follow Himanshu Kumar for code editor setup guides and integration tips. ↓ 6. Master Basic Usage. Most people learn 5% of a tool and say they "know" it. You "know" Photoshop because you can crop an image. You "know" Excel because you can sum a column. You "know" Claude Code because you asked it one question. Basic usage means: How to give Claude Code context about your project. How to ask for changes to existing code. How to generate new files and features. How to review what Claude produces. How to iterate when the output isn't perfect. These basics are the foundation of everything. Skip them and every advanced feature feels confusing. Master them and every advanced feature feels obvious. The video breaks down each one with real examples. Not theory. Actual usage on actual projects. You've been using AI tools at 5% capacity and wondering why your results are 5% of what others get. Save this post. Follow Himanshu Kumar for daily Claude Code usage tips. ↓ 7. Learn Every Command. Claude Code has commands that most users never discover. Because most users type one message and expect magic. That's not how professionals use it. Professionals use specific commands that tell Claude Code exactly what to do, how to do it, and what constraints to follow. The difference between a beginner and someone making $10K/month with Claude Code is knowing which command to use and when. The video walks through every single one. Not just what they do. But when to use each one. And why one command is better than another for specific situations. You've been using Claude Code like a hammer. These commands turn it into a full toolbox. Stop treating a power tool like a blunt instrument. Save this post. Follow Himanshu Kumar for the command cheat sheet I use daily. ↓ 8. Understand Modes and Shortcuts. Speed matters. The person who builds an app in 2 hours charges $5,000. The person who builds the same app in 2 days charges $2,000. Same app. Same quality. Different speed. Different income. Claude Code has modes that change how it operates. And shortcuts that cut your workflow time in half. Most people don't know either exists. They use Claude Code in default mode for everything. Like driving a car in first gear on the highway. Technically it works. But everyone is passing you. The video shows you every mode. Every shortcut. Every time-saving trick that separates the people charging $2,000 per project from the people charging $10,000. Speed is money. Literally. Save this post. Follow Himanshu Kumar for the shortcuts that cut my build time by 60%. ↓ 9. Write a Proper Planning Prompt. This is the section that separates amateurs from professionals. And it's the section most people skip. A planning prompt tells Claude Code what you're building before you start building it. Architecture. File structure. Technologies. Features. Constraints. Edge cases. Without a planning prompt, Claude Code guesses. And guessing produces garbage. With a planning prompt, Claude Code executes a clear plan. And clear plans produce working software. The video shows you exactly how to write a planning prompt that makes Claude Code produce professional-grade output on the first try. "But I just want to start coding." That's why your code breaks every time. That's why you restart projects 4 times. That's why nothing you build ever gets finished. Because you refuse to plan. A 5-minute planning prompt saves you 5 hours of debugging. But you'd rather skip the 5 minutes and suffer through the 5 hours because patience isn't your thing. And that's exactly why you're not making money. Planning is the most underpaid skill in coding. And the most overpaid when you master it. Save this post. Follow Himanshu Kumar for the planning prompt templates I use for every client project. ↓ 10. Choose the Right Model. Claude Code lets you select different AI models. Not all models are the same. Not all tasks need the same model. Using the most powerful model for a simple task wastes credits. Using a basic model for a complex task wastes time. The video explains: Which model to use for quick fixes. Which model to use for complex architecture. Which model to use for debugging. Which model to use for code generation. Most people pick one model and use it for everything. That's like using a sledgehammer to hang a picture frame. Model selection is strategy. And strategy is money. The people making $10K/month with Claude Code are strategic about every credit they spend. You're burning through credits because you use the most expensive model to write a hello world. ↓ 11. Use Git and Version Control. If you're not using version control, you're one mistake away from losing everything. Claude Code integrates with Git. Every change tracked. Every version saved. Every mistake reversible. Without Git: Claude makes a change. It breaks something. You can't undo it. You start over. 3 hours wasted. With Git: Claude makes a change. It breaks something. You roll back in 5 seconds. Keep working. Version control isn't optional. It's insurance. And the people not using it are the same people who say "I lost my entire project" like it's something that just happens. It doesn't just happen. It happens because you didn't set up Git. The video walks through the entire Git integration. Save this post. Follow Himanshu Kumar for the Git workflow that's saved every project I've ever built. ↓ 12. Set Up Claude MD and Memory. This is the feature that makes Claude Code feel like a real team member instead of a stranger you explain everything to every time. ClaudeMD is a memory file. You tell Claude Code about your project once. It remembers forever. Coding style preferences. Project architecture decisions. Technology stack. File naming conventions. Business logic rules. Without ClaudeMD: Every new conversation starts from zero. You explain the same things repeatedly. Output is inconsistent. With ClaudeMD: Claude knows your project. Claude follows your rules. Claude produces consistent, professional code. The difference between a sloppy freelancer and a reliable agency is consistency. Claude. MD gives you consistency without the agency overhead. Most people don't set this up and wonder why Claude Code gives different answers every time. ↓ 13. Automate with Tasks. This is where Claude Code stops being a tool and starts being an employee. Tasks let you define repeating workflows. "Every time I push code, run tests." "Every time I create a new file, add boilerplate." "Every time I start a session, check for errors." Automated. Hands-free. Consistent. You're doing these things manually every single day. The same checks. The same steps. The same routine. Tasks do them automatically. So you can focus on the work that actually makes money. Every manual task you automate is time you get back. And time is the only thing you can never make more of. Save this post. Follow Himanshu Kumar for the task automation templates that run my entire workflow. ↓ 14. Explore Features Most People Never Touch. The video covers features that 95% of Claude Code users don't know exist. Because they watched a 3-minute TikTok about Claude Code and think they're experts now. They're not. They're using 5% of a tool that can do everything. The full tutorial goes deep into features that most tutorials skip because they're "too advanced." They're not too advanced. They're too valuable for lazy creators to bother explaining. This video explains all of them. Clearly. For beginners. The 5% of features you don't know about are the 5% that make people rich. ↓ Let's zoom out. I just broke down 14 sections of Claude Code. Setup and installation. Desktop app. Dependencies. Code editor integration. Basic usage. Commands. Modes and shortcuts. Planning prompts. Model selection. Git and version control. Memory and Claude. MD. Tasks and automation. Advanced features. All in one video. All free. All beginner friendly. The person who masters even half of these in the next 2 weeks will be in the top 1% of Claude Code users. The top 1% of Claude Code users are the ones charging $5,000-$10,000 per project and building them in a single afternoon. Everyone else is asking ChatGPT to fix their resume. Same tools. Same access. Completely different outcomes. Because one person treats AI like a toy. And the other treats it like a business. ↓ Here's the hard truth nobody wants to hear. You don't have a talent problem. You don't have an intelligence problem. You don't have a resources problem. You have an action problem. Everything I just listed has a free tutorial right here in the attached video. 33 minutes. That's it. 33 minutes to learn the tool that people are using to build $5,000-$20,000/month businesses. You spent more time today scrolling Twitter than it takes to watch this video. You spent more time this week watching Netflix than it takes to master Claude Code basics. You spent more time this month doing nothing than it would take to completely change your income. The information is free. The tool is accessible. The opportunity is here. The only thing missing is you caring enough to start. ↓ CANCEL your plans this week. This isn't optional anymore. The people learning Claude Code right now will be building apps for the people who didn't learn it. That's not a prediction. That's already happening. Companies are replacing $150/hour developers with one person and Claude Code. If you code: learn Claude Code or become half as valuable by next year. If you don't code: learn Claude Code or miss the biggest opportunity to start earning from tech without a CS degree. There's no path forward that doesn't include AI coding tools. None. You have one window. Right now. This week. ↓ Here's your action plan for the next 7 days: Day 1: Watch the full video. Install Claude Code. Set up dependencies. Day 2: Learn basic usage. Try 5 different commands. Day 3: Write your first planning prompt. Build a small project. Day 4: Set up Claude. MD. Configure your memory file. Day 5: Master modes and shortcuts. Build a second project faster. Day 6: Set up Git integration. Automate with tasks. Day 7: Build something real. A tool, an app, a website. Ship it. 7 days. One tool. One completely different skill set. One completely different income potential. Or 7 more days of scrolling Twitter watching other people build things while you "plan to start." Your call. ↓ This is the most important video you'll watch this year. 33 minutes. Complete Claude Code mastery. From zero to building real projects. Save this post. Come back to it every single day this week. Check off each section as you complete it. Follow Himanshu Kumarfor daily Claude Code breakdowns, advanced tutorials, and the exact workflows that are turning beginners into $10K/month builders. The only thing between you and $10K/month with Claude Code is this video and 7 days. Don't waste them. You Must Follow me Himanshu Kumar, so i can send you DM.

Himanshu Kumar

85,668 次观看 • 2 个月前