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"Superfluid Vortices as a Model for Gravitational Precession" with Implications for Electromagnetism Authors: Matt Baker, Alan Baker Superfluid vortices in a Bose-Einstein condensate provide a framework for modeling gravitational effects, particularly the precession of planetary orbits, building on concepts of superfluidity in quantum systems. Calculations using modified Navier-Stokes equations...

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Matt Baker

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History is made: Fine structure mystery solved? /🧵 UPDATE: My model may have solved an outstanding question in physics: the origin of the fine structure constant. I found a robust source and it works even with some tuning within -3% to 7% over a massive range of non-linear fields. What is the fine structure constant? Watch the video below and you'll find out. Essentially its a mysterious constant that occurs in atomic and subatomic physics that has no unit. Countless people have tried to find a reason for it, and most have turned to numerology. I did something that only could work in this age of LLMs and someone who is a motivated expert in the field. Did a massive paper search, found the material in 10 minutes, aligned everything, then had it double check for me. Note that I wasn't looking for it. It occurred to me by accident when I was developing my bilinear principle of electromagnetic interaction. Within an hour, I had a value for it. My idea was simple: apply my non-linear soliton model to the electron in a way that makes my current x current interaction make sense not just at the atomic level but the subatomic level. Look at E = mc^2. It's the same as E = (sqrt(m) c)(sqrt(m) c). If sqrt(m) could be scaled to a charge, then you could interpret it as two scaled currents multiplying against each other. This is almost identical to the equation for the energy contained within an inductor L with a current I running through it: E = 0.5 L I^2. So far, sketchy right? Here's the thing, vortexes are solitons and they can exist in superfluids -- they even have quantised "Noether charge". They acquire a mass from their vorticity, have an energy associated with them and can move at any speed inside the medium with a different speed. Exactly like electrons! So my idea was to map the electron exactly to these vortices. If I could predict the mass of the electron, I could actually finally connect mass to electromagentism properly (Planck's argument is elegant but indirect). I used dimensional analysis to do this. It took a few tries to get it right and I had it checked by 10 different instances of the highest powered LLMs. With this first order approximation I got within 1/4th of the mass. It was almost exactly 1/4th... so I realised, I had to use a double cover to make it like SU(2). After another half an hour of research, I felt confident in using something like Dirac's scissor to do it. A kind of knot within the electron. It got me within 1.2% of the mass of the electron... but there was a problem, the mass of the electron occurred on the other side of the equation. I cringed at myself. Such an amateur mistake, I thought. But the LLM (Opus 4.5) actually corrected me and told me I had just accidentally derived the fine structure constant. Why? Because the mass terms cancelled out, or if one prefers, leave behind a correction ratio. Set that correction ratio and you get a function that implicitly defines the fine structure constant through two "parameters": 1. The actual geometry of the soliton/vortex. 2. The non-linearity of the medium and how it changed the vortex core. I went back and found an equation from friggin' Lord Kelvin, derived in the Victorian era. I then found an analysis from 1970 on the Non-linear Schrodinger Equation (NLSE) applied to exactly this domain but for a larger scale soliton in superfluids -- it didn't matter though because a superfluid and the medium of space are scale free as I had proven in the superconduction part of my paper. In any case plugging these two approximation in got me within 1.2% of the fine structure constant by solving the following implicit equation: 1 = 8 * pi * alpha (Log_e(8/alpha) - alpha_core) Where alpha_core is 1.615 in the NLSE. The most incredible thing? It was a ROBUST value for thin ring vortices. Even if you adjusted the alpha_core from 1.5-2, the values for the fine structure constant only changed between -3% to +7%. I cannot emphasise how incredible this result is -- it means that the actual non-linear equation and the other candidate geometries that reproduce SU(2) can actually get the exact value of the fine structure constant. I don't know of anyone who even approached this problem in this way without making ansatz or postulates. I'm still checking through the math in disbelief. If it's wrong it's going to be beautifully wrong. All LLMs I've ran it through agree with the math, just not the foundations which is expected due to their QED bias. This will be in chapter 11 of my paper. I've already sketched out the full derivation. This SIGNIFICANTLY strengthens an already game changing paper. I promise it'll be the last discovery I include and thank you for your patience. I wrote this full update out because of the significance of this development. Details will be in the paper, even if there was a mistake (it'll be shuffled on its own into the appendix -- but I really hope it all checks out). 💝🥰 /End
15:57

History is made: Fine structure mystery solved? /🧵 UPDATE: My model may have solved an outstanding question in physics: the origin of the fine structure constant. I found a robust source and it works even with some tuning within -3% to 7% over a massive range of non-linear fields. What is the fine structure constant? Watch the video below and you'll find out. Essentially its a mysterious constant that occurs in atomic and subatomic physics that has no unit. Countless people have tried to find a reason for it, and most have turned to numerology. I did something that only could work in this age of LLMs and someone who is a motivated expert in the field. Did a massive paper search, found the material in 10 minutes, aligned everything, then had it double check for me. Note that I wasn't looking for it. It occurred to me by accident when I was developing my bilinear principle of electromagnetic interaction. Within an hour, I had a value for it. My idea was simple: apply my non-linear soliton model to the electron in a way that makes my current x current interaction make sense not just at the atomic level but the subatomic level. Look at E = mc^2. It's the same as E = (sqrt(m) c)(sqrt(m) c). If sqrt(m) could be scaled to a charge, then you could interpret it as two scaled currents multiplying against each other. This is almost identical to the equation for the energy contained within an inductor L with a current I running through it: E = 0.5 L I^2. So far, sketchy right? Here's the thing, vortexes are solitons and they can exist in superfluids -- they even have quantised "Noether charge". They acquire a mass from their vorticity, have an energy associated with them and can move at any speed inside the medium with a different speed. Exactly like electrons! So my idea was to map the electron exactly to these vortices. If I could predict the mass of the electron, I could actually finally connect mass to electromagentism properly (Planck's argument is elegant but indirect). I used dimensional analysis to do this. It took a few tries to get it right and I had it checked by 10 different instances of the highest powered LLMs. With this first order approximation I got within 1/4th of the mass. It was almost exactly 1/4th... so I realised, I had to use a double cover to make it like SU(2). After another half an hour of research, I felt confident in using something like Dirac's scissor to do it. A kind of knot within the electron. It got me within 1.2% of the mass of the electron... but there was a problem, the mass of the electron occurred on the other side of the equation. I cringed at myself. Such an amateur mistake, I thought. But the LLM (Opus 4.5) actually corrected me and told me I had just accidentally derived the fine structure constant. Why? Because the mass terms cancelled out, or if one prefers, leave behind a correction ratio. Set that correction ratio and you get a function that implicitly defines the fine structure constant through two "parameters": 1. The actual geometry of the soliton/vortex. 2. The non-linearity of the medium and how it changed the vortex core. I went back and found an equation from friggin' Lord Kelvin, derived in the Victorian era. I then found an analysis from 1970 on the Non-linear Schrodinger Equation (NLSE) applied to exactly this domain but for a larger scale soliton in superfluids -- it didn't matter though because a superfluid and the medium of space are scale free as I had proven in the superconduction part of my paper. In any case plugging these two approximation in got me within 1.2% of the fine structure constant by solving the following implicit equation: 1 = 8 * pi * alpha (Log_e(8/alpha) - alpha_core) Where alpha_core is 1.615 in the NLSE. The most incredible thing? It was a ROBUST value for thin ring vortices. Even if you adjusted the alpha_core from 1.5-2, the values for the fine structure constant only changed between -3% to +7%. I cannot emphasise how incredible this result is -- it means that the actual non-linear equation and the other candidate geometries that reproduce SU(2) can actually get the exact value of the fine structure constant. I don't know of anyone who even approached this problem in this way without making ansatz or postulates. I'm still checking through the math in disbelief. If it's wrong it's going to be beautifully wrong. All LLMs I've ran it through agree with the math, just not the foundations which is expected due to their QED bias. This will be in chapter 11 of my paper. I've already sketched out the full derivation. This SIGNIFICANTLY strengthens an already game changing paper. I promise it'll be the last discovery I include and thank you for your patience. I wrote this full update out because of the significance of this development. Details will be in the paper, even if there was a mistake (it'll be shuffled on its own into the appendix -- but I really hope it all checks out). 💝🥰 /End

Korobochka (コロボ) 🇦🇺✝️🇷🇺

47,180 Aufrufe • vor 8 Monaten

Astronomers have discovered an extraordinary star orbiting Sagittarius A*, the supermassive black hole at the centre of the Milky Way, on the most extreme stellar orbit observed there so far. The star, designated S301, was identified using the GRAVITY instrument and its upgraded GRAVITY+ system on ESO’s Very Large Telescope Interferometer in Chile. What makes S301 particularly important is not simply its enormous speed, but how deeply its orbit carries it into the strongly curved spacetime surrounding the black hole. S301 completes one orbit in only about 8.7 years, the shortest known period for a star around Sagittarius A*, and during its closest approach it passes roughly 1.78 billion kilometres from the black hole, only about 12 times the Earth–Sun distance and comparable to the distance between Saturn and the Sun. At that point it reaches around 25,000 km/s, more than 8% of the speed of light, making it the fastest known star in the Milky Way. Sagittarius A* contains approximately 4.3 million times the mass of the Sun, compressed into a region small enough to behave observationally as a black hole. Astronomers have been studying stars around it for decades because their trajectories provide exceptionally clean tests of gravity. The most famous example is S2, whose 16-year orbit has already allowed researchers to detect gravitational redshift and relativistic orbital precession exactly where general relativity predicts them. S301 takes this experiment much further. Its orbit is extremely elongated, with an eccentricity of about 0.98, and at pericentre it approaches Sagittarius A* roughly ten times more closely than S2 in terms of Schwarzschild radii. The resulting relativistic effects should therefore be considerably stronger. The most interesting consequence is that S301 may allow astronomers to directly measure the spin of Sagittarius A*. According to general relativity, a rotating black hole does not simply curve spacetime through its mass; its rotation also drags the surrounding spacetime with it. This phenomenon, known as frame dragging or the Lense–Thirring effect, produces an additional precession in the orbit of an object moving close to the black hole. The effect becomes rapidly weaker with distance, which is why it has been extremely difficult to detect using previously known stars around Sagittarius A*. S301 travels close enough that the change in its orbit caused by the black hole’s rotation may become measurable within roughly the next decade. Importantly, the researchers have not yet measured the spin of Sagittarius A* from S301. Rather, they have discovered a star whose orbit is sensitive enough to that spin that such a direct measurement may now be realistically achievable. The discovery was technically difficult because S301 is extraordinarily faint. In the infrared K band it has a magnitude of about 19.3, and ESO notes that it appears roughly two billion times fainter than Betelgeuse in the sky. GRAVITY achieves the necessary angular resolution by combining the light from four 8.2-metre Unit Telescopes of the VLT through interferometry, effectively producing a virtual telescope with far greater resolving power than any individual telescope. The team first clearly identified S301 in observations from 2023 and subsequently followed it during 2024 and 2025. Once its preliminary orbit was established, astronomers were able to trace it retrospectively in earlier data from 2021 and even find evidence for it in observations obtained in 2017. Altogether, 19 astrometric measurements were used to constrain its orbit. There is still an important limitation: S301 is currently too faint for researchers to obtain a reliable spectrum and radial velocity. Without that information, two possible three-dimensional orientations of its orbit remain compatible with the observations. Future instruments should resolve this problem. In particular, MICADO on ESO’s Extremely Large Telescope should be sensitive enough to obtain spectroscopy of S301 and determine its radial velocity, while continued observations with GRAVITY+ will refine its astrometry. Its next pericentre passage is expected in 2031, and observing at least two complete orbits should give researchers the precision needed to search for the subtle additional precession produced by the spin of Sagittarius A*. S301 may also provide clues about how stars end up so close to a supermassive black hole. Stars are unlikely to form normally at such small distances because the black hole’s tidal forces make the collapse of ordinary star-forming clouds extremely difficult. The researchers instead favour a scenario involving the Hills mechanism. S301 may originally have belonged to a tight binary system that approached Sagittarius A*. The black hole’s tidal gravity could have torn the binary apart, capturing S301 onto its present highly eccentric orbit while ejecting its companion at enormous velocity, potentially fast enough for that star to escape the Milky Way entirely. The observed orbit of S301 is consistent with this interpretation. The importance of the discovery therefore goes beyond setting a speed record. S301 effectively acts as a natural test particle moving through one of the strongest gravitational fields that astronomers can study using individual stars. Tracking its motion could provide the first direct stellar-dynamical measurement of the rotation of Sagittarius A*, improve tests of general relativity in the strong-field regime and, over longer timescales, potentially probe more subtle properties predicted for rotating Kerr black holes. Instead of observing the black hole itself, we can use the trajectory of S301 to map how Sagittarius A* deforms and twists the spacetime around it. 👉
1:09

Astronomers have discovered an extraordinary star orbiting Sagittarius A*, the supermassive black hole at the centre of the Milky Way, on the most extreme stellar orbit observed there so far. The star, designated S301, was identified using the GRAVITY instrument and its upgraded GRAVITY+ system on ESO’s Very Large Telescope Interferometer in Chile. What makes S301 particularly important is not simply its enormous speed, but how deeply its orbit carries it into the strongly curved spacetime surrounding the black hole. S301 completes one orbit in only about 8.7 years, the shortest known period for a star around Sagittarius A*, and during its closest approach it passes roughly 1.78 billion kilometres from the black hole, only about 12 times the Earth–Sun distance and comparable to the distance between Saturn and the Sun. At that point it reaches around 25,000 km/s, more than 8% of the speed of light, making it the fastest known star in the Milky Way. Sagittarius A* contains approximately 4.3 million times the mass of the Sun, compressed into a region small enough to behave observationally as a black hole. Astronomers have been studying stars around it for decades because their trajectories provide exceptionally clean tests of gravity. The most famous example is S2, whose 16-year orbit has already allowed researchers to detect gravitational redshift and relativistic orbital precession exactly where general relativity predicts them. S301 takes this experiment much further. Its orbit is extremely elongated, with an eccentricity of about 0.98, and at pericentre it approaches Sagittarius A* roughly ten times more closely than S2 in terms of Schwarzschild radii. The resulting relativistic effects should therefore be considerably stronger. The most interesting consequence is that S301 may allow astronomers to directly measure the spin of Sagittarius A*. According to general relativity, a rotating black hole does not simply curve spacetime through its mass; its rotation also drags the surrounding spacetime with it. This phenomenon, known as frame dragging or the Lense–Thirring effect, produces an additional precession in the orbit of an object moving close to the black hole. The effect becomes rapidly weaker with distance, which is why it has been extremely difficult to detect using previously known stars around Sagittarius A*. S301 travels close enough that the change in its orbit caused by the black hole’s rotation may become measurable within roughly the next decade. Importantly, the researchers have not yet measured the spin of Sagittarius A* from S301. Rather, they have discovered a star whose orbit is sensitive enough to that spin that such a direct measurement may now be realistically achievable. The discovery was technically difficult because S301 is extraordinarily faint. In the infrared K band it has a magnitude of about 19.3, and ESO notes that it appears roughly two billion times fainter than Betelgeuse in the sky. GRAVITY achieves the necessary angular resolution by combining the light from four 8.2-metre Unit Telescopes of the VLT through interferometry, effectively producing a virtual telescope with far greater resolving power than any individual telescope. The team first clearly identified S301 in observations from 2023 and subsequently followed it during 2024 and 2025. Once its preliminary orbit was established, astronomers were able to trace it retrospectively in earlier data from 2021 and even find evidence for it in observations obtained in 2017. Altogether, 19 astrometric measurements were used to constrain its orbit. There is still an important limitation: S301 is currently too faint for researchers to obtain a reliable spectrum and radial velocity. Without that information, two possible three-dimensional orientations of its orbit remain compatible with the observations. Future instruments should resolve this problem. In particular, MICADO on ESO’s Extremely Large Telescope should be sensitive enough to obtain spectroscopy of S301 and determine its radial velocity, while continued observations with GRAVITY+ will refine its astrometry. Its next pericentre passage is expected in 2031, and observing at least two complete orbits should give researchers the precision needed to search for the subtle additional precession produced by the spin of Sagittarius A*. S301 may also provide clues about how stars end up so close to a supermassive black hole. Stars are unlikely to form normally at such small distances because the black hole’s tidal forces make the collapse of ordinary star-forming clouds extremely difficult. The researchers instead favour a scenario involving the Hills mechanism. S301 may originally have belonged to a tight binary system that approached Sagittarius A*. The black hole’s tidal gravity could have torn the binary apart, capturing S301 onto its present highly eccentric orbit while ejecting its companion at enormous velocity, potentially fast enough for that star to escape the Milky Way entirely. The observed orbit of S301 is consistent with this interpretation. The importance of the discovery therefore goes beyond setting a speed record. S301 effectively acts as a natural test particle moving through one of the strongest gravitational fields that astronomers can study using individual stars. Tracking its motion could provide the first direct stellar-dynamical measurement of the rotation of Sagittarius A*, improve tests of general relativity in the strong-field regime and, over longer timescales, potentially probe more subtle properties predicted for rotating Kerr black holes. Instead of observing the black hole itself, we can use the trajectory of S301 to map how Sagittarius A* deforms and twists the spacetime around it. 👉

Erika 

290,760 Aufrufe • vor 3 Tagen

Are We Alone? Measuring vast cosmic distances requires specialized units tailored to the immense scales of the universe. From solar system explorations to intergalactic studies, we employ various distance units to describe the cosmos. 1. Astronomical Unit (AU) Definition: An astronomical unit equals the average distance between Earth and the Sun, approximately 149.6 million kilometers (92.96 million miles). Practical Applications: Calculating planetary orbits: For example, the distance between Earth and Mars varies between 0.52 AU and 2.52 AU, crucial for spacecraft mission planning like NASA's Perseverance Rover. Measuring exoplanet systems: The habitable zone around stars is often expressed in AU. Limitations: Restricted to the solar system: The AU becomes impractical for measuring distances to stars or galaxies. 2. Light-Year (ly) Definition: A light-year is the distance light travels in one Earth year, approximately 9.46 trillion kilometers (5.88 trillion miles). Practical Applications: Understanding cosmic timelines: A galaxy 10 million light-years away reveals its appearance 10 million years ago, helping astronomers study the universe's history. Popular science communication: Light-years are relatable for non-experts, making astronomy accessible to the public. Limitations: Indirect measurement: Determining light-years often relies on models and assumptions about stellar properties, introducing uncertainties. 3. Parsec (pc) Definition: A parsec equals 3.26 light-years or about 30.86 trillion kilometers (19.2 trillion miles). It originates from parallax measurements, where 1 parsec is the distance at which a star exhibits a parallax angle of 1 arcsecond. Practical Applications: Mapping stellar neighborhoods: The Gaia mission precisely measures parallax to map stars up to 10 kpc away. Galactic studies: Parsecs are essential for charting structures like the spiral arms of the Milky Way. Limitations: Limited by parallax accuracy: Beyond a few thousand parsecs, parallax measurements become unreliable due to diminishing angular shifts. 4. Kiloparsec (kpc) and Megaparsec (Mpc) Definition: 1 kiloparsec (kpc) = 1,000 parsecs 1 megaparsec (Mpc) = 1 million parsecs Practical Applications: Galaxy clusters: The Local Group spans ~1 Mpc, while the Virgo Supercluster extends over 16 Mpc. Cosmology: Distances to galaxies measured in Mpc are vital for understanding cosmic expansion via Hubble’s Law. Limitations: Dependence on redshift: For extremely distant galaxies, distances are inferred using redshift, introducing model-dependent errors. Scaling issues: Megaparsecs are too large for smaller-scale studies, and kiloparsecs are insufficient for large-scale structures. 5. Challenges in Measuring Distances While these units provide a framework, accurately determining distances remains a challenge: Uncertainties in standard candles: Techniques like Cepheid variables and Type Ia supernovae rely on assumptions about intrinsic brightness. Cosmic dust interference: Dust can obscure light, making distant measurements imprecise. Gravitational lensing effects: Light from distant objects can bend, distorting perceptions of distance. 6. Balancing Practicality and Precision Each unit has its niche, tailored to specific scales and research needs. AUs are invaluable for solar system navigation, while parsecs dominate stellar and galactic studies. Light-years serve as a bridge for public understanding. However, limitations persist, particularly for extremely distant objects where uncertainties grow. By refining methods such as parallax measurements, redshift techniques, and space-based telescopes like Gaia and JWST, we continue to push the boundaries of cosmic cartography. Understanding these units not only aids in grasping the universe’s vastness but also highlights the ingenuity behind astronomical discoveries. (Video
8:19

Are We Alone? Measuring vast cosmic distances requires specialized units tailored to the immense scales of the universe. From solar system explorations to intergalactic studies, we employ various distance units to describe the cosmos. 1. Astronomical Unit (AU) Definition: An astronomical unit equals the average distance between Earth and the Sun, approximately 149.6 million kilometers (92.96 million miles). Practical Applications: Calculating planetary orbits: For example, the distance between Earth and Mars varies between 0.52 AU and 2.52 AU, crucial for spacecraft mission planning like NASA's Perseverance Rover. Measuring exoplanet systems: The habitable zone around stars is often expressed in AU. Limitations: Restricted to the solar system: The AU becomes impractical for measuring distances to stars or galaxies. 2. Light-Year (ly) Definition: A light-year is the distance light travels in one Earth year, approximately 9.46 trillion kilometers (5.88 trillion miles). Practical Applications: Understanding cosmic timelines: A galaxy 10 million light-years away reveals its appearance 10 million years ago, helping astronomers study the universe's history. Popular science communication: Light-years are relatable for non-experts, making astronomy accessible to the public. Limitations: Indirect measurement: Determining light-years often relies on models and assumptions about stellar properties, introducing uncertainties. 3. Parsec (pc) Definition: A parsec equals 3.26 light-years or about 30.86 trillion kilometers (19.2 trillion miles). It originates from parallax measurements, where 1 parsec is the distance at which a star exhibits a parallax angle of 1 arcsecond. Practical Applications: Mapping stellar neighborhoods: The Gaia mission precisely measures parallax to map stars up to 10 kpc away. Galactic studies: Parsecs are essential for charting structures like the spiral arms of the Milky Way. Limitations: Limited by parallax accuracy: Beyond a few thousand parsecs, parallax measurements become unreliable due to diminishing angular shifts. 4. Kiloparsec (kpc) and Megaparsec (Mpc) Definition: 1 kiloparsec (kpc) = 1,000 parsecs 1 megaparsec (Mpc) = 1 million parsecs Practical Applications: Galaxy clusters: The Local Group spans ~1 Mpc, while the Virgo Supercluster extends over 16 Mpc. Cosmology: Distances to galaxies measured in Mpc are vital for understanding cosmic expansion via Hubble’s Law. Limitations: Dependence on redshift: For extremely distant galaxies, distances are inferred using redshift, introducing model-dependent errors. Scaling issues: Megaparsecs are too large for smaller-scale studies, and kiloparsecs are insufficient for large-scale structures. 5. Challenges in Measuring Distances While these units provide a framework, accurately determining distances remains a challenge: Uncertainties in standard candles: Techniques like Cepheid variables and Type Ia supernovae rely on assumptions about intrinsic brightness. Cosmic dust interference: Dust can obscure light, making distant measurements imprecise. Gravitational lensing effects: Light from distant objects can bend, distorting perceptions of distance. 6. Balancing Practicality and Precision Each unit has its niche, tailored to specific scales and research needs. AUs are invaluable for solar system navigation, while parsecs dominate stellar and galactic studies. Light-years serve as a bridge for public understanding. However, limitations persist, particularly for extremely distant objects where uncertainties grow. By refining methods such as parallax measurements, redshift techniques, and space-based telescopes like Gaia and JWST, we continue to push the boundaries of cosmic cartography. Understanding these units not only aids in grasping the universe’s vastness but also highlights the ingenuity behind astronomical discoveries. (Video

Erika 

15,378 Aufrufe • vor 1 Jahr

$AMD| $META is using $GOOGL to negotiate 🧵 The Ironwood pod is 5.1–10x more expensive annually ($148.3 million ÷ $14.87–$29.04 million) and 5.1–10x more expensive monthly ($12.36 million ÷ $1.24–$2.42 million) than renting 15 MI450 racks for equivalent compute. The rapidly evolving landscape of artificial intelligence infrastructure presents a complex interplay of technological innovation, market dynamics, and strategic maneuvering among major players. Recent leaked information suggesting that Meta Platforms ($META) might work with Google's Tensor Processing Unit (TPU) in 2027 has sparked speculation about its true intent. This leak is likely a strategic move by Meta to negotiate more favorable terms with AMD , leveraging the competitive dynamics of the AI hardware market to optimize its substantial investment in AI infrastructure. By examining the key elements of this scenario Meta's investment strategy, the comparative advantages of AMD's MI450 and Google's Ironwood TPU, and the broader market context; we can discern the potential beneficiaries and the strategic implications of this information. Meta's aggressive pursuit of AI capabilities is underscored by its planned expenditure of $66-72 billion on AI infrastructure in 2025, with expectations to escalate significantly in 2026. This investment is part of a broader strategy to build "titan clusters" like Prometheus, which are projected to reach 1 gigawatt of compute power by 2026. Such a scale of investment reflects Meta's recognition of the critical role that AI will play in its future growth, particularly in enhancing its social media platforms and developing new AI-driven applications. However, the financial burden of this infrastructure buildout necessitates a careful consideration of cost-effectiveness and scalability, which brings us to the leaked information about potential collaboration with Google's Ironwood TPU. Google's Ironwood TPU, introduced as the seventh-generation ASIC optimized for TensorFlow-based inference, represents a high-cost, cloud-locked solution priced at $445 million per pod (9,216 chips) over three years. This model, while offering significant performance gains and power efficiency, is tailored for pod-scale deployment and integrated with Google's cloud services, limiting flexibility and increasing costs for customers. In contrast, AMD's MI450 GPU, priced at $30,000–$40,000 per unit, provides a modular, open ROCm ecosystem that delivers comparable compute capacity at a fraction of the cost. Renting 15 MI450 racks could achieve similar 42+ exaFLOPS inference compute at 5–10x lower cost than renting a single Ironwood pod, underscoring AMD's competitive edge in terms of total cost of ownership (TCO). The leaked information about Meta's potential TPU deployment in 2027, therefore, can be interpreted as a negotiating tactic rather than a definitive shift in strategy. By signaling interest in Google's solution, Meta may be attempting to pressure AMD into offering more favorable terms/prices for 5-10GW. This tactic aligns with Meta's broader goal to finance most of its AI spend internally while exploring partnerships that can reduce costs and enhance flexibility. The post's emphasis on MI450's TCO advantage and its partnerships with major players like OpenAI, Microsoft, and Meta itself suggests that AMD is a critical component of Meta's AI infrastructure strategy. The threat of working with Google's TPU could prompt AMD to reassess its pricing, provide additional support, or offer incentives to retain Meta as a customer, thereby securing or expanding its market share. From a logical standpoint, Meta stands to benefit the most from this strategy. As a major buyer in a high-stakes market projected to surpass $1 trillion in annual spending by 2030, Meta's negotiating power is significant. The leaked information could lead to substantial cost savings on its $66-72 billion investment, enhancing its financial flexibility and allowing for further investment in AI capabilities. Moreover, this tactic reinforces Meta's position as a leader in the AI infrastructure race, potentially attracting more external financing for its data center projects and strengthening its competitive stance against other hyperscalers like Amazon and Microsoft. AMD could also benefit from this scenario. The negotiation pressure might lead to small short-term concessions, but it could also solidify long-term partnerships with Meta, ensuring continued demand for MI450 and other AI hardware solutions. Initially Meta's 42% allocation to AMD MI300X and its partnerships with Oracle, Dell, and HP indicates a deep integration of AMD's technology into Meta's infrastructure, which could be leveraged to maintain this relationship. For AMD, retaining Meta as a large key customer is crucial to capturing a larger share of the rapidly growing data center infrastructure market, driven by the insatiable demand for AI compute power. Google, on the other hand, faces a more limited benefit from this leaked information. While securing Meta as a customer would reinforce its position in the AI hardware market, the high cost and ecosystem lock-in of the Ironwood TPU might deter Meta from fully committing to this solution. The leaked information could prompt Google to reconsider its pricing or ecosystem strategy to remain competitive, but the immediate impact is likely to be minimal compared to the potential gains for Meta and AMD. Investors and market analysts also stand to benefit from this information, as it provides insights into the competitive dynamics of the AI hardware market. Adjustments in portfolios based on anticipated shifts in market share and profitability could lead to opportunities for those who correctly anticipate outcomes. The negotiation dynamic might introduce volatility, but it also highlights the strategic importance of cost-effective solutions in the AI infrastructure space. Lastly, the leaked information about Meta potentially working with Google's TPU in 2027 is likely a strategic move to negotiate with AMD, leveraging the competitive landscape to optimize its AI infrastructure investment. Meta, as the primary negotiator, stands to gain the most by securing better terms from AMD, reducing costs, and enhancing its financial flexibility. AMD, while initially at risk, could benefit from retaining a key customer and solidifying its market position. Google faces limited immediate benefits but may need to adapt its strategy to remain competitive. This scenario underscores the complex interplay of technology, market dynamics, and strategic maneuvering in the AI hardware market, where cost-effectiveness and scalability are paramount. As the data center infrastructure market continues to grow, the outcomes of such negotiations will shape the future of AI development and deployment.
1:12

$AMD| $META is using $GOOGL to negotiate 🧵 The Ironwood pod is 5.1–10x more expensive annually ($148.3 million ÷ $14.87–$29.04 million) and 5.1–10x more expensive monthly ($12.36 million ÷ $1.24–$2.42 million) than renting 15 MI450 racks for equivalent compute. The rapidly evolving landscape of artificial intelligence infrastructure presents a complex interplay of technological innovation, market dynamics, and strategic maneuvering among major players. Recent leaked information suggesting that Meta Platforms ($META) might work with Google's Tensor Processing Unit (TPU) in 2027 has sparked speculation about its true intent. This leak is likely a strategic move by Meta to negotiate more favorable terms with AMD , leveraging the competitive dynamics of the AI hardware market to optimize its substantial investment in AI infrastructure. By examining the key elements of this scenario Meta's investment strategy, the comparative advantages of AMD's MI450 and Google's Ironwood TPU, and the broader market context; we can discern the potential beneficiaries and the strategic implications of this information. Meta's aggressive pursuit of AI capabilities is underscored by its planned expenditure of $66-72 billion on AI infrastructure in 2025, with expectations to escalate significantly in 2026. This investment is part of a broader strategy to build "titan clusters" like Prometheus, which are projected to reach 1 gigawatt of compute power by 2026. Such a scale of investment reflects Meta's recognition of the critical role that AI will play in its future growth, particularly in enhancing its social media platforms and developing new AI-driven applications. However, the financial burden of this infrastructure buildout necessitates a careful consideration of cost-effectiveness and scalability, which brings us to the leaked information about potential collaboration with Google's Ironwood TPU. Google's Ironwood TPU, introduced as the seventh-generation ASIC optimized for TensorFlow-based inference, represents a high-cost, cloud-locked solution priced at $445 million per pod (9,216 chips) over three years. This model, while offering significant performance gains and power efficiency, is tailored for pod-scale deployment and integrated with Google's cloud services, limiting flexibility and increasing costs for customers. In contrast, AMD's MI450 GPU, priced at $30,000–$40,000 per unit, provides a modular, open ROCm ecosystem that delivers comparable compute capacity at a fraction of the cost. Renting 15 MI450 racks could achieve similar 42+ exaFLOPS inference compute at 5–10x lower cost than renting a single Ironwood pod, underscoring AMD's competitive edge in terms of total cost of ownership (TCO). The leaked information about Meta's potential TPU deployment in 2027, therefore, can be interpreted as a negotiating tactic rather than a definitive shift in strategy. By signaling interest in Google's solution, Meta may be attempting to pressure AMD into offering more favorable terms/prices for 5-10GW. This tactic aligns with Meta's broader goal to finance most of its AI spend internally while exploring partnerships that can reduce costs and enhance flexibility. The post's emphasis on MI450's TCO advantage and its partnerships with major players like OpenAI, Microsoft, and Meta itself suggests that AMD is a critical component of Meta's AI infrastructure strategy. The threat of working with Google's TPU could prompt AMD to reassess its pricing, provide additional support, or offer incentives to retain Meta as a customer, thereby securing or expanding its market share. From a logical standpoint, Meta stands to benefit the most from this strategy. As a major buyer in a high-stakes market projected to surpass $1 trillion in annual spending by 2030, Meta's negotiating power is significant. The leaked information could lead to substantial cost savings on its $66-72 billion investment, enhancing its financial flexibility and allowing for further investment in AI capabilities. Moreover, this tactic reinforces Meta's position as a leader in the AI infrastructure race, potentially attracting more external financing for its data center projects and strengthening its competitive stance against other hyperscalers like Amazon and Microsoft. AMD could also benefit from this scenario. The negotiation pressure might lead to small short-term concessions, but it could also solidify long-term partnerships with Meta, ensuring continued demand for MI450 and other AI hardware solutions. Initially Meta's 42% allocation to AMD MI300X and its partnerships with Oracle, Dell, and HP indicates a deep integration of AMD's technology into Meta's infrastructure, which could be leveraged to maintain this relationship. For AMD, retaining Meta as a large key customer is crucial to capturing a larger share of the rapidly growing data center infrastructure market, driven by the insatiable demand for AI compute power. Google, on the other hand, faces a more limited benefit from this leaked information. While securing Meta as a customer would reinforce its position in the AI hardware market, the high cost and ecosystem lock-in of the Ironwood TPU might deter Meta from fully committing to this solution. The leaked information could prompt Google to reconsider its pricing or ecosystem strategy to remain competitive, but the immediate impact is likely to be minimal compared to the potential gains for Meta and AMD. Investors and market analysts also stand to benefit from this information, as it provides insights into the competitive dynamics of the AI hardware market. Adjustments in portfolios based on anticipated shifts in market share and profitability could lead to opportunities for those who correctly anticipate outcomes. The negotiation dynamic might introduce volatility, but it also highlights the strategic importance of cost-effective solutions in the AI infrastructure space. Lastly, the leaked information about Meta potentially working with Google's TPU in 2027 is likely a strategic move to negotiate with AMD, leveraging the competitive landscape to optimize its AI infrastructure investment. Meta, as the primary negotiator, stands to gain the most by securing better terms from AMD, reducing costs, and enhancing its financial flexibility. AMD, while initially at risk, could benefit from retaining a key customer and solidifying its market position. Google faces limited immediate benefits but may need to adapt its strategy to remain competitive. This scenario underscores the complex interplay of technology, market dynamics, and strategic maneuvering in the AI hardware market, where cost-effectiveness and scalability are paramount. As the data center infrastructure market continues to grow, the outcomes of such negotiations will shape the future of AI development and deployment.

Mike

182,273 Aufrufe • vor 8 Monaten

TOPIC #44: BUILDING MOMENTUM FOR PI NETWORK OM Today let's discuss "building momentum". Sun Tzu was a Chinese military general, strategist, philosopher, and writer who lived during the Eastern Zhou period. Sun Tzu is traditionally credited as the author of The Art of War, an influential work of military strategy that has affected both Western and East Asian philosophy and military thought. In his chapter "Momentum" of Sun Tzu "The Art of War" said: 👉"Thus, a good warrior seeks it in the situation, not in the person, so he can choose the person and take advantage of the situation. A warrior who takes advantage of the situation fights like turning wood or stone. The nature of wood or stone is that when it is at peace, it is still; when it is in danger, it moves; when it is square, it stops; when it is round, it moves. Therefore, a warrior's situation is like turning a round stone on a mountain thousands of feet high; this is the situation." 👉Meaning: People who are good at leading troops in battle always try to create a favorable situation, and do not demand perfection from their subordinates, so they can select talents to utilize and create a favorable situation. People who are good at utilizing the situation command the army to fight, just like rolling wood and stones. The characteristics of wood and stone are that they are still when placed in a flat and stable place, and they roll when placed in a steep and rugged place. Square things are easy to be still, and round things are flexible to roll. Therefore, the favorable situation created by people who are good at commanding battles is like rolling a round stone down from a mountain, which is the so-called "momentum" ✳️Let's discuss how we can begin building good momentum before OM. In order to use the situation to successfully build momentum for OM at GCV $314,159, we need to consider our current situation: 1. We can see that blockchain shows more big amount transfers on the blockchain, such as thousands and hundreds of Pi per transaction. This could be from ecosystem payments or black market sales, but most of the thousands or hundreds seem to come from black market policy violations. We also notice that the migration speed is generally much slower, averaging around a thousand per day. We did observe that CT can reach a speed of 60,000 wallets per day. Therefore, we can estimate that migration speed is related to black market activities. When there are more black market activities, there is a higher risk to the project and CT will be more conservative. Therefore, we should focus on community education. We have an International Telegram Group where you are welcome to invite your friends or any pioneers who have gotten lost to join in. 2. Now, GCV data is much lower than before as previously estimated. This is because most of the community has used up their resources and run out of money to provide full Pi payments. Even though we don't have much GCV data, GCV still holds significant value in the hearts of most pioneers. Therefore, we can say that GCV $314,159, the primary currency function "value scale," has been successfully completed. 3. Now, we must move on to the next step: allowing Pi GCV to circulate in the Pi community ecosystem. It's crucial that we only create GCV data and not other data. "The Art of War" Chapter" Momentum" told us how to do it. 👉We should carefully consider strategies to incentivize merchants to join our ecosystem and automatically transfer pioneer purchase habits within it so that GCV data will automatically created by normal business. Since Pi is not yet recognized as a legal form of payment by any government, we are currently in the "circulate tool" phase within the community. As such, it may not be feasible to require merchants to accept full Pi payments. Understanding the operational constraints faced by merchants in this regard is essential. Merchants need FIAT for bookkeeping, government taxes, employee wages, rent, materials, and utilities. They can only ensure the sustainability of their business by accepting Pi as profit. 👉In light of the widespread economic challenges, which have resulted in high unemployment rates and financial hardships for many, businesses are also finding it tough to sell products, particularly small and emerging businesses. To support these businesses, we could introduce a phased approach, allowing them to accept partial payments in both FIAT and Pi, and gradually transition to full Pi payments when OM. This approach will undoubtedly be beneficial for them, and could contribute to GCV data generation. 👉Similarly, for pioneers facing financial difficulties due to inflation and unemployment, the option to save money on essential expenses through a combination of partial Pi and FIAT payments would be welcomed. It's crucial to highlight the potential benefits and savings that could be achieved through this approach, contributing to the growth of GCV as well. 🙏Implementing a method aligned with these practices will serve to attract more merchants and pioneers to join our network. Furthermore, broadening our user base and promoting the use of Pi for transactions is pivotal for the success of our ecosystem. This approach could lead to a collaborative and mutually beneficial ecosystem, providing value to all participants and driving the momentum of the Pi Network. ✳️In Conclusion: Only when we understand the situation well can we build momentum that benefits the Pi Network community as a whole. In this manner, the Pi Network ecosystem will exhibit heightened activity, and the GCV data will possess a robust and well-established foundation. When OM, there will be no uncertainty or necessity for CT announcements pertaining to the price. This is owing to widespread acceptance and acknowledgment of GCV within the pioneering and merchant as well as among service providers, over a span of 5 months. It is inconceivable that Dr. Nicolas would disavow GCV as the price established by pioneers. Hence, without a shadow of doubt, GCV will emerge as the price when OM. According to The Art of War, building momentum is crucial for success in any important situation. Of course, it applies to Pi Network as well! 👉Today, a video featuring products from the Canadian GCV merchant "Maiden's Heart" was published. The merchant accepts 70% fiat and 30% Pi payment and offers free shipping to the USA and Canada, with all prices inclusive of shipping fees. For further inquiries, please contact them via email at maidens.heart888@gmail.com or visit their website at Doris Yin 🪷🪷🪷
1:22

TOPIC #44: BUILDING MOMENTUM FOR PI NETWORK OM Today let's discuss "building momentum". Sun Tzu was a Chinese military general, strategist, philosopher, and writer who lived during the Eastern Zhou period. Sun Tzu is traditionally credited as the author of The Art of War, an influential work of military strategy that has affected both Western and East Asian philosophy and military thought. In his chapter "Momentum" of Sun Tzu "The Art of War" said: 👉"Thus, a good warrior seeks it in the situation, not in the person, so he can choose the person and take advantage of the situation. A warrior who takes advantage of the situation fights like turning wood or stone. The nature of wood or stone is that when it is at peace, it is still; when it is in danger, it moves; when it is square, it stops; when it is round, it moves. Therefore, a warrior's situation is like turning a round stone on a mountain thousands of feet high; this is the situation." 👉Meaning: People who are good at leading troops in battle always try to create a favorable situation, and do not demand perfection from their subordinates, so they can select talents to utilize and create a favorable situation. People who are good at utilizing the situation command the army to fight, just like rolling wood and stones. The characteristics of wood and stone are that they are still when placed in a flat and stable place, and they roll when placed in a steep and rugged place. Square things are easy to be still, and round things are flexible to roll. Therefore, the favorable situation created by people who are good at commanding battles is like rolling a round stone down from a mountain, which is the so-called "momentum" ✳️Let's discuss how we can begin building good momentum before OM. In order to use the situation to successfully build momentum for OM at GCV $314,159, we need to consider our current situation: 1. We can see that blockchain shows more big amount transfers on the blockchain, such as thousands and hundreds of Pi per transaction. This could be from ecosystem payments or black market sales, but most of the thousands or hundreds seem to come from black market policy violations. We also notice that the migration speed is generally much slower, averaging around a thousand per day. We did observe that CT can reach a speed of 60,000 wallets per day. Therefore, we can estimate that migration speed is related to black market activities. When there are more black market activities, there is a higher risk to the project and CT will be more conservative. Therefore, we should focus on community education. We have an International Telegram Group where you are welcome to invite your friends or any pioneers who have gotten lost to join in. 2. Now, GCV data is much lower than before as previously estimated. This is because most of the community has used up their resources and run out of money to provide full Pi payments. Even though we don't have much GCV data, GCV still holds significant value in the hearts of most pioneers. Therefore, we can say that GCV $314,159, the primary currency function "value scale," has been successfully completed. 3. Now, we must move on to the next step: allowing Pi GCV to circulate in the Pi community ecosystem. It's crucial that we only create GCV data and not other data. "The Art of War" Chapter" Momentum" told us how to do it. 👉We should carefully consider strategies to incentivize merchants to join our ecosystem and automatically transfer pioneer purchase habits within it so that GCV data will automatically created by normal business. Since Pi is not yet recognized as a legal form of payment by any government, we are currently in the "circulate tool" phase within the community. As such, it may not be feasible to require merchants to accept full Pi payments. Understanding the operational constraints faced by merchants in this regard is essential. Merchants need FIAT for bookkeeping, government taxes, employee wages, rent, materials, and utilities. They can only ensure the sustainability of their business by accepting Pi as profit. 👉In light of the widespread economic challenges, which have resulted in high unemployment rates and financial hardships for many, businesses are also finding it tough to sell products, particularly small and emerging businesses. To support these businesses, we could introduce a phased approach, allowing them to accept partial payments in both FIAT and Pi, and gradually transition to full Pi payments when OM. This approach will undoubtedly be beneficial for them, and could contribute to GCV data generation. 👉Similarly, for pioneers facing financial difficulties due to inflation and unemployment, the option to save money on essential expenses through a combination of partial Pi and FIAT payments would be welcomed. It's crucial to highlight the potential benefits and savings that could be achieved through this approach, contributing to the growth of GCV as well. 🙏Implementing a method aligned with these practices will serve to attract more merchants and pioneers to join our network. Furthermore, broadening our user base and promoting the use of Pi for transactions is pivotal for the success of our ecosystem. This approach could lead to a collaborative and mutually beneficial ecosystem, providing value to all participants and driving the momentum of the Pi Network. ✳️In Conclusion: Only when we understand the situation well can we build momentum that benefits the Pi Network community as a whole. In this manner, the Pi Network ecosystem will exhibit heightened activity, and the GCV data will possess a robust and well-established foundation. When OM, there will be no uncertainty or necessity for CT announcements pertaining to the price. This is owing to widespread acceptance and acknowledgment of GCV within the pioneering and merchant as well as among service providers, over a span of 5 months. It is inconceivable that Dr. Nicolas would disavow GCV as the price established by pioneers. Hence, without a shadow of doubt, GCV will emerge as the price when OM. According to The Art of War, building momentum is crucial for success in any important situation. Of course, it applies to Pi Network as well! 👉Today, a video featuring products from the Canadian GCV merchant "Maiden's Heart" was published. The merchant accepts 70% fiat and 30% Pi payment and offers free shipping to the USA and Canada, with all prices inclusive of shipping fees. For further inquiries, please contact them via email at [email protected] or visit their website at Doris Yin 🪷🪷🪷

Doris Yin 东方紫莲🪷

19,076 Aufrufe • vor 2 Jahren

This is another piece about #kaspa and why you should accumulate as much as possible. I think people don't realize yet the tangible progress that $kas had from the launch of the project. And who complains about the chart going sideways for a few months, that is called redistribution after an amazing run during the bear market. Enjoy. Kaspa: The Blockchain Speed Demon Redefining Crypto Efficiency Kaspa has emerged as a significant player in the cryptocurrency landscape, particularly noted for its advancements in transaction speed and scalability while maintaining the foundational principles of Bitcoin (BTC). Here's an insightful look into Kaspa's achievements and its relation to Bitcoin: Unprecedented Speed on Testnet: Kaspa's testnet has demonstrated remarkable speed, achieving up to 10 blocks per second (BPS). This is a monumental leap when compared to Bitcoin, which processes about one block every 10 minutes. This speed was showcased in Kaspa's Testnet 11, where the network's peer-to-peer dynamics and node health remained stable, indicating the robustness of its design. This capability is facilitated by Kaspa's unique implementation of the GHOSTDAG protocol, which allows for parallel processing of blocks, significantly enhancing throughput compared to traditional blockchain structures. Similarities to Bitcoin: Decentralization and Security: Just like Bitcoin, Kaspa uses a Proof-of-Work (PoW) consensus mechanism, ensuring that the network remains decentralized and secure. Kaspa's approach, however, does not orphan blocks created in parallel but instead orders them within the consensus, maintaining a high degree of security without sacrificing speed. No Premine or ICO: Kaspa was launched with a fair distribution method, eschewing pre-mines or initial coin offerings, mimicking Bitcoin's launch ethos to ensure a level playing field for all participants from the outset. Scarcity: Similar to Bitcoin, Kaspa has a capped supply, which encourages a deflationary model. This aspect could potentially make Kaspa an attractive store of value, akin to Bitcoin's role as "digital gold," although Kaspa brands itself more as "digital silver" due to its focus on speed and utility. Advancements Beyond Bitcoin: Scalability: Where Bitcoin struggles with scalability due to its block size limit, Kaspa's BlockDAG structure enables it to process transactions at a scale that Bitcoin cannot match without significant network congestion or fee hikes. This makes Kaspa potentially more suitable for everyday transactions. Transaction Confirmation Time: Kaspa transactions are confirmed in about 10 seconds, compared to Bitcoin's minimum of 10 minutes, making it highly efficient for real-world applications where speed is crucial. Energy Efficiency: By design, Kaspa aims to be less energy-intensive than Bitcoin while maintaining PoW security, leveraging algorithms like kHeavyHash which are more suited to the high-throughput environment of a DAG. Future Adaptability: Kaspa plans to introduce features like smart contracts with very low fees, potentially positioning it as a platform for decentralized applications (dApps) without the scaling issues faced by other networks like Ethereum. The Road Ahead: Kaspa is not just another cryptocurrency; it's a project that challenges the established norms of blockchain scalability. By solving the blockchain trilemma of security, scalability, and decentralization, Kaspa could set a new standard for what's possible in cryptocurrency. However, its relatively new status in the market means it has yet to prove its resilience over decades like Bitcoin. The community's enthusiasm, combined with ongoing development like the Rust rewrite for better performance, suggests Kaspa is on a path to potentially significant growth and adoption. In conclusion, while Kaspa shares foundational similarities with Bitcoin, its advancements in speed, scalability, and adaptability make it a compelling alternative or companion in the crypto ecosystem. Its testnet achievements are just the beginning of what could be a transformative journey in the blockchain space.
5:10

This is another piece about #kaspa and why you should accumulate as much as possible. I think people don't realize yet the tangible progress that $kas had from the launch of the project. And who complains about the chart going sideways for a few months, that is called redistribution after an amazing run during the bear market. Enjoy. Kaspa: The Blockchain Speed Demon Redefining Crypto Efficiency Kaspa has emerged as a significant player in the cryptocurrency landscape, particularly noted for its advancements in transaction speed and scalability while maintaining the foundational principles of Bitcoin (BTC). Here's an insightful look into Kaspa's achievements and its relation to Bitcoin: Unprecedented Speed on Testnet: Kaspa's testnet has demonstrated remarkable speed, achieving up to 10 blocks per second (BPS). This is a monumental leap when compared to Bitcoin, which processes about one block every 10 minutes. This speed was showcased in Kaspa's Testnet 11, where the network's peer-to-peer dynamics and node health remained stable, indicating the robustness of its design. This capability is facilitated by Kaspa's unique implementation of the GHOSTDAG protocol, which allows for parallel processing of blocks, significantly enhancing throughput compared to traditional blockchain structures. Similarities to Bitcoin: Decentralization and Security: Just like Bitcoin, Kaspa uses a Proof-of-Work (PoW) consensus mechanism, ensuring that the network remains decentralized and secure. Kaspa's approach, however, does not orphan blocks created in parallel but instead orders them within the consensus, maintaining a high degree of security without sacrificing speed. No Premine or ICO: Kaspa was launched with a fair distribution method, eschewing pre-mines or initial coin offerings, mimicking Bitcoin's launch ethos to ensure a level playing field for all participants from the outset. Scarcity: Similar to Bitcoin, Kaspa has a capped supply, which encourages a deflationary model. This aspect could potentially make Kaspa an attractive store of value, akin to Bitcoin's role as "digital gold," although Kaspa brands itself more as "digital silver" due to its focus on speed and utility. Advancements Beyond Bitcoin: Scalability: Where Bitcoin struggles with scalability due to its block size limit, Kaspa's BlockDAG structure enables it to process transactions at a scale that Bitcoin cannot match without significant network congestion or fee hikes. This makes Kaspa potentially more suitable for everyday transactions. Transaction Confirmation Time: Kaspa transactions are confirmed in about 10 seconds, compared to Bitcoin's minimum of 10 minutes, making it highly efficient for real-world applications where speed is crucial. Energy Efficiency: By design, Kaspa aims to be less energy-intensive than Bitcoin while maintaining PoW security, leveraging algorithms like kHeavyHash which are more suited to the high-throughput environment of a DAG. Future Adaptability: Kaspa plans to introduce features like smart contracts with very low fees, potentially positioning it as a platform for decentralized applications (dApps) without the scaling issues faced by other networks like Ethereum. The Road Ahead: Kaspa is not just another cryptocurrency; it's a project that challenges the established norms of blockchain scalability. By solving the blockchain trilemma of security, scalability, and decentralization, Kaspa could set a new standard for what's possible in cryptocurrency. However, its relatively new status in the market means it has yet to prove its resilience over decades like Bitcoin. The community's enthusiasm, combined with ongoing development like the Rust rewrite for better performance, suggests Kaspa is on a path to potentially significant growth and adoption. In conclusion, while Kaspa shares foundational similarities with Bitcoin, its advancements in speed, scalability, and adaptability make it a compelling alternative or companion in the crypto ecosystem. Its testnet achievements are just the beginning of what could be a transformative journey in the blockchain space.

Satoshi Vibz

11,501 Aufrufe • vor 1 Jahr

Hermes + Claude + Higgsfield MCP + ViralBuilder = 💰💰💰 Four tools. One prompt chain. Hook to finished video in 10 minutes. I built a Claude skill that writes shot-by-shot Higgsfield prompts from a single creative brief. ViralBuilder tells you what's winning. The skill turns it into a production-ready prompt. Higgsfield renders it. No creative director. No guessing. No separate tools. Here is the setup: Higgsfield MCP → Open Claude Code → Settings → Connectors → Enter: → Connect your account Hermes → The agent layer running underneath Claude Code → It holds your skills, crons, memory, and routing rules → When you prompt Claude, Hermes feeds it the context it needs ViralBuilder (like Gethookd) → The winning ecom video database → Scrapes top performing ecom videos across platforms → Claude reads the data and extracts what styles, hooks, and formats are actually scaling The skill: video-prompt-builder → Installed inside Claude via Hermes → Takes a creative brief and outputs a full shot-by-shot prompt → Covers camera work, effects, transitions, pacing, and energy arc → Every output is structured for Higgsfield to render without ambiguity No switching apps. No export steps. Everything runs from one place. ▸ FIND WINNING CREATIVE ANGLES ViralBuilder tells you what the market already validated. Claude reads it and extracts the pattern. Prompts to run: "Search ViralBuilder for the top performing ecom videos in [niche] over the last 21 days. Extract the 3 dominant hook styles and rank by view velocity." "Pull the winning video formats in [niche] from ViralBuilder. Which opening 3 seconds appears most across videos spending over $10k?" "Find what video style is scaling right now in [niche] for the US market. UGC, talking head, or product demo. Filter for videos with over 1M views." "Pull the last 30 days of viral ecom hooks in [niche] from ViralBuilder. Cluster by emotional trigger. Which cluster has the most longevity?" You are not guessing at angles. You are reading what the market already spent money validating. ▸ BUILD THE PROMPT WITH THE SKILL This is where the video-prompt-builder skill takes over. You give Claude the winning angle. The skill outputs a complete shot-by-shot prompt with effects, transitions, pacing, and energy arc ready to fire into Higgsfield. Prompts to run: "Use the video-prompt-builder skill. Brief: 15-second UGC ad for [product] in [niche]. Hook style: [style from ViralBuilder]. Tone: direct to camera, US English. Output the full shot-by-shot effects timeline, effects inventory, density map, and energy arc." "Use the video-prompt-builder skill. The dominant hook in [niche] this week is [hook]. Build a 10-second product video prompt that opens with a speed ramp into a close-up product reveal. Include a signature visual effect and a low-density CTA landing." "Use the video-prompt-builder skill. Brief: replicate the pacing and energy of a [style description] video for [product]. Target duration: 20 seconds. Output all four sections. Then generate the video with Higgsfield using the shot-by-shot prompt." The skill outputs four sections every time: → Shot-by-shot effects timeline with camera, movement, and transitions per shot → Master effects inventory showing every technique used and where → Effects density map showing high, medium, and low intensity across the timeline → Energy arc describing how the video opens, builds, and lands That output goes directly into Higgsfield. No rewriting. No translating. ▸ GENERATE THE CREATIVE Claude writes the brief via the skill. Higgsfield MCP builds the video. Both happen in the same session. Prompts to run: "Use the video-prompt-builder skill to write a 15-second UGC prompt for [product]. Hook in the first 3 seconds, speed ramp into product reveal, slow-motion CTA landing. Then generate with Higgsfield in 9:16 format." "Build 3 prompt variations on this winning angle: [angle]. Each variation opens with a different effect — speed ramp, digital zoom, whip pan. Use the video-prompt-builder skill for each. Then generate all three with Higgsfield." "Use the video-prompt-builder skill. Brief: problem-solution ad for [product], 20 seconds, US market. Problem shot at high density, product reveal at medium, result and CTA at low. Generate with Higgsfield in 9:16." No separate tool. No file transfer. The video comes back in the same thread. ▸ CHAIN THE WHOLE STACK One prompt. All four tools firing together. "You are my ad creative director. Hermes has loaded my brand context. Pull the top performing video style in [niche] from ViralBuilder this week. Use the video-prompt-builder skill to write a full shot-by-shot prompt for [product] that replicates that style — 20 seconds, 9:16, US market, hook in the first 3 seconds. Output the effects timeline, inventory, density map, and energy arc. Then generate the video with Higgsfield." That single prompt replaces a half-day of production. The math before this stack: Brief: 30 minutes Script: 1 hour Creative production: 2 to 3 hours Agency or freelancer cost: $500 to $2,000 per creative With this stack: Hook to finished creative: 10 minutes Cost per creative: tool subscription, a fraction of agency rate 5 product tests in the time it used to take to brief one Bad product tests are where US ad budget disappears. $600 to $1,500 per failed test, before you even know if the angle works. This stack shows you what the market already validated before you spend a dollar on production. Hermes = your context layer. Brand, goals, past performance. Claude is always informed. ViralBuilder = your winning video database. See exactly what styles, hooks, and formats are scaling before you produce anything. video-prompt-builder skill = the translation layer. Turns a creative brief into a structured, production-ready Higgsfield prompt every time. Claude = the brain. Reads the market, writes the brief, chains the tools. Higgsfield MCP = the output. Video generated directly from the prompt. No export step. Four tools. One session. 10 minutes. Comment + RT "STACK" and I'll DM you the full workflow + the video-prompt-builder skill file.
0:26

Hermes + Claude + Higgsfield MCP + ViralBuilder = 💰💰💰 Four tools. One prompt chain. Hook to finished video in 10 minutes. I built a Claude skill that writes shot-by-shot Higgsfield prompts from a single creative brief. ViralBuilder tells you what's winning. The skill turns it into a production-ready prompt. Higgsfield renders it. No creative director. No guessing. No separate tools. Here is the setup: Higgsfield MCP → Open Claude Code → Settings → Connectors → Enter: → Connect your account Hermes → The agent layer running underneath Claude Code → It holds your skills, crons, memory, and routing rules → When you prompt Claude, Hermes feeds it the context it needs ViralBuilder (like Gethookd) → The winning ecom video database → Scrapes top performing ecom videos across platforms → Claude reads the data and extracts what styles, hooks, and formats are actually scaling The skill: video-prompt-builder → Installed inside Claude via Hermes → Takes a creative brief and outputs a full shot-by-shot prompt → Covers camera work, effects, transitions, pacing, and energy arc → Every output is structured for Higgsfield to render without ambiguity No switching apps. No export steps. Everything runs from one place. ▸ FIND WINNING CREATIVE ANGLES ViralBuilder tells you what the market already validated. Claude reads it and extracts the pattern. Prompts to run: "Search ViralBuilder for the top performing ecom videos in [niche] over the last 21 days. Extract the 3 dominant hook styles and rank by view velocity." "Pull the winning video formats in [niche] from ViralBuilder. Which opening 3 seconds appears most across videos spending over $10k?" "Find what video style is scaling right now in [niche] for the US market. UGC, talking head, or product demo. Filter for videos with over 1M views." "Pull the last 30 days of viral ecom hooks in [niche] from ViralBuilder. Cluster by emotional trigger. Which cluster has the most longevity?" You are not guessing at angles. You are reading what the market already spent money validating. ▸ BUILD THE PROMPT WITH THE SKILL This is where the video-prompt-builder skill takes over. You give Claude the winning angle. The skill outputs a complete shot-by-shot prompt with effects, transitions, pacing, and energy arc ready to fire into Higgsfield. Prompts to run: "Use the video-prompt-builder skill. Brief: 15-second UGC ad for [product] in [niche]. Hook style: [style from ViralBuilder]. Tone: direct to camera, US English. Output the full shot-by-shot effects timeline, effects inventory, density map, and energy arc." "Use the video-prompt-builder skill. The dominant hook in [niche] this week is [hook]. Build a 10-second product video prompt that opens with a speed ramp into a close-up product reveal. Include a signature visual effect and a low-density CTA landing." "Use the video-prompt-builder skill. Brief: replicate the pacing and energy of a [style description] video for [product]. Target duration: 20 seconds. Output all four sections. Then generate the video with Higgsfield using the shot-by-shot prompt." The skill outputs four sections every time: → Shot-by-shot effects timeline with camera, movement, and transitions per shot → Master effects inventory showing every technique used and where → Effects density map showing high, medium, and low intensity across the timeline → Energy arc describing how the video opens, builds, and lands That output goes directly into Higgsfield. No rewriting. No translating. ▸ GENERATE THE CREATIVE Claude writes the brief via the skill. Higgsfield MCP builds the video. Both happen in the same session. Prompts to run: "Use the video-prompt-builder skill to write a 15-second UGC prompt for [product]. Hook in the first 3 seconds, speed ramp into product reveal, slow-motion CTA landing. Then generate with Higgsfield in 9:16 format." "Build 3 prompt variations on this winning angle: [angle]. Each variation opens with a different effect — speed ramp, digital zoom, whip pan. Use the video-prompt-builder skill for each. Then generate all three with Higgsfield." "Use the video-prompt-builder skill. Brief: problem-solution ad for [product], 20 seconds, US market. Problem shot at high density, product reveal at medium, result and CTA at low. Generate with Higgsfield in 9:16." No separate tool. No file transfer. The video comes back in the same thread. ▸ CHAIN THE WHOLE STACK One prompt. All four tools firing together. "You are my ad creative director. Hermes has loaded my brand context. Pull the top performing video style in [niche] from ViralBuilder this week. Use the video-prompt-builder skill to write a full shot-by-shot prompt for [product] that replicates that style — 20 seconds, 9:16, US market, hook in the first 3 seconds. Output the effects timeline, inventory, density map, and energy arc. Then generate the video with Higgsfield." That single prompt replaces a half-day of production. The math before this stack: Brief: 30 minutes Script: 1 hour Creative production: 2 to 3 hours Agency or freelancer cost: $500 to $2,000 per creative With this stack: Hook to finished creative: 10 minutes Cost per creative: tool subscription, a fraction of agency rate 5 product tests in the time it used to take to brief one Bad product tests are where US ad budget disappears. $600 to $1,500 per failed test, before you even know if the angle works. This stack shows you what the market already validated before you spend a dollar on production. Hermes = your context layer. Brand, goals, past performance. Claude is always informed. ViralBuilder = your winning video database. See exactly what styles, hooks, and formats are scaling before you produce anything. video-prompt-builder skill = the translation layer. Turns a creative brief into a structured, production-ready Higgsfield prompt every time. Claude = the brain. Reads the market, writes the brief, chains the tools. Higgsfield MCP = the output. Video generated directly from the prompt. No export step. Four tools. One session. 10 minutes. Comment + RT "STACK" and I'll DM you the full workflow + the video-prompt-builder skill file.

Kid Pak

58,113 Aufrufe • vor 3 Monaten

Thought experiment for people regarding the concept of Absolute Time. Absolute means NO EXCEPTIONS. We are NOT looking back in time when we see galaxies and stars. We are NOT looking back in time 1.25 seconds when we see the moon. We're Not looking back in time 3 minutes when we see Mars. We're Not looking back in time 8.33 minutes when we see the Sun. If an astronaut lit a matchstick on Mars, the distant observer would see predator heat waves at the top of the matchstick in real-time while the matchstick started to blacken towards the astronaut's fingers. But there would be no orange light from that chemical reaction or flame seen. If the matchstick burnt out before the packet of orange light from that particular chemical reaction made it to Earth… then the distant observer would just see a disembodied orange flash of light with a lag. But the Earth-bound observer would never actually see the flame associated with the orange wavelength it put out. The wavelength of color emitted by the flame is not a recording of reality. If the orange light is 650 Thz, that means there are 650 trillion individual and separate bursts of orange light pulsating in 1 second. NOT that "the same light" is "waving" 650 trillion times a second and that light is a recording of reality. There are 650 trillion brand-new lights flashing in 1 second. Each Hertz is a brand-new emission and packet unto itself. Time does not re-emit 650 trillion times a second, nor is a photon a particle or a packet of reality acting like the frame of a reel of footage. The orange light that already left the flame will continue to propagate out until it meets the electrons making up the distant observer. But remember, it is never the same light within that packet. And the electrons making up the observer will absorb all of those different lights within that packet and re-emit brand-new lights that produce the product of illumination. A photon is a massless packet of energy, spherically expanding at the rate of c from the source it comes from. Illumination is the result of that energy being absorbed and RE-emitted by any other electrons that did not output that primary packet. But time is not associated with the same light. It's never the same light and time does not re-emit between packets. Time is not relative. Do NOT allow your mind Carte Blanche to think along the lines of relative time. DROP IT for this thought experiment. We are thinking along the lines of ABSOLUTE TIME/ Galilean VARIANCE. What does absolute time mean? It means time is constant in ALL frames of reference. Any frequency shifts between atomic clocks IS a literal change in the speed of light. But relativity forbids the speed of light from Ever changing, so relativity (Lorentz INVARIANCE) invented the concept of the 4th dimension and space-time. Because Relativity doesn't allow light speed to shift.. they interpret the same frequency shift between atomic clocks as being conclusive, irrefutable evidence that time and reality itself shifts. Rather than say it's just that ONE clock being affected by Earth's gravity and the oscillation of that ONE cesium clock is being altered compared to other clocks. People don't realize that in relativity... time dilation is SYMMETRICAL! Not even most relativists know their own theory. If Clock A and Clock B are synchronized and together... and then they accelerate apart... Einstein said Clock A would see Clock B as being slower itself. And Clock B would view Clock A as slower than ITself. NOT that only one observer would see back in time and the other would see forward or not at all. No... BOTH observers are supposed to see each other BACK in time relative to each other according to Einstein and the consequence of the math. It doesn't make ANY sense!! But relativists toss that part of time dilation under their 4th dimensional rug. The rug is woven from threads of gold that only the smart people can see apparently. So... here's a thought experiment/ Gedankenexperiment for absolute time. NO paradoxes... no nonsense or confusion. What do you see in a club? You see disco lights changing and a color wheel effect. You see everyone in REAL-TIME. Not just because they are so close to the lights. Light is not a recording of reality. Light is simply color. Illuminating reality in a certain color. Just because you can't see something yet or the color hasn't reached you doesn't mean it isn't happening in real-time. Zoom out and look at the people in the club through binoculars a mile away. You are still looking at them in real-time. The colors are shifting with a delay in the club. Now look at the club through a telescope from the surface of the moon. You're still looking at the people in real-time. But now there is a lag of the colors shifting by 1.25 seconds because it takes light 1.25 seconds to travel from the Earth to the moon. Now look at the club through an even bigger telescope from the surface of Mars. You're still looking at the people in real-time, but now there is a lag of the colors shifting by 3 minutes because it takes light 3 minutes to travel from Earth to Mars. fr you are a third hypothetical observer zoomed out and watching the person from Mars AND seeing the club on Earth... you're still seeing everything happening in real-time as well. But you see the colored wavepackets traveling with a delay to the observer on Mars. And the disco color wheel effect is just lagging before it affects the observer from Mars and the observer on the Moon. It doesn't matter how far you zoom out! There is only now to observe. But there WILL be a lag and delay for a given color/wavepacket to reach distant observers. But all points in space are already illuminated by other starlight. So if you're too far away... you'll just see the club in real-time but without any disco lights. Just see them in white light because that's the source already illuminating the scene. This is where it gets the most difficult because people think light itself is a recording of reality that replays a scene from where it came from. But another punch in the gut of relativity is that in order to see REFLECTED light... that would require a TWO-WAY transit. Which means the light would have to be sent out... record the scene of a distant event and then RETURN in order to REplay the event. Which means it would take 6 minutes to see the club from Mars by that logic and 2.5 seconds to see the club from the moon by that logic. The difference in tick rates between clocks has NOTHING to do with time dilation. Wait.. what?! How can that be? Because a clock itself doesn't represent all of time and reality. The difference between clocks is a "Transverse relative time shift." If the only light in the universe was from the lighter… the only way a distant observer would be able to see the astronaut on Mars is if the astronaut held down the button of the lighter for longer than 3 minutes. It takes 3 minutes for the packet of light to travel from Mars to Earth. The distant observer would never be able to see Mars, unless the light stretched from Mars all the way to Earth, and illuminated the path between Mars and Earth. And that would take 3 minutes for the boundary and first part of that wave packet to reach Earth. But if the distant observer wanted to observe Mars in real-time… then that packet of light would have to be on for longer than 3 minutes. So if the astronaut on Mars flicked the lighter at 12:00, the distant observer on Earth wouldn't see anything until 12:03. If the light was on for 3 minutes and 10 seconds, and the distant observer is 3 light minutes away... the distant observer would be able to see Mars in real-time for 10 seconds starting at 12:03. In the 20 second video clip of the rotating planet with shifting colors... just imagine you're a couple light minutes or light seconds away. You're still seeing the planet spin in real-time. But there is simply a delay of switching colors. You are Not looking back in time. It's just a color wheel effect from a great distance away. That's it!! There are many major flaws which tarnish people's critical thinking on this thought experiment. 1. Light does NOT ricochet or bounce. Electrons absorb, emit and re-emit ALL electromagnetic radiation. The electrons, making up the glass of a mirror will absorb the incoming light and re-emit a brand-new light as an equal and opposite reaction. NOT that "the same light" bounced off the mirror and continued on within the same frame of reference.  2. Light is NOT a recording of reality. 3. It is NOT the same light being observed from a source. It's never the same light. Each Hertz is a new light. Think of half of a sine wave as being its own emission. On an oscilloscope, a stimulus generates a peak which initiates an equal and opposite trough. Or vice versa. That repeating process is not "the same light." If you cut and paste that sine wave to another sine wave, the boundary between the waves will always be in phase. (thus refraction) 4. The speed of light is NOT the same in ALL frames of reference, no matter what. 5. Light is NOT made of particles and waves that flip back-and-forth. 6. Time is NOT connected to the speed of light. Time remains constant regardless if you accelerate towards or away from a clock. The clocks themselves will indeed be off! But that's an affect on the electrons making up the atomic clock affecting the oscillation of the isotope which is ASSUMED to ALWAYS be the same. So ANY difference in oscillation is treated as a literal distortion in space-time. 7. Space and time are not linked at all. That is a mathematical artifice under Lorentz invariance. Time is relative under Lorentz invariance. But time is absolute under Galilean VARIANCE. When people hear or see the word GALILEAN... their brains switch to auto pilot to "aether theory" and "classical physics." What people don't realize is that aether theory used Galilean INVARIANCE. Rather than space-time being used as an excuse to explain the difference in frequencies between atomic clocks... it was originally aether being used as an excuse to keep the speed of light the same. But None of those things are valid! We are thinking under the framework of Galilean VARIANCE! Completely new revolutionary model returning to Isaac Newton and Classical physics but without the corpuscular theory (particle) theory for light... without a particle-wave duality... without an aether... without a 4th dimension. Just good ol elementary math within 3D Euclidean space. Everything happening in real-time, right now. This reformulation of Galilean transformations was offered by Dr. Edward Dowdye in 1991 called The Extinction Shift Principle. Effectivity as opposed to Relativity. If light required a two-way transit, in order to travel out… Record an event, and travel back to replay the recording…  then it would take 6 minutes to see the astronaut on Mars instead of 3.  Remember… They say the SAME light is a recording, and must travel there and travel back in order to REplay. Relativity says time is relative: t' ≠ t time is NOT the same from all frames of reference) and t = tₒ / √1 - v²/c² but Galilean Variance says time is not relative: t' = t (Time IS the same from all frames of reference) and τ_tr = τₒ / √1 - v²/c² Relativity says c' = c (The velocity of light is the same from all frames of reference) but Galilean variance says c' ≠ c (The velocity of light is NOT the same from all frames of reference) and that c' = c ± v (The velocity of light in one frame of reference is dependent upon the velocity of the light source relative to an observer in another frame of reference. Whether that light source is approaching or receding away from that observer) Relativity says E = mc² (Energy and mass are universally equivalent and literally interchangeable under All conditions.) but Galilean variance says E = Δmc² = mₒc² (Energy changes in a system are the result from changes in mass. mₒ represents the original mass. Mass and energy do not literally interchange. There is an equivalence, not an interchange.) The Rebirth of Classical Physics: Time, Light & Gravity Star light and illumination: Flicking a Lighter on Mars visual example:
0:21

Thought experiment for people regarding the concept of Absolute Time. Absolute means NO EXCEPTIONS. We are NOT looking back in time when we see galaxies and stars. We are NOT looking back in time 1.25 seconds when we see the moon. We're Not looking back in time 3 minutes when we see Mars. We're Not looking back in time 8.33 minutes when we see the Sun. If an astronaut lit a matchstick on Mars, the distant observer would see predator heat waves at the top of the matchstick in real-time while the matchstick started to blacken towards the astronaut's fingers. But there would be no orange light from that chemical reaction or flame seen. If the matchstick burnt out before the packet of orange light from that particular chemical reaction made it to Earth… then the distant observer would just see a disembodied orange flash of light with a lag. But the Earth-bound observer would never actually see the flame associated with the orange wavelength it put out. The wavelength of color emitted by the flame is not a recording of reality. If the orange light is 650 Thz, that means there are 650 trillion individual and separate bursts of orange light pulsating in 1 second. NOT that "the same light" is "waving" 650 trillion times a second and that light is a recording of reality. There are 650 trillion brand-new lights flashing in 1 second. Each Hertz is a brand-new emission and packet unto itself. Time does not re-emit 650 trillion times a second, nor is a photon a particle or a packet of reality acting like the frame of a reel of footage. The orange light that already left the flame will continue to propagate out until it meets the electrons making up the distant observer. But remember, it is never the same light within that packet. And the electrons making up the observer will absorb all of those different lights within that packet and re-emit brand-new lights that produce the product of illumination. A photon is a massless packet of energy, spherically expanding at the rate of c from the source it comes from. Illumination is the result of that energy being absorbed and RE-emitted by any other electrons that did not output that primary packet. But time is not associated with the same light. It's never the same light and time does not re-emit between packets. Time is not relative. Do NOT allow your mind Carte Blanche to think along the lines of relative time. DROP IT for this thought experiment. We are thinking along the lines of ABSOLUTE TIME/ Galilean VARIANCE. What does absolute time mean? It means time is constant in ALL frames of reference. Any frequency shifts between atomic clocks IS a literal change in the speed of light. But relativity forbids the speed of light from Ever changing, so relativity (Lorentz INVARIANCE) invented the concept of the 4th dimension and space-time. Because Relativity doesn't allow light speed to shift.. they interpret the same frequency shift between atomic clocks as being conclusive, irrefutable evidence that time and reality itself shifts. Rather than say it's just that ONE clock being affected by Earth's gravity and the oscillation of that ONE cesium clock is being altered compared to other clocks. People don't realize that in relativity... time dilation is SYMMETRICAL! Not even most relativists know their own theory. If Clock A and Clock B are synchronized and together... and then they accelerate apart... Einstein said Clock A would see Clock B as being slower itself. And Clock B would view Clock A as slower than ITself. NOT that only one observer would see back in time and the other would see forward or not at all. No... BOTH observers are supposed to see each other BACK in time relative to each other according to Einstein and the consequence of the math. It doesn't make ANY sense!! But relativists toss that part of time dilation under their 4th dimensional rug. The rug is woven from threads of gold that only the smart people can see apparently. So... here's a thought experiment/ Gedankenexperiment for absolute time. NO paradoxes... no nonsense or confusion. What do you see in a club? You see disco lights changing and a color wheel effect. You see everyone in REAL-TIME. Not just because they are so close to the lights. Light is not a recording of reality. Light is simply color. Illuminating reality in a certain color. Just because you can't see something yet or the color hasn't reached you doesn't mean it isn't happening in real-time. Zoom out and look at the people in the club through binoculars a mile away. You are still looking at them in real-time. The colors are shifting with a delay in the club. Now look at the club through a telescope from the surface of the moon. You're still looking at the people in real-time. But now there is a lag of the colors shifting by 1.25 seconds because it takes light 1.25 seconds to travel from the Earth to the moon. Now look at the club through an even bigger telescope from the surface of Mars. You're still looking at the people in real-time, but now there is a lag of the colors shifting by 3 minutes because it takes light 3 minutes to travel from Earth to Mars. fr you are a third hypothetical observer zoomed out and watching the person from Mars AND seeing the club on Earth... you're still seeing everything happening in real-time as well. But you see the colored wavepackets traveling with a delay to the observer on Mars. And the disco color wheel effect is just lagging before it affects the observer from Mars and the observer on the Moon. It doesn't matter how far you zoom out! There is only now to observe. But there WILL be a lag and delay for a given color/wavepacket to reach distant observers. But all points in space are already illuminated by other starlight. So if you're too far away... you'll just see the club in real-time but without any disco lights. Just see them in white light because that's the source already illuminating the scene. This is where it gets the most difficult because people think light itself is a recording of reality that replays a scene from where it came from. But another punch in the gut of relativity is that in order to see REFLECTED light... that would require a TWO-WAY transit. Which means the light would have to be sent out... record the scene of a distant event and then RETURN in order to REplay the event. Which means it would take 6 minutes to see the club from Mars by that logic and 2.5 seconds to see the club from the moon by that logic. The difference in tick rates between clocks has NOTHING to do with time dilation. Wait.. what?! How can that be? Because a clock itself doesn't represent all of time and reality. The difference between clocks is a "Transverse relative time shift." If the only light in the universe was from the lighter… the only way a distant observer would be able to see the astronaut on Mars is if the astronaut held down the button of the lighter for longer than 3 minutes. It takes 3 minutes for the packet of light to travel from Mars to Earth. The distant observer would never be able to see Mars, unless the light stretched from Mars all the way to Earth, and illuminated the path between Mars and Earth. And that would take 3 minutes for the boundary and first part of that wave packet to reach Earth. But if the distant observer wanted to observe Mars in real-time… then that packet of light would have to be on for longer than 3 minutes. So if the astronaut on Mars flicked the lighter at 12:00, the distant observer on Earth wouldn't see anything until 12:03. If the light was on for 3 minutes and 10 seconds, and the distant observer is 3 light minutes away... the distant observer would be able to see Mars in real-time for 10 seconds starting at 12:03. In the 20 second video clip of the rotating planet with shifting colors... just imagine you're a couple light minutes or light seconds away. You're still seeing the planet spin in real-time. But there is simply a delay of switching colors. You are Not looking back in time. It's just a color wheel effect from a great distance away. That's it!! There are many major flaws which tarnish people's critical thinking on this thought experiment. 1. Light does NOT ricochet or bounce. Electrons absorb, emit and re-emit ALL electromagnetic radiation. The electrons, making up the glass of a mirror will absorb the incoming light and re-emit a brand-new light as an equal and opposite reaction. NOT that "the same light" bounced off the mirror and continued on within the same frame of reference.  2. Light is NOT a recording of reality. 3. It is NOT the same light being observed from a source. It's never the same light. Each Hertz is a new light. Think of half of a sine wave as being its own emission. On an oscilloscope, a stimulus generates a peak which initiates an equal and opposite trough. Or vice versa. That repeating process is not "the same light." If you cut and paste that sine wave to another sine wave, the boundary between the waves will always be in phase. (thus refraction) 4. The speed of light is NOT the same in ALL frames of reference, no matter what. 5. Light is NOT made of particles and waves that flip back-and-forth. 6. Time is NOT connected to the speed of light. Time remains constant regardless if you accelerate towards or away from a clock. The clocks themselves will indeed be off! But that's an affect on the electrons making up the atomic clock affecting the oscillation of the isotope which is ASSUMED to ALWAYS be the same. So ANY difference in oscillation is treated as a literal distortion in space-time. 7. Space and time are not linked at all. That is a mathematical artifice under Lorentz invariance. Time is relative under Lorentz invariance. But time is absolute under Galilean VARIANCE. When people hear or see the word GALILEAN... their brains switch to auto pilot to "aether theory" and "classical physics." What people don't realize is that aether theory used Galilean INVARIANCE. Rather than space-time being used as an excuse to explain the difference in frequencies between atomic clocks... it was originally aether being used as an excuse to keep the speed of light the same. But None of those things are valid! We are thinking under the framework of Galilean VARIANCE! Completely new revolutionary model returning to Isaac Newton and Classical physics but without the corpuscular theory (particle) theory for light... without a particle-wave duality... without an aether... without a 4th dimension. Just good ol elementary math within 3D Euclidean space. Everything happening in real-time, right now. This reformulation of Galilean transformations was offered by Dr. Edward Dowdye in 1991 called The Extinction Shift Principle. Effectivity as opposed to Relativity. If light required a two-way transit, in order to travel out… Record an event, and travel back to replay the recording…  then it would take 6 minutes to see the astronaut on Mars instead of 3.  Remember… They say the SAME light is a recording, and must travel there and travel back in order to REplay. Relativity says time is relative: t' ≠ t time is NOT the same from all frames of reference) and t = tₒ / √1 - v²/c² but Galilean Variance says time is not relative: t' = t (Time IS the same from all frames of reference) and τ_tr = τₒ / √1 - v²/c² Relativity says c' = c (The velocity of light is the same from all frames of reference) but Galilean variance says c' ≠ c (The velocity of light is NOT the same from all frames of reference) and that c' = c ± v (The velocity of light in one frame of reference is dependent upon the velocity of the light source relative to an observer in another frame of reference. Whether that light source is approaching or receding away from that observer) Relativity says E = mc² (Energy and mass are universally equivalent and literally interchangeable under All conditions.) but Galilean variance says E = Δmc² = mₒc² (Energy changes in a system are the result from changes in mass. mₒ represents the original mass. Mass and energy do not literally interchange. There is an equivalence, not an interchange.) The Rebirth of Classical Physics: Time, Light & Gravity Star light and illumination: Flicking a Lighter on Mars visual example:

TheRealVerbz (Jason Verbelli)

16,608 Aufrufe • vor 1 Jahr

Scientists discover surprising link between gut-brain interactions and mental health | Eric W. Dolan, PsyPost A new study provides evidence that the connection between the brain and the stomach may be linked to mental health in a measurable way. Researchers from Aarhus University in Denmark, publishing their work in Nature Mental Health, report that a specific pattern of communication between the brain and the stomach reflects how individuals feel emotionally and psychologically. Their findings suggest that these gut-brain interactions can indicate a person’s levels of anxiety, depression, well-being, and overall quality of life. The idea that emotions are linked to physical sensations in the gut is widely reflected in language. People often talk about having “butterflies in the stomach” when nervous, or feeling “sick to the stomach” when distressed. Yet, despite these common expressions, most scientific attention in the field of brain-body interaction has focused on other organs, such as the heart and lungs. These areas have long been studied for their roles in emotion and mood. The researchers were struck by how little was known about how the stomach, in particular, interacts with the brain. While recent studies have explored the influence of gut bacteria and digestion on mental health, very little work had been done on the electrical rhythms of the stomach and how they may directly communicate with the brain’s networks involved in emotion, attention, and cognition. The team behind this new study wanted to explore whether a person’s psychological profile might be reflected in how strongly the stomach and brain are coupled during rest. Their aim was not to link a specific diagnosis like depression to a single brain region, but rather to identify patterns across a broad spectrum of mental health experiences. “Our interest grew from the long-standing discussion about the role of the body in shaping emotion, a question that has fascinated philosophers and scientists for centuries,” said study author Leah Banellis (Leah Banellis), a postdoctoral fellow in Cognitive Neuroscience at Aarhus University. “Yet, while the heart and lungs have received much attention, the stomach has been largely overlooked. This gap struck us as especially surprising, because the link between the stomach and emotional experience feels so intuitive. It is heavily reflected in everyday language, with phrases like ‘butterflies in the stomach,’ ‘sick to our stomach,’ or ‘trust your gut.'” The research was part of the Visceral Mind Project, a large-scale initiative that combines data on brain activity, bodily rhythms, and psychological assessments. The team recorded data from 243 people using a method that captures both electrical signals from the stomach (electrogastrography) and brain activity measured with functional magnetic resonance imaging (fMRI). The participants represented a wide range of mental health profiles, from those reporting high well-being to others showing signs of distress, including anxiety, depression, fatigue, and insomnia. To capture this diversity, the researchers didn’t exclude people with psychiatric symptoms or diagnoses. Instead, they aimed for variation, which would allow their models to detect patterns across the mental health spectrum. Each participant underwent a series of recordings while lying still in the MRI scanner. At the same time, sensors on the abdomen captured the stomach’s slow electrical rhythm, which cycles about three times per minute. This rhythm, which originates from specialized cells in the stomach lining, is typically involved in coordinating digestion. But the researchers suspected it might also be linked to mental state. To analyze the relationship between stomach and brain activity, the team used a method that looks at how well the two rhythms align over time. This measure, known as phase-locking value, essentially captures the degree of synchronization between stomach signals and brain signals across different regions. The researchers then combined this data with results from a comprehensive mental health questionnaire. The battery included 37 different scores across a range of domains—such as anxiety, stress, mood, fatigue, attention, sleep quality, and life satisfaction. Using a statistical method known as canonical correlation analysis, they looked for patterns that linked brain-stomach coupling with the participants’ mental health profiles. The analysis revealed a clear and statistically significant pattern. Stronger coupling between the stomach’s rhythm and brain activity was associated with poorer mental health. Individuals who reported more symptoms of anxiety, depression, stress, and fatigue tended to show increased synchronization between their stomach and brain rhythms. In contrast, those with higher levels of well-being and life satisfaction showed weaker coupling. “For the first time, we’ve found a scientific link between your ‘gut feelings’ and your mental health, showing a surprising connection between your stomach’s natural rhythm and your brain,” Banellis told PsyPost. “Specifically, our study revealed that stronger communication between the stomach and brain is linked to worse mental health, such as higher symptoms of anxiety, depression, stress, and fatigue, whereas weaker stomach-brain communication aligns with better mental health reflected in higher overall well-being and quality of life.” This stomach-brain signature was not random. It was localized in specific brain networks, particularly those involved in attention, cognitive control, and salience detection. Some of the strongest associations were found in regions like the superior angular gyrus and the posterior frontal and parietal areas—regions often implicated in cognitive tasks and mental health disorders. Importantly, the researchers ran multiple control analyses to ensure the robustness of their findings. They ruled out the possibility that the observed effects were simply due to general brain activity patterns, fluctuations in heart rate or breathing, or basic features of stomach physiology. In other words, the association appeared specific to the coupling between the stomach’s electrical rhythm and particular brain networks—not just a general marker of body or brain state. Their approach was designed to detect broad psychological dimensions rather than focus on one diagnosis. The strongest psychological pattern they found was a spectrum ranging from negative affective states (like anxiety and depression) to positive traits (like well-being and quality of life). This result suggests that the stomach-brain connection is not tied to any one disorder but instead reflects a general mode of psychological functioning. “Anxiety, depression, stress, and fatigue showed the strongest links to stomach-brain communication,” Banellis explained. “While phrases like ‘butterflies in the stomach’ or feeling ‘sick to your stomach’ are common ways we describe emotional distress, it was surprising to find such consistent and clear evidence across these symptoms. Even more unexpected was the direction of the effect: we might have assumed that stronger alignment between the body and brain would be beneficial. Instead, our findings suggest that heightened stomach-brain communication could act more like a warning signal, an internal alarm system reflecting mental strain rather than harmony.” Read more:
12:34

Scientists discover surprising link between gut-brain interactions and mental health | Eric W. Dolan, PsyPost A new study provides evidence that the connection between the brain and the stomach may be linked to mental health in a measurable way. Researchers from Aarhus University in Denmark, publishing their work in Nature Mental Health, report that a specific pattern of communication between the brain and the stomach reflects how individuals feel emotionally and psychologically. Their findings suggest that these gut-brain interactions can indicate a person’s levels of anxiety, depression, well-being, and overall quality of life. The idea that emotions are linked to physical sensations in the gut is widely reflected in language. People often talk about having “butterflies in the stomach” when nervous, or feeling “sick to the stomach” when distressed. Yet, despite these common expressions, most scientific attention in the field of brain-body interaction has focused on other organs, such as the heart and lungs. These areas have long been studied for their roles in emotion and mood. The researchers were struck by how little was known about how the stomach, in particular, interacts with the brain. While recent studies have explored the influence of gut bacteria and digestion on mental health, very little work had been done on the electrical rhythms of the stomach and how they may directly communicate with the brain’s networks involved in emotion, attention, and cognition. The team behind this new study wanted to explore whether a person’s psychological profile might be reflected in how strongly the stomach and brain are coupled during rest. Their aim was not to link a specific diagnosis like depression to a single brain region, but rather to identify patterns across a broad spectrum of mental health experiences. “Our interest grew from the long-standing discussion about the role of the body in shaping emotion, a question that has fascinated philosophers and scientists for centuries,” said study author Leah Banellis (Leah Banellis), a postdoctoral fellow in Cognitive Neuroscience at Aarhus University. “Yet, while the heart and lungs have received much attention, the stomach has been largely overlooked. This gap struck us as especially surprising, because the link between the stomach and emotional experience feels so intuitive. It is heavily reflected in everyday language, with phrases like ‘butterflies in the stomach,’ ‘sick to our stomach,’ or ‘trust your gut.'” The research was part of the Visceral Mind Project, a large-scale initiative that combines data on brain activity, bodily rhythms, and psychological assessments. The team recorded data from 243 people using a method that captures both electrical signals from the stomach (electrogastrography) and brain activity measured with functional magnetic resonance imaging (fMRI). The participants represented a wide range of mental health profiles, from those reporting high well-being to others showing signs of distress, including anxiety, depression, fatigue, and insomnia. To capture this diversity, the researchers didn’t exclude people with psychiatric symptoms or diagnoses. Instead, they aimed for variation, which would allow their models to detect patterns across the mental health spectrum. Each participant underwent a series of recordings while lying still in the MRI scanner. At the same time, sensors on the abdomen captured the stomach’s slow electrical rhythm, which cycles about three times per minute. This rhythm, which originates from specialized cells in the stomach lining, is typically involved in coordinating digestion. But the researchers suspected it might also be linked to mental state. To analyze the relationship between stomach and brain activity, the team used a method that looks at how well the two rhythms align over time. This measure, known as phase-locking value, essentially captures the degree of synchronization between stomach signals and brain signals across different regions. The researchers then combined this data with results from a comprehensive mental health questionnaire. The battery included 37 different scores across a range of domains—such as anxiety, stress, mood, fatigue, attention, sleep quality, and life satisfaction. Using a statistical method known as canonical correlation analysis, they looked for patterns that linked brain-stomach coupling with the participants’ mental health profiles. The analysis revealed a clear and statistically significant pattern. Stronger coupling between the stomach’s rhythm and brain activity was associated with poorer mental health. Individuals who reported more symptoms of anxiety, depression, stress, and fatigue tended to show increased synchronization between their stomach and brain rhythms. In contrast, those with higher levels of well-being and life satisfaction showed weaker coupling. “For the first time, we’ve found a scientific link between your ‘gut feelings’ and your mental health, showing a surprising connection between your stomach’s natural rhythm and your brain,” Banellis told PsyPost. “Specifically, our study revealed that stronger communication between the stomach and brain is linked to worse mental health, such as higher symptoms of anxiety, depression, stress, and fatigue, whereas weaker stomach-brain communication aligns with better mental health reflected in higher overall well-being and quality of life.” This stomach-brain signature was not random. It was localized in specific brain networks, particularly those involved in attention, cognitive control, and salience detection. Some of the strongest associations were found in regions like the superior angular gyrus and the posterior frontal and parietal areas—regions often implicated in cognitive tasks and mental health disorders. Importantly, the researchers ran multiple control analyses to ensure the robustness of their findings. They ruled out the possibility that the observed effects were simply due to general brain activity patterns, fluctuations in heart rate or breathing, or basic features of stomach physiology. In other words, the association appeared specific to the coupling between the stomach’s electrical rhythm and particular brain networks—not just a general marker of body or brain state. Their approach was designed to detect broad psychological dimensions rather than focus on one diagnosis. The strongest psychological pattern they found was a spectrum ranging from negative affective states (like anxiety and depression) to positive traits (like well-being and quality of life). This result suggests that the stomach-brain connection is not tied to any one disorder but instead reflects a general mode of psychological functioning. “Anxiety, depression, stress, and fatigue showed the strongest links to stomach-brain communication,” Banellis explained. “While phrases like ‘butterflies in the stomach’ or feeling ‘sick to your stomach’ are common ways we describe emotional distress, it was surprising to find such consistent and clear evidence across these symptoms. Even more unexpected was the direction of the effect: we might have assumed that stronger alignment between the body and brain would be beneficial. Instead, our findings suggest that heightened stomach-brain communication could act more like a warning signal, an internal alarm system reflecting mental strain rather than harmony.” Read more:

Owen Gregorian

92,301 Aufrufe • vor 11 Monaten

Lecture 2 on our Quantum Mechanics Series Schrödinger’s equation doesn’t start from mystery. It starts from a very specific bet…the state of a particle is a complex field ψ(x,t), and whatever dynamics we write down must move ψ forward in time in a way that preserves total probability. We ask a basic question…what equation should ψ satisfy so that |ψ|² behaves like a conserved density, the way mass density does in fluid flow? What is ψ? Think of ψ(x,t) as the amplitude assigned to “the particle is at position x at time t”. It’s not a probability. It’s the object you add first, and only at the end do you square p(x,t) = |ψ(x,t)|² Because ψ is complex, it has magnitude and phase. Write it in polar form ψ(x,t) = r(x,t) exp(i θ(x,t)) Then r² = |ψ|² is the density, and θ will end up controlling flow (the probability current). Where does Schrödinger’s equation come from? Start with two empirical inputs about waves and particles: E = ħ ω p = ħ k Here ħ (“h-bar”) is Planck’s constant divided by 2π. It’s the unit conversion factor between the wave description (frequency ω, wavevector k) and the particle description (energy E, momentum p). In units, ħ has units of joule-seconds, so multiplying ω (1/seconds) gives energy (joules), and multiplying k (1/meters) gives momentum (kg·m/s). It’s the number that tells you how much energy or momentum you get per unit frequency or wavenumber. A plane wave with angular frequency ω and wavevector k is ψ(x,t) = A exp(i(k·x − ω t)) Now notice what derivatives do to this wave: ∂ψ/∂t = −i ω ψ ∇ψ = i k ψ ∇²ψ = −|k|² ψ Multiply those identities by ħ: i ħ ∂ψ/∂t = ħ ω ψ = E ψ −i ħ ∇ψ = ħ k ψ = p ψ −ħ² ∇²ψ = ħ² |k|² ψ = p² ψ So for plane waves, the operators Ê = i ħ ∂/∂t p̂ = −i ħ ∇ act like energy and momentum! Now use the classical nonrelativistic energy relation: E = p²/(2m) + V(x) This is bookkeeping for a particle moving slow enough that relativity can be ignored. The term p²/(2m) is kinetic energy. If p = mv, then p²/(2m) = (m²v²)/(2m) = (1/2)mv². The term V(x) is potential energy. It depends on position because forces come from spatially varying energy. A slope in V pushes the particle. Examples: for a charged particle in an electric potential φ(x), V(x) = q φ(x). Near Earth, V(z) = mgz. The point is total energy equals kinetic plus potential. Turn that into an equation for ψ by replacing E and p with the operators above: Ê ψ = (p̂²/(2m) + V) ψ Compute p̂² = (−i ħ ∇)·(−i ħ ∇) = −ħ² ∇², so we get i ħ ∂ψ/∂t = ( −ħ²/(2m) ∇² + V(x) ) ψ That is the time-dependent Schrödinger equation. The derivation here is a controlled heuristic: we matched the plane-wave identities to the measured relations E = ħω and p = ħk, then imposed the same energy bookkeeping as classical mechanics. Why this equation is the right kind of rule If ψ is the state, we need a rule that preserves total probability: ∫ |ψ(x,t)|² dx = 1 Schrödinger evolution does. You can see it by deriving a continuity equation. Let ρ(x,t) = |ψ|² = ψ* ψ. Take a time derivative: ∂ρ/∂t = ψ* ∂ψ/∂t + ψ ∂ψ*/∂t Use Schrödinger and its complex conjugate: ∂ψ/∂t = (1/(i ħ)) ( −ħ²/(2m) ∇²ψ + Vψ ) ∂ψ*/∂t = (−1/(i ħ)) ( −ħ²/(2m) ∇²ψ* + Vψ* ) Plug in. The V terms cancel exactly, and what remains can be rearranged into a divergence: ∂ρ/∂t + ∇·j = 0 where the probability current is j = (ħ/(2mi)) ( ψ* ∇ψ − ψ ∇ψ* ) This is the best way to explain ehat ψ is: |ψ|² behaves like a conserved density, and the phase of ψ is what drives the current j. So in this series, ψ isn’t a slogan. It’s the object whose modulus squared is the density, whose phase generates flow, and whose time evolution is fixed (up to V) by matching wave relations to energy bookkeeping: i ħ ∂ψ/∂t = ( -ħ²/(2m) ∇² + V ) ψ #QuantumMechanics #SchrodingerEquation #WaveFunction #BornRule #Physics #MathematicalPhysics
0:40

Lecture 2 on our Quantum Mechanics Series Schrödinger’s equation doesn’t start from mystery. It starts from a very specific bet…the state of a particle is a complex field ψ(x,t), and whatever dynamics we write down must move ψ forward in time in a way that preserves total probability. We ask a basic question…what equation should ψ satisfy so that |ψ|² behaves like a conserved density, the way mass density does in fluid flow? What is ψ? Think of ψ(x,t) as the amplitude assigned to “the particle is at position x at time t”. It’s not a probability. It’s the object you add first, and only at the end do you square p(x,t) = |ψ(x,t)|² Because ψ is complex, it has magnitude and phase. Write it in polar form ψ(x,t) = r(x,t) exp(i θ(x,t)) Then r² = |ψ|² is the density, and θ will end up controlling flow (the probability current). Where does Schrödinger’s equation come from? Start with two empirical inputs about waves and particles: E = ħ ω p = ħ k Here ħ (“h-bar”) is Planck’s constant divided by 2π. It’s the unit conversion factor between the wave description (frequency ω, wavevector k) and the particle description (energy E, momentum p). In units, ħ has units of joule-seconds, so multiplying ω (1/seconds) gives energy (joules), and multiplying k (1/meters) gives momentum (kg·m/s). It’s the number that tells you how much energy or momentum you get per unit frequency or wavenumber. A plane wave with angular frequency ω and wavevector k is ψ(x,t) = A exp(i(k·x − ω t)) Now notice what derivatives do to this wave: ∂ψ/∂t = −i ω ψ ∇ψ = i k ψ ∇²ψ = −|k|² ψ Multiply those identities by ħ: i ħ ∂ψ/∂t = ħ ω ψ = E ψ −i ħ ∇ψ = ħ k ψ = p ψ −ħ² ∇²ψ = ħ² |k|² ψ = p² ψ So for plane waves, the operators Ê = i ħ ∂/∂t p̂ = −i ħ ∇ act like energy and momentum! Now use the classical nonrelativistic energy relation: E = p²/(2m) + V(x) This is bookkeeping for a particle moving slow enough that relativity can be ignored. The term p²/(2m) is kinetic energy. If p = mv, then p²/(2m) = (m²v²)/(2m) = (1/2)mv². The term V(x) is potential energy. It depends on position because forces come from spatially varying energy. A slope in V pushes the particle. Examples: for a charged particle in an electric potential φ(x), V(x) = q φ(x). Near Earth, V(z) = mgz. The point is total energy equals kinetic plus potential. Turn that into an equation for ψ by replacing E and p with the operators above: Ê ψ = (p̂²/(2m) + V) ψ Compute p̂² = (−i ħ ∇)·(−i ħ ∇) = −ħ² ∇², so we get i ħ ∂ψ/∂t = ( −ħ²/(2m) ∇² + V(x) ) ψ That is the time-dependent Schrödinger equation. The derivation here is a controlled heuristic: we matched the plane-wave identities to the measured relations E = ħω and p = ħk, then imposed the same energy bookkeeping as classical mechanics. Why this equation is the right kind of rule If ψ is the state, we need a rule that preserves total probability: ∫ |ψ(x,t)|² dx = 1 Schrödinger evolution does. You can see it by deriving a continuity equation. Let ρ(x,t) = |ψ|² = ψ* ψ. Take a time derivative: ∂ρ/∂t = ψ* ∂ψ/∂t + ψ ∂ψ*/∂t Use Schrödinger and its complex conjugate: ∂ψ/∂t = (1/(i ħ)) ( −ħ²/(2m) ∇²ψ + Vψ ) ∂ψ*/∂t = (−1/(i ħ)) ( −ħ²/(2m) ∇²ψ* + Vψ* ) Plug in. The V terms cancel exactly, and what remains can be rearranged into a divergence: ∂ρ/∂t + ∇·j = 0 where the probability current is j = (ħ/(2mi)) ( ψ* ∇ψ − ψ ∇ψ* ) This is the best way to explain ehat ψ is: |ψ|² behaves like a conserved density, and the phase of ψ is what drives the current j. So in this series, ψ isn’t a slogan. It’s the object whose modulus squared is the density, whose phase generates flow, and whose time evolution is fixed (up to V) by matching wave relations to energy bookkeeping: i ħ ∂ψ/∂t = ( -ħ²/(2m) ∇² + V ) ψ #QuantumMechanics #SchrodingerEquation #WaveFunction #BornRule #Physics #MathematicalPhysics

Mathelirium

40,835 Aufrufe • vor 7 Monaten

Lenz Law "Eddy Current" Experiments 2 of 3 - Ring Magnets and a Spinning Copper Rod (YouTube deleted this experiment as well, labeling it as "medical misinformation.") Around 2010, I wrote a Facebook note titled "Frictionless Flywheel". (Facebook deleted my page of 15 years). In that write-up, I speculated and predicted that... ... if a magnet falls in slow motion down a copper tube... then what would happen if you did the inverse? Drop a magnet ring around a copper rod? It's the same relative motion between the magnet and the copper. So the magnet would tilt at an angle while slightly rotating as it falls in slow motion around the copper. But then I said... "Let's take it one step further... I predict that if you have magnet rings around a copper rod... and then you spin the rod... that the magnet rings would stabilize around the rod from eddy currents." I said, there will be a magnetic bearing effect where the magnet ring will levitate and spin. Because it's following in the wake of the eddy currents produced by the rotating copper. So the magnet should catch up to speed of the copper and match it like the moon to the Earth. Essentially remaining fixed relative to the copper rod... but since the copper is spinning at a high RPM, then the magnet should spin at a high RPM; albeit, with a slight lag. So, abiding by the scientific method, I make an observation of the magnet falling in slow motion. I thought critically about. I get a new idea. Once the new idea passes muster, I allow my self to make a prediction for a scenario that hasn't been tried yet. Worked it out in my mind to see if it was viable. But then I needed to test it. I didn't have the ability to spin a copper rod at a high enough RPM that I theorized would be needed. So I communicated with my friend Josh Toms We had been talking about different experiments for quite a while. Josh is a real experimenter and a good man. He tests things before he speaks on them and when he speaks, it's with an informed opinion. He sent me a copper sphere about 2 inches in diameter and taught me some cool stuff about eddy currents I hadn't seen when sending each other clips of experiments we were working on. I explained the concept of the "frictionless flywheel" and my prediction that the magnets would levitate and stabilize around the copper rod if it was fast enough. So Josh built a platform using an angle grinder as a motor to spin a copper rod. He put very strong NdFeB magnets around the 1 inch diameter copper rod. IT WORKED! Josh brought this platform to the Tesla Technology Conference in Albuquerque, NM in 2010 where we met up. I filmed the demonstration of the platform you see in this video. There was a young boy who was fascinated by some of the magnet demonstrations I and others were giving at the conference. So I was explaining the process of what was happening to the inquisitive kid. Again.. what you notice here is that the eddy currents act as a force field to prevent friction between the magnets and copper. There is a stable gap and "magnetic cushion" that forms after a certain RPM. (Dependent upon the mass/gauss of the magnet in relation to the diamagnetic quality/mass of the copper or material in motion.) And what is super interesting to me is that once again... you can violently shake the entire platform and it WILL NOT disturb the magnets. The will not touch that copper after a certain RPM. You could make violent 90˚ turns and from the perspective of the magnets... it's just moving in slow motion relative to the copper. Just like how the magnet falls in slow motion relative to the copper down the tube. But this is just at a higher velocity relative to a stationary observer viewing the rotating system. But it doesn't matter if it's a centripetally rotating system at a constant velocity or linear acceleration in a particular direction... those eddy currents act as a force field to protect whatever is relative to the magnets and/or copper in those moments. So the claim and prediction that a frictionless flywheel effect and magnetic bearing would take place between a ring magnet and spinning copper rod DEMONSTRATED to be true. Now "PROVING" this scenario means to derive mathematical equations to justify an explanation of how the end results arise. Spinning magnets on a copper rod doesn't prove anything. In science... proof is math. Experiments can never prove anything. Experiments never provide equations or numbers or variables. Experiments provide evidence. You collect the evidence FIRST... AFTER doing real-world, hands-on experimentation. Anything else is just armchair research and talk. You write the proofs on paper to describe how you think things happen. And your proofs must abide by the constraints of the model you use. Sure... you can use a mathematical artifice like Lorentz invariance (relativity) to prove the end results arise because of: dark matter accumulating between the magnet and copper. Or that it must be space-time curvature bending... Or that it must be because of the aether rippling and vortexing... Or because of the dynamic casimir effect or zero point energy. Or anti-gravity. NOPE! None of those things are needed in the explanations I offer for any experiments and real-world data collected. I use the framework of what I call GALILEAN VARIANCE. Good ol' classical physics in 3D Euclidean space. No 4th dimension. No space-time. No aether. No nonsense. Only Newtonian Mechanics and elementary math.... but with a twist! ... and under.. "a new light". I have a completely different interpretation as to what gravity is and the mechanism of how it arises. Issac Newton never claimed to have a theory for the CAUSE of gravity. Newton's brilliant work and optics describes the behavior of matter and energy within a gravitational field. But he never claimed to know what causes gravity. "I have not as yet been able to discover the reason for these properties of gravity from phenomena, and I do not form any hypotheses." - Newton However... I do claim to have an explanation for the cause and mechanism of gravity. And it means a total abandonment of relativity and half of QED. Gravity IS a force! (and an emission) Eddy currents are not gravity. Magnetism is not gravity. And the kicker... gravity is not electric either. It's an illusion. Gravity is not an illusion. But the illusion is that people think gravity is electric. It's not. I have a completely different take. And boy has it gotten me in a lot of trouble and banned from speaking time and time again. But... if the information is valid... then trying to suppress it is like trying to hold an inflated basketball under water. Your arms are gonna get tired eventually and things are gonna come up to the surface with spring-loaded force. Equal to the amount people tried holding it back. The Rebirth of Classical Physics: Time, Light & Gravity
2:36

Lenz Law "Eddy Current" Experiments 2 of 3 - Ring Magnets and a Spinning Copper Rod (YouTube deleted this experiment as well, labeling it as "medical misinformation.") Around 2010, I wrote a Facebook note titled "Frictionless Flywheel". (Facebook deleted my page of 15 years). In that write-up, I speculated and predicted that... ... if a magnet falls in slow motion down a copper tube... then what would happen if you did the inverse? Drop a magnet ring around a copper rod? It's the same relative motion between the magnet and the copper. So the magnet would tilt at an angle while slightly rotating as it falls in slow motion around the copper. But then I said... "Let's take it one step further... I predict that if you have magnet rings around a copper rod... and then you spin the rod... that the magnet rings would stabilize around the rod from eddy currents." I said, there will be a magnetic bearing effect where the magnet ring will levitate and spin. Because it's following in the wake of the eddy currents produced by the rotating copper. So the magnet should catch up to speed of the copper and match it like the moon to the Earth. Essentially remaining fixed relative to the copper rod... but since the copper is spinning at a high RPM, then the magnet should spin at a high RPM; albeit, with a slight lag. So, abiding by the scientific method, I make an observation of the magnet falling in slow motion. I thought critically about. I get a new idea. Once the new idea passes muster, I allow my self to make a prediction for a scenario that hasn't been tried yet. Worked it out in my mind to see if it was viable. But then I needed to test it. I didn't have the ability to spin a copper rod at a high enough RPM that I theorized would be needed. So I communicated with my friend Josh Toms We had been talking about different experiments for quite a while. Josh is a real experimenter and a good man. He tests things before he speaks on them and when he speaks, it's with an informed opinion. He sent me a copper sphere about 2 inches in diameter and taught me some cool stuff about eddy currents I hadn't seen when sending each other clips of experiments we were working on. I explained the concept of the "frictionless flywheel" and my prediction that the magnets would levitate and stabilize around the copper rod if it was fast enough. So Josh built a platform using an angle grinder as a motor to spin a copper rod. He put very strong NdFeB magnets around the 1 inch diameter copper rod. IT WORKED! Josh brought this platform to the Tesla Technology Conference in Albuquerque, NM in 2010 where we met up. I filmed the demonstration of the platform you see in this video. There was a young boy who was fascinated by some of the magnet demonstrations I and others were giving at the conference. So I was explaining the process of what was happening to the inquisitive kid. Again.. what you notice here is that the eddy currents act as a force field to prevent friction between the magnets and copper. There is a stable gap and "magnetic cushion" that forms after a certain RPM. (Dependent upon the mass/gauss of the magnet in relation to the diamagnetic quality/mass of the copper or material in motion.) And what is super interesting to me is that once again... you can violently shake the entire platform and it WILL NOT disturb the magnets. The will not touch that copper after a certain RPM. You could make violent 90˚ turns and from the perspective of the magnets... it's just moving in slow motion relative to the copper. Just like how the magnet falls in slow motion relative to the copper down the tube. But this is just at a higher velocity relative to a stationary observer viewing the rotating system. But it doesn't matter if it's a centripetally rotating system at a constant velocity or linear acceleration in a particular direction... those eddy currents act as a force field to protect whatever is relative to the magnets and/or copper in those moments. So the claim and prediction that a frictionless flywheel effect and magnetic bearing would take place between a ring magnet and spinning copper rod DEMONSTRATED to be true. Now "PROVING" this scenario means to derive mathematical equations to justify an explanation of how the end results arise. Spinning magnets on a copper rod doesn't prove anything. In science... proof is math. Experiments can never prove anything. Experiments never provide equations or numbers or variables. Experiments provide evidence. You collect the evidence FIRST... AFTER doing real-world, hands-on experimentation. Anything else is just armchair research and talk. You write the proofs on paper to describe how you think things happen. And your proofs must abide by the constraints of the model you use. Sure... you can use a mathematical artifice like Lorentz invariance (relativity) to prove the end results arise because of: dark matter accumulating between the magnet and copper. Or that it must be space-time curvature bending... Or that it must be because of the aether rippling and vortexing... Or because of the dynamic casimir effect or zero point energy. Or anti-gravity. NOPE! None of those things are needed in the explanations I offer for any experiments and real-world data collected. I use the framework of what I call GALILEAN VARIANCE. Good ol' classical physics in 3D Euclidean space. No 4th dimension. No space-time. No aether. No nonsense. Only Newtonian Mechanics and elementary math.... but with a twist! ... and under.. "a new light". I have a completely different interpretation as to what gravity is and the mechanism of how it arises. Issac Newton never claimed to have a theory for the CAUSE of gravity. Newton's brilliant work and optics describes the behavior of matter and energy within a gravitational field. But he never claimed to know what causes gravity. "I have not as yet been able to discover the reason for these properties of gravity from phenomena, and I do not form any hypotheses." - Newton However... I do claim to have an explanation for the cause and mechanism of gravity. And it means a total abandonment of relativity and half of QED. Gravity IS a force! (and an emission) Eddy currents are not gravity. Magnetism is not gravity. And the kicker... gravity is not electric either. It's an illusion. Gravity is not an illusion. But the illusion is that people think gravity is electric. It's not. I have a completely different take. And boy has it gotten me in a lot of trouble and banned from speaking time and time again. But... if the information is valid... then trying to suppress it is like trying to hold an inflated basketball under water. Your arms are gonna get tired eventually and things are gonna come up to the surface with spring-loaded force. Equal to the amount people tried holding it back. The Rebirth of Classical Physics: Time, Light & Gravity

TheRealVerbz (Jason Verbelli)

45,879 Aufrufe • vor 1 Jahr

As promised... Putting the pieces together. They will yield under the weight of the evidence. Tracked the speed, matched it with a conventional device. Replicated the density of human flesh, replicated the characteristics of human flesh. Matched the projectiles weight and size and replicate the wound we witnessed. Without the neck wound the prepared narrative of a .30-06 shot to the chest would have been perfect. The 2 gram PETN shaped charge with a 2cm standoff height would have made a entry wound nearly identical to a .30-06 entry wound. The wound channel and internal trauma would have match what would have been expected. The copper and cone would have even left fragmentation if desired. Every aspect of the event would have perfectly match a .30-06 shot to the chest. Nobody could have guessed that shrapnel would strike his neck forcing a hard pivot away from a single shot to the chest to a single shot to the neck. The neck wound has none of the characteristics of a .30-06 impact, his physical reactions (Key Physical Reactions Observed The video (duration ~25 seconds, slowed for clarity) depicts a sequence of involuntary movements occurring in under 3 seconds post-event. Here's a chronological breakdown: Initial Trigger (0-0.5 seconds post-onset): The individual's head snaps backward sharply (retroflexion), with minimal forward lean or lateral tilt. His torso briefly lifts upward from the chair (paraspinal muscle spasm), despite being seated and leaning slightly forward. This creates a momentary "arch" in the spine, elevating the shoulders ~2-3 inches off the backrest. No immediate external propulsion (e.g., no visible "push" from behind or side), suggesting an internal or proximal force vector originating near the upper torso/neck. Upper Body Response (0.5-1.5 seconds): Arms flex rigidly at the elbows and adduct toward the midline (drawing inward across the chest/abdomen), with hands clenching into tight fists. This is not a protective flail or grasp but a sustained, unnatural rigidity. The neck shows a subtle "pop" or fabric disturbance at the collar level, followed by the necklace chain whipping upward and over the head—consistent with a localized explosive expansion rather than general air displacement. Lower Body Response (1-2 seconds): Legs extend forcefully forward and outward from a relaxed seated position, with knees locking and feet plantar-flexing (toes pointing downward). The thighs lift the pelvis slightly, contributing to the torso elevation. There's a brief crossing or scissoring of the lower legs, which resolves into limp collapse. Collapse Phase (2-3 seconds onward): The body slumps laterally out of the chair in a ragdoll-like manner, with total loss of postural tone. Arms remain semi-flexed but drop without resistance, and the head lolls forward unnaturally. No voluntary recovery attempts (e.g., no bracing with hands or vocalization beyond a gasp), and bystanders' reactions lag by ~1 second, indicating the event's rapidity. These movements are highly stereotyped and non-voluntary—far from a simple faint, trip, or even a standard gunshot flinch. The symmetry (bilateral arm/leg involvement) and speed rule out focal motor issues like a stroke. Biomechanical Explanation Neurological Basis: This sequence matches decorticate posturing (also called decorticate rigidity), a primitive reflex triggered by severe disruption to the brain's cerebral cortex while sparing deeper structures like the midbrain and pons. It's a brainstem-mediated response to protect vital functions during acute cerebral insult. Arm flexion/adduction and fisting: Caused by disinhibition of rubrospinal tracts, leading to flexor dominance in the upper limbs. Leg extension: Vestibulospinal tracts activate extensors in the lower body for "postural support" in a collapsing state. Torso lift and head retroflexion: Paraspinal (erector spinae) spasm from sudden sympathetic surge and vestibular overload, akin to an "agonal" (end-stage) reflex. Force Dynamics: The lack of external blast markers (e.g., no widespread debris scatter or hair whipping from wind) but presence of localized effects (collar disturbance, chain ejection) points to a contained pressure wave. Gases expand rapidly in a confined space (e.g., between skin and clothing), creating shear forces that propagate through tissues without much external venting. The body's center of mass shifts posteriorly due to the spasm, explaining the backward fall from a seated position. Physiological Cascade: The entire reaction implies near-instantaneous loss of consciousness ( 5 seconds) and lacks the explosive torso lift. Blunt Trauma: Wouldn't cause bilateral rigidity or such rapid desanguination patterns. Secondary Blast (Shrapnel): Possible contributor (e.g., micro-fragments from the device), but the primary wave dominates the neuro response. In summary, this isn't a "fall" or "shot"—it's a textbook neurogenic collapse from blast wave neurotrauma, pointing to an improvised explosive device (IED) in intimate contact. The precision suggests targeted assassination tech, not random violence.
1:19

As promised... Putting the pieces together. They will yield under the weight of the evidence. Tracked the speed, matched it with a conventional device. Replicated the density of human flesh, replicated the characteristics of human flesh. Matched the projectiles weight and size and replicate the wound we witnessed. Without the neck wound the prepared narrative of a .30-06 shot to the chest would have been perfect. The 2 gram PETN shaped charge with a 2cm standoff height would have made a entry wound nearly identical to a .30-06 entry wound. The wound channel and internal trauma would have match what would have been expected. The copper and cone would have even left fragmentation if desired. Every aspect of the event would have perfectly match a .30-06 shot to the chest. Nobody could have guessed that shrapnel would strike his neck forcing a hard pivot away from a single shot to the chest to a single shot to the neck. The neck wound has none of the characteristics of a .30-06 impact, his physical reactions (Key Physical Reactions Observed The video (duration ~25 seconds, slowed for clarity) depicts a sequence of involuntary movements occurring in under 3 seconds post-event. Here's a chronological breakdown: Initial Trigger (0-0.5 seconds post-onset): The individual's head snaps backward sharply (retroflexion), with minimal forward lean or lateral tilt. His torso briefly lifts upward from the chair (paraspinal muscle spasm), despite being seated and leaning slightly forward. This creates a momentary "arch" in the spine, elevating the shoulders ~2-3 inches off the backrest. No immediate external propulsion (e.g., no visible "push" from behind or side), suggesting an internal or proximal force vector originating near the upper torso/neck. Upper Body Response (0.5-1.5 seconds): Arms flex rigidly at the elbows and adduct toward the midline (drawing inward across the chest/abdomen), with hands clenching into tight fists. This is not a protective flail or grasp but a sustained, unnatural rigidity. The neck shows a subtle "pop" or fabric disturbance at the collar level, followed by the necklace chain whipping upward and over the head—consistent with a localized explosive expansion rather than general air displacement. Lower Body Response (1-2 seconds): Legs extend forcefully forward and outward from a relaxed seated position, with knees locking and feet plantar-flexing (toes pointing downward). The thighs lift the pelvis slightly, contributing to the torso elevation. There's a brief crossing or scissoring of the lower legs, which resolves into limp collapse. Collapse Phase (2-3 seconds onward): The body slumps laterally out of the chair in a ragdoll-like manner, with total loss of postural tone. Arms remain semi-flexed but drop without resistance, and the head lolls forward unnaturally. No voluntary recovery attempts (e.g., no bracing with hands or vocalization beyond a gasp), and bystanders' reactions lag by ~1 second, indicating the event's rapidity. These movements are highly stereotyped and non-voluntary—far from a simple faint, trip, or even a standard gunshot flinch. The symmetry (bilateral arm/leg involvement) and speed rule out focal motor issues like a stroke. Biomechanical Explanation Neurological Basis: This sequence matches decorticate posturing (also called decorticate rigidity), a primitive reflex triggered by severe disruption to the brain's cerebral cortex while sparing deeper structures like the midbrain and pons. It's a brainstem-mediated response to protect vital functions during acute cerebral insult. Arm flexion/adduction and fisting: Caused by disinhibition of rubrospinal tracts, leading to flexor dominance in the upper limbs. Leg extension: Vestibulospinal tracts activate extensors in the lower body for "postural support" in a collapsing state. Torso lift and head retroflexion: Paraspinal (erector spinae) spasm from sudden sympathetic surge and vestibular overload, akin to an "agonal" (end-stage) reflex. Force Dynamics: The lack of external blast markers (e.g., no widespread debris scatter or hair whipping from wind) but presence of localized effects (collar disturbance, chain ejection) points to a contained pressure wave. Gases expand rapidly in a confined space (e.g., between skin and clothing), creating shear forces that propagate through tissues without much external venting. The body's center of mass shifts posteriorly due to the spasm, explaining the backward fall from a seated position. Physiological Cascade: The entire reaction implies near-instantaneous loss of consciousness ( 5 seconds) and lacks the explosive torso lift. Blunt Trauma: Wouldn't cause bilateral rigidity or such rapid desanguination patterns. Secondary Blast (Shrapnel): Possible contributor (e.g., micro-fragments from the device), but the primary wave dominates the neuro response. In summary, this isn't a "fall" or "shot"—it's a textbook neurogenic collapse from blast wave neurotrauma, pointing to an improvised explosive device (IED) in intimate contact. The precision suggests targeted assassination tech, not random violence.

Jon Bray

26,913 Aufrufe • vor 9 Monaten

Thermodynamic computing is here There is a new computing paradigm emerging from the noise, and its arrival may be as significant as the dawn of deep learning or the advent of cloud virtualization. A new company, Extropic, has just launched its first thermodynamic computer, a device they call a TSU, or Thermal Sampling Unit. While the web is already filling with deep technical dives, what’s more important for most of us is building a clear intuition for what this technology is, how it’s fundamentally different from anything that’s come before, and why it’s generating so much excitement. This isn’t just another chip; it’s a new way to think about computation itself. Seeing is Believing: Solving Puzzles in One Shot To understand what a TSU does, let’s look at two classic, notoriously difficult computer science problems: Sudoku and the Eight Queens problem. When you or I solve a Sudoku, we use a process of sequential logic, guess-and-check, and backtracking. We make an assumption, follow its logical conclusion, and if we hit a dead end, we erase and try again. A classical computer does the same, just much faster. A TSU, however, approaches this in a completely different way. Using a TSU simulator, one can “program” the problem by first clamping the known values—the clues already on the board. Then, you program in the constraints: no duplicate numbers in any row, column, or 3x3 square. With the problem thus defined, the TSU doesn’t “search” for a solution; it anneals one. In a single computational step, the solution simply emerges, backfilling all the empty squares correctly. The same principle applies to the Eight Queens problem, a challenge to place eight queens on a chessboard so that none can attack any other. This is a complex combinatorial problem with 92 distinct solutions. A classical computer would have to iteratively search for these. A TSU, by contrast, can be programmed with the constraints (the “anti-affinity” between queens on the same row, column, or diagonal) and then set to sample the “solution space.” In this context, a valid solution is one with a “problem energy” of zero. The TSU’s physical nature allows it to naturally find these zero-energy states. A simulation of this process shows the TSU discovering all 92 unique solutions, demonstrating its ability to not just find an answer, but to explore the entire landscape of all correct answers. This is a fundamentally new approach, one that bypasses the brute-force, iterative methods we’ve relied on for decades. The Physics of Computation: Using Noise, Not Fighting It This new power comes from a radical design philosophy. For the last 70 years, computing has been about one thing: order. We build chips that are deterministic, logical, and precise. The great enemy has always been noise, heat, and randomness. We spend billions on cooling and error correction to eliminate these very things. Quantum computing, in many ways, is the ultimate expression of this, requiring temperatures near absolute zero to eliminate all thermal noise and achieve quantum coherence. Thermodynamic computing is the polar opposite. It doesn’t fight the noise; it uses it. The TSU is built on the understanding that the natural, stochastic noise from “leaky” transistors—the very randomness we’ve tried to engineer out of existence—is itself a powerful computational resource. Think of it this way: a GPU, which is central to today’s AI, has to simulate noise. When a generative AI model creates a new image or sentence, it’s using complex algorithms to fake randomness. The TSU doesn’t need to fake it; it harnesses the actual physical randomness of thermodynamics. It is a piece of hardware that directly computes with probability. This makes it a hybrid, sitting somewhere between a purely analog computer (which might use light or sound waves to compute) and a digital GPU. It’s a physical device that leverages the laws of physics itself to find solutions, rather than just using logic gates to simulate them. From a Lost Hiker to a Million Bouncy Balls Perhaps the best way to build intuition is with a metaphor. Imagine that solving a complex optimization problem is like trying to find the lowest point of altitude in a 100-square-mile mountainous landscape. Classical computing, using an algorithm like gradient descent, is like being a single hiker dropped into this landscape at night. You have no map or satellite view. All you have is an altimeter and the sensation of the slope under your feet. You can only take one step at a time, always walking downhill, hoping you don’t get stuck in a small local valley when the true, lowest canyon is miles away. Thermodynamic computing is a completely different approach. It’s like having a million bouncy balls and a helicopter. You drop all million balls simultaneously across the entire 100-square-mile landscape. Then, you “turn on an earthquake,” shaking the entire system. The balls bounce and jostle, but as the shaking (the “annealing”) subsides, where do they all end up? They naturally settle into the lowest points. The balls that collect in the deepest valley represent the optimal solution. The TSU is, in essence, a physical device for dropping those million balls at once and letting the laws of thermodynamics find the lowest “energy” state for you, all at the same time. Beyond Puzzles: The Real-World Impact This is far more than just a clever way to solve brain teasers. This ability to instantly find the lowest energy state for a complex, constrained system has staggering real-world applications. One of the most immediate is protein folding. Companies like Google’s DeepMind have made incredible progress with AI like AlphaFold, which predicts protein structures. But this is still a predictive model trained on existing data. A TSU could potentially solve the folding problem directly, treating the protein as a system of atomic affinities and repulsions and finding its most stable, lowest-energy configuration almost instantaneously. This could revolutionize drug discovery and materials science. An even more profound possibility lies in nuclear fusion. One of the greatest engineering challenges in history is controlling the superheated plasma within a tokamak reactor. This requires shaping unimaginably complex magnetic containment fields in real-time to prevent the plasma from touching the reactor walls. This is a real-time optimization problem so complex it’s currently beyond our capabilities. A TSU, however, could be fast enough. Its ability to compute with electricity itself, rather than abstracting the problem through layers of software, might allow it to update the magnetic fields fast enough to stabilize the fusion reaction. One could even imagine a future where thermodynamic computing elements are built directly into the tokamak’s walls, allowing the reactor to physically and intelligently react to the plasma’s state in real time. A ‘GPT-2 Moment’ for a New Era It’s easy to become numb to hype, but what we are witnessing with the TSU feels different. This is what you might call a “GPT-2 moment.” For those who were there, GPT-2 was the first generative AI model that wasn’t just a toy; it was the first time you could play with it at home and see the spark of true generative intelligence. It was the precursor that pointed directly to the GPT-3 and ChatGPT revolution that has since changed the world. This TSU has that same feel. It’s the “SDK” for a new computing paradigm. This technology is as different from classical computing as quantum computing is, but with a critical difference: a team of 15 built this in two years, and it runs at room temperature on your desk. Quantum computing has seen decades of work and billions in funding, and it still hasn’t produced a commercially viable, scalable machine. The TSU is here now. Based on a two-decade-long career at the cutting edge of technology—from seeing the obvious future of virtualization in 2007 to an early conviction in deep learning and GPT—this has all the same hallmarks of a fundamental, world-changing shift. We are not just building faster calculators; we are learning to compute with the universe itself. Pay close attention to this. This is the next big thing.
18:01

Thermodynamic computing is here There is a new computing paradigm emerging from the noise, and its arrival may be as significant as the dawn of deep learning or the advent of cloud virtualization. A new company, Extropic, has just launched its first thermodynamic computer, a device they call a TSU, or Thermal Sampling Unit. While the web is already filling with deep technical dives, what’s more important for most of us is building a clear intuition for what this technology is, how it’s fundamentally different from anything that’s come before, and why it’s generating so much excitement. This isn’t just another chip; it’s a new way to think about computation itself. Seeing is Believing: Solving Puzzles in One Shot To understand what a TSU does, let’s look at two classic, notoriously difficult computer science problems: Sudoku and the Eight Queens problem. When you or I solve a Sudoku, we use a process of sequential logic, guess-and-check, and backtracking. We make an assumption, follow its logical conclusion, and if we hit a dead end, we erase and try again. A classical computer does the same, just much faster. A TSU, however, approaches this in a completely different way. Using a TSU simulator, one can “program” the problem by first clamping the known values—the clues already on the board. Then, you program in the constraints: no duplicate numbers in any row, column, or 3x3 square. With the problem thus defined, the TSU doesn’t “search” for a solution; it anneals one. In a single computational step, the solution simply emerges, backfilling all the empty squares correctly. The same principle applies to the Eight Queens problem, a challenge to place eight queens on a chessboard so that none can attack any other. This is a complex combinatorial problem with 92 distinct solutions. A classical computer would have to iteratively search for these. A TSU, by contrast, can be programmed with the constraints (the “anti-affinity” between queens on the same row, column, or diagonal) and then set to sample the “solution space.” In this context, a valid solution is one with a “problem energy” of zero. The TSU’s physical nature allows it to naturally find these zero-energy states. A simulation of this process shows the TSU discovering all 92 unique solutions, demonstrating its ability to not just find an answer, but to explore the entire landscape of all correct answers. This is a fundamentally new approach, one that bypasses the brute-force, iterative methods we’ve relied on for decades. The Physics of Computation: Using Noise, Not Fighting It This new power comes from a radical design philosophy. For the last 70 years, computing has been about one thing: order. We build chips that are deterministic, logical, and precise. The great enemy has always been noise, heat, and randomness. We spend billions on cooling and error correction to eliminate these very things. Quantum computing, in many ways, is the ultimate expression of this, requiring temperatures near absolute zero to eliminate all thermal noise and achieve quantum coherence. Thermodynamic computing is the polar opposite. It doesn’t fight the noise; it uses it. The TSU is built on the understanding that the natural, stochastic noise from “leaky” transistors—the very randomness we’ve tried to engineer out of existence—is itself a powerful computational resource. Think of it this way: a GPU, which is central to today’s AI, has to simulate noise. When a generative AI model creates a new image or sentence, it’s using complex algorithms to fake randomness. The TSU doesn’t need to fake it; it harnesses the actual physical randomness of thermodynamics. It is a piece of hardware that directly computes with probability. This makes it a hybrid, sitting somewhere between a purely analog computer (which might use light or sound waves to compute) and a digital GPU. It’s a physical device that leverages the laws of physics itself to find solutions, rather than just using logic gates to simulate them. From a Lost Hiker to a Million Bouncy Balls Perhaps the best way to build intuition is with a metaphor. Imagine that solving a complex optimization problem is like trying to find the lowest point of altitude in a 100-square-mile mountainous landscape. Classical computing, using an algorithm like gradient descent, is like being a single hiker dropped into this landscape at night. You have no map or satellite view. All you have is an altimeter and the sensation of the slope under your feet. You can only take one step at a time, always walking downhill, hoping you don’t get stuck in a small local valley when the true, lowest canyon is miles away. Thermodynamic computing is a completely different approach. It’s like having a million bouncy balls and a helicopter. You drop all million balls simultaneously across the entire 100-square-mile landscape. Then, you “turn on an earthquake,” shaking the entire system. The balls bounce and jostle, but as the shaking (the “annealing”) subsides, where do they all end up? They naturally settle into the lowest points. The balls that collect in the deepest valley represent the optimal solution. The TSU is, in essence, a physical device for dropping those million balls at once and letting the laws of thermodynamics find the lowest “energy” state for you, all at the same time. Beyond Puzzles: The Real-World Impact This is far more than just a clever way to solve brain teasers. This ability to instantly find the lowest energy state for a complex, constrained system has staggering real-world applications. One of the most immediate is protein folding. Companies like Google’s DeepMind have made incredible progress with AI like AlphaFold, which predicts protein structures. But this is still a predictive model trained on existing data. A TSU could potentially solve the folding problem directly, treating the protein as a system of atomic affinities and repulsions and finding its most stable, lowest-energy configuration almost instantaneously. This could revolutionize drug discovery and materials science. An even more profound possibility lies in nuclear fusion. One of the greatest engineering challenges in history is controlling the superheated plasma within a tokamak reactor. This requires shaping unimaginably complex magnetic containment fields in real-time to prevent the plasma from touching the reactor walls. This is a real-time optimization problem so complex it’s currently beyond our capabilities. A TSU, however, could be fast enough. Its ability to compute with electricity itself, rather than abstracting the problem through layers of software, might allow it to update the magnetic fields fast enough to stabilize the fusion reaction. One could even imagine a future where thermodynamic computing elements are built directly into the tokamak’s walls, allowing the reactor to physically and intelligently react to the plasma’s state in real time. A ‘GPT-2 Moment’ for a New Era It’s easy to become numb to hype, but what we are witnessing with the TSU feels different. This is what you might call a “GPT-2 moment.” For those who were there, GPT-2 was the first generative AI model that wasn’t just a toy; it was the first time you could play with it at home and see the spark of true generative intelligence. It was the precursor that pointed directly to the GPT-3 and ChatGPT revolution that has since changed the world. This TSU has that same feel. It’s the “SDK” for a new computing paradigm. This technology is as different from classical computing as quantum computing is, but with a critical difference: a team of 15 built this in two years, and it runs at room temperature on your desk. Quantum computing has seen decades of work and billions in funding, and it still hasn’t produced a commercially viable, scalable machine. The TSU is here now. Based on a two-decade-long career at the cutting edge of technology—from seeing the obvious future of virtualization in 2007 to an early conviction in deep learning and GPT—this has all the same hallmarks of a fundamental, world-changing shift. We are not just building faster calculators; we are learning to compute with the universe itself. Pay close attention to this. This is the next big thing.

David Shapiro (L/0)

83,649 Aufrufe • vor 9 Monaten

TOPIC #107: PI NETWORK IS A STABLE COIN? -WHO DECIDES PI FULLY OM FIXED VALUE? Dear GCV army, I hope you are all doing great! First of all, I would like to express my sincere gratitude for all your hard work. Many of you have achieved significant milestones, and it’s evident that you are making a great difference. Our influence has grown significantly, with an increasing number of social media posts and YouTubers publicly supporting us. I can see that more and more people are beginning to understand why we advocate for GCV. Today's meeting aims to alleviate any doubts you may have, allowing you to relax and feel confident as we embark on our historic journey together. I will answer the questions I’ve received and address some important issues we need to focus on to maintain our community's efficiency, particularly regarding our Generals, which will be the topic next weekend. I put the questions I received here. "A question addressed to Ms. Doris Yin in the emergency meeting 1– In light of the rapidly changing global circumstances and the increasing discussion about stablecoins backed by U.S. Treasury bonds, how do you see the future role of the Pi Network in this context? And what practical steps should the GCV army take now to accelerate this path? 2_ There are those who promote the idea that the price of Pi is what appears in the market (currently around $0.49) and compare it to the price of GCV within the ecosystem (314,159 Pi = 1 good or service). They say if Pi’s price rises to $2, it means that the value within The ecosystem is approximately 2 million dollars. With sincere appreciation and discipline." This is from the Arab head of GCV Ambassador Mr. Mohammed. Another question: "Hello, my Global Ambassador, I am Ateba Joseph, Ecological Ambassador in Cameroon And a member of the GCV army, I am delighted to exchange with you. Regarding the meeting with the GCV army on Sunday, July 27, 2025.. Here is my concern: A few days ago, a correspondence indicated that Pi is not or is not yet a stable coin. Upon reading this information, we have provided many explanations to help the pioneers understand this. I hope you will focus more on this statement to further strengthen our understanding of the subject. Thank you for taking my concerns into consideration" Thank you for the above questions; my answers are below. The first question concerns stablecoins. Many pioneers are hoping that Pi can be recognized by the U.S. government as a stablecoin. I wrote an article on this in May. On July 18, 2025, President Trump signed the Guiding and Establishing National Innovation for US Stablecoins Act (the GENIUS Act) into law. This legislation establishes a regulatory framework for payment stablecoins and marks the first federal legislation on digital assets enacted since President Trump issued an executive order aimed at making the U.S. the “crypto capital of the world.” U.S.-issued stablecoins are expected to become the primary means of dollar transactions globally, especially in emerging markets with unstable local currencies. The sponsors of the GENIUS Act estimate that by 2030, stablecoin issuers may collectively become the largest holders of U.S. Treasuries, surpassing foreign central banks. From this, we can see that U.S. stablecoins must maintain reserves backing outstanding payment stablecoins on a one-to-one basis, consisting only of specified assets, including U.S. dollars and short-term Treasury securities. It is clear that the Pi Network will not take this path, as it is not part of our plan. A stablecoin is essentially a digital representation of the U.S. dollar. All stablecoin issuers do not create a new currency; rather, it’s akin to purchasing chips at a casino – you must use U.S. dollars to buy those chips. However, Pi is a completely new currency. It does not need to be backed up by U.S. dollars or U.S. Treasuries to be used. If that were the case, we wouldn’t need to establish an ecosystem or have a three-year enclosed mainnet. I previously mentioned the possibility of Pi being an algorithmic stablecoin since only algorithmic stablecoins do not need to be backed by U.S. dollars. However, algorithmic stablecoins have faced significant failures in the past. The collapse of the Terra (LUNA) cryptocurrency resulted in a loss of at least $40 billion in market capitalization, with estimates reaching as high as $60 billion. TerraUSD (UST), an algorithmic stablecoin, lost its peg to the U.S. dollar, contributing to its overall collapse. The new stablecoin legislation recently passed through the Senate effectively ties the U.S. Treasury to crypto, as it essentially bets the government’s cash flow on digital tokens and market speculation. This legislation requires stablecoins to be backed by short-term Treasury bills, generating an estimated $2–$3 trillion in new demand for government debt, which is nearly half the current size of the T-bill market. On paper, this looks beneficial, but in reality, it creates a circular feedback loop: crypto demand fuels stablecoins, stablecoins buy T-bills, and T-bills fund government deficits. The government becomes reliant on speculative capital flows. Thus, we should understand why the U.S. government will not support the Pi Network as a stablecoin, as they require stablecoin issuers to buy T-bills and can no longer trust algorithmic stablecoins. So, what is the future of the Pi Network as a currency? From my perspective, Pi is already listed on exchange markets. It cannot be classified as a security because it is mined freely and is not an ICO. Instead, it should be categorized as a commodity, similar to Bitcoin and ETH. When a currency is listed for trading on an exchange, its price is determined by the balance of supply and demand. However, Pi is a currency in its own right; it has inherent value from Pi holders -Pioneers. Historically, currency has served as a medium of exchange. A medium of exchange is a widely accepted item for buying goods and services in an economy. It facilitates transactions by eliminating the need for a barter system, where goods are directly exchanged for other goods. In modern economies, money (such as currency) serves as the primary medium of exchange. **Functions of Money:** One of the core functions of money is to serve as a medium of exchange, enabling the smooth transfer of value between buyers and sellers, thereby simplifying trade and economic activity. **Examples:** In modern economies, this typically includes currency (paper money, coins) or digital money. In specific historical contexts, other items, such as cigarettes in prisoner-of-war camps, have also served as mediums of exchange. **Importance of Acceptance:** For a medium of exchange to function effectively, it must be widely accepted and trusted within the relevant community. **Not the Same as a Payment Method:** While credit cards and checks are used for payments, they do not serve as mediums of exchange themselves. Therefore, stablecoin is not a new currency. It is more likely to have a credit card or check character. It is a USD digital status. From the analysis presented, we can draw the following conclusions: The current price of Pi on the exchange market primarily serves as a temporary measure to facilitate broad expansion. While this is not our primary objective, it constitutes a strategic approach towards achieving our mission. To gain a clearer perspective, we must adopt a higher-level view of the overall vision for the Pi Network. The mission and vision of Pi Network clearly articulate that it is not intended to function as a commodity for sale, nor is it meant to be an investment vehicle or a speculative security. Instead, it is crucial to recognize that Pi is designed to be a medium of exchange—a new form of currency. As pioneers in this venture, we have the unique opportunity to acquire Pi through free mining. However, it is important to note that the current mining rate is relatively slow. To overcome this limitation and to further our goal of mass adoption, it is essential for more individuals to join the Pi Network and participate in holding Pi. One efficient way to accelerate this process is by allowing Pi to be traded on the exchange market, which can result in rapid and widespread adoption. Since Pi can be mined for free, a lower price could make it more accessible to a larger number of people. It's important to focus on our primary goal during this pre-full Open Mainnet (OM) phase: mass adoption, rather than aiming for high prices, which many pioneers expected. Some pioneers want to sell when the price increases, but if too many sell, it could undermine our goal of achieving mass adoption. This scenario is reminiscent of historical instances when shells served as currency—readily accessible from the sea or buy from the village market. For shells to function effectively as currency, a collective effort was needed to hold and circulate them within the village. If only a select few individuals possess the shells, the currency lacks the necessary circulation to sustain an economy. Hence, our goal should not be centered on achieving a high price; instead, we should strive to make Pi more affordable so that a greater number of individuals can acquire and hold it, thereby fostering a thriving economic ecosystem. Of course, the rising price will build up merchants' confidence to accept it as payment. This is why we refer to it as a buyback campaign, which aims to achieve mass adoption and foster ecosystem confidence. As Pi evolves into a currency, the question of its value becomes pertinent. Given that it is a new currency, its value is not immediately clear. This presents an opportunity for us, the pioneers, to play a crucial role in defining it. The determination of Pi's value is not the responsibility of a central authority such as CT, the government, or the exchange. Instead, it will emerge from a decentralized consensus within the community, which collectively owns Pi. This concept is akin to ancient times when the value of shells was not determined by the sellers. Rather, the value was derived from the collective agreement of the village that utilized them as currency. I hope this elaboration clarifies the distinction between value and price, enabling a deeper understanding of the foundational principles that drive our mission with Pi Network. Pi represents a groundbreaking innovation—a revolution that is poised for long-term economic development on a global scale, rather than perpetuating cycles of plunder and exploitation. By harnessing the power of blockchain technology, Pi empowers ordinary individuals, which creates an inherent conflict of interest with the U.S. government in the short term. Should the U.S. government endorse the Pi Network, it raises questions about the viability of U.S. treasuries and who would ultimately purchase them. Consequently, the government may prioritize support for stablecoins backed by the U.S. dollar and U.S. Treasury securities, as this can help alleviate the U.S. government's issues with limited demand. However, I previously mentioned the potential for Pi to emerge as an algorithmic stablecoin. At that time, the Genius Bill had not yet been enacted. If the Pi Network gains acceptance from the U.S. government, its growth could become rapid and expansive, leading to widespread adoption in other nations. This path would position Pi as a legitimate currency in nearly every country, contingent upon certain conditions. For instance, if the price of Pi in the exchange market can align with the GCV, this could be achieved through a buyback mechanism involving 10 million pioneers. Such a scenario would indicate that Pi differs significantly from past algorithmic stablecoin failures, presenting a compelling case for the U.S. government to view Pi as a low-risk asset. However, it presents a significant challenge to be collectively reached by pioneers, and there are other conditions that we cannot achieve in a short time. While it might appear that Pi Network conflicts with the U.S. dollar or stablecoins in the short term, it has the potential to address the broader issue of overprinting currency, which has plagued the U.S. and many other nations. This would benefit international trade by alleviating concerns about currency appreciation or depreciation in international transactions. The global economy indeed requires a super sovereign currency—one that ensures stability for future generations and fosters lasting peace and prosperity. To comprehend Pi as a currency, it is crucial to recognize that we must cultivate long-term value by generating GCV data. In the short term, our focus needs to be on establishing a robust exchange market and decentralized applications (DApps) to drive mass adoption. If this is understood, there should be no need to feel discouraged by the current low price of Pi. The true value of Pi as a currency derives not from the exchange market, trading platforms, or governmental endorsement, but rather from our community's collective efforts and engagement. You might wonder how a government could adopt Pi, given that it does not take the form of a stablecoin. I would counter with the example of Bitcoin, which has thrived even in environments where many countries have imposed bans. Currently, Pi is transitioning from its traditional commodity status to being recognized as a currency, meaning governmental awareness of Pi Network is still in development. As such, existing regulations generally pertain to older forms of cryptocurrency rather than our innovative approach. Our branding as a digital currency, rather than a cryptocurrency, is intentional. Dr. Nicolas has expressed concerns that many aspects of conventional cryptocurrencies pose challenges to government frameworks and public trust, often leading to economic harm rather than benefit. Our commitment to Know Your Customer (KYC) and Know Your Business (KYB) protocols distinguishes us by mitigating money laundering risks and protecting Pi holders from speculative practices. Many businesses face bankruptcy or closure because consumers lack the disposable income to engage in spending. Imagine how Pi could enable those businesses to survive and thrive—people could utilize Pi to make purchases and easily convert it into fiat currency to sustain operations, thereby preserving many jobs. The function in our wallet that allows users to "buy" Pi is not merely a feature; it represents a vision for the future where conversion to fiat currency can happen immediately, without dependency on third-party exchanges. Moving forward, we can establish a fixed rate (the GCV) for conversions. Once larger institutions and prominent companies recognize the low-risk profile of joining Pi Network due to its GCV stability, we can expect a considerable influx of participants seeking to gain a competitive advantage. You may ask how companies would finance the purchase of Pi at GCV rates. This is an insightful question. My perspective is that the demand for Pi’s stable value will inherently incentivize investments. Much like why individuals purchase stablecoins for their convenience in facilitating cross-border transactions, Pi will appeal to consumers and businesses alike, particularly because we are leveraging Web 3.0 blockchain technology, AI-driven platforms, and a rich ecosystem of decentralized applications (DApps). We are cultivating a loyal customer base that recognizes the value of this innovation. We understand that high-net-worth individuals seek safe investment opportunities. While U.S. treasury bonds currently represent a secure asset class, they are not without risk. Therefore, if Pi Network can maintain a limited supply coupled with blockchain technology and a consistent GCV, it is plausible that affluent investors would allocate a portion of their capital to acquire Pi. This would lead to fiat inflows whenever there is increased demand for Pi, establishing an equilibrium between Pi and fiat currencies. This interplay is why I believe DApps are critically significant. We need broader usage of Pi in real-world applications. I hope my analysis has helped clarify why the price of Pi should not overly concern us. Buying Pi to hold onto it allows pioneers to accumulate more, while building merchant confidence is essential to kickstart the ecosystem. Merchants will be motivated to see Pi’s price appreciation since this removes the risks for DApps and service providers who depend on exchange market prices. A rise in demand for Pi will subsequently reduce its supply, which is beneficial for price increases. I look forward to discussing Pi GCV army management in another session. Thank you for your time. Let’s continue striving for greatness together. Doris Yin 🪷🪷🪷 Founder, Global GCV Movement Disclaimer: This speech is intended solely for educational purposes within the GCV community. The views and content shared here represent my personal perspective and are part of the GCV movement, but do not reflect the official position of the Pi Core Team (PCT). Pi Network represents a new revolution, meaning there is no existing example for us to follow and no guiding manual. As Dr. Fan mentioned, we cannot predict what will happen around the next corner. Therefore, we must practice and forge our own path. As more people traverse this journey, the road will become clearer.
0:55

TOPIC #107: PI NETWORK IS A STABLE COIN? -WHO DECIDES PI FULLY OM FIXED VALUE? Dear GCV army, I hope you are all doing great! First of all, I would like to express my sincere gratitude for all your hard work. Many of you have achieved significant milestones, and it’s evident that you are making a great difference. Our influence has grown significantly, with an increasing number of social media posts and YouTubers publicly supporting us. I can see that more and more people are beginning to understand why we advocate for GCV. Today's meeting aims to alleviate any doubts you may have, allowing you to relax and feel confident as we embark on our historic journey together. I will answer the questions I’ve received and address some important issues we need to focus on to maintain our community's efficiency, particularly regarding our Generals, which will be the topic next weekend. I put the questions I received here. "A question addressed to Ms. Doris Yin in the emergency meeting 1– In light of the rapidly changing global circumstances and the increasing discussion about stablecoins backed by U.S. Treasury bonds, how do you see the future role of the Pi Network in this context? And what practical steps should the GCV army take now to accelerate this path? 2_ There are those who promote the idea that the price of Pi is what appears in the market (currently around $0.49) and compare it to the price of GCV within the ecosystem (314,159 Pi = 1 good or service). They say if Pi’s price rises to $2, it means that the value within The ecosystem is approximately 2 million dollars. With sincere appreciation and discipline." This is from the Arab head of GCV Ambassador Mr. Mohammed. Another question: "Hello, my Global Ambassador, I am Ateba Joseph, Ecological Ambassador in Cameroon And a member of the GCV army, I am delighted to exchange with you. Regarding the meeting with the GCV army on Sunday, July 27, 2025.. Here is my concern: A few days ago, a correspondence indicated that Pi is not or is not yet a stable coin. Upon reading this information, we have provided many explanations to help the pioneers understand this. I hope you will focus more on this statement to further strengthen our understanding of the subject. Thank you for taking my concerns into consideration" Thank you for the above questions; my answers are below. The first question concerns stablecoins. Many pioneers are hoping that Pi can be recognized by the U.S. government as a stablecoin. I wrote an article on this in May. On July 18, 2025, President Trump signed the Guiding and Establishing National Innovation for US Stablecoins Act (the GENIUS Act) into law. This legislation establishes a regulatory framework for payment stablecoins and marks the first federal legislation on digital assets enacted since President Trump issued an executive order aimed at making the U.S. the “crypto capital of the world.” U.S.-issued stablecoins are expected to become the primary means of dollar transactions globally, especially in emerging markets with unstable local currencies. The sponsors of the GENIUS Act estimate that by 2030, stablecoin issuers may collectively become the largest holders of U.S. Treasuries, surpassing foreign central banks. From this, we can see that U.S. stablecoins must maintain reserves backing outstanding payment stablecoins on a one-to-one basis, consisting only of specified assets, including U.S. dollars and short-term Treasury securities. It is clear that the Pi Network will not take this path, as it is not part of our plan. A stablecoin is essentially a digital representation of the U.S. dollar. All stablecoin issuers do not create a new currency; rather, it’s akin to purchasing chips at a casino – you must use U.S. dollars to buy those chips. However, Pi is a completely new currency. It does not need to be backed up by U.S. dollars or U.S. Treasuries to be used. If that were the case, we wouldn’t need to establish an ecosystem or have a three-year enclosed mainnet. I previously mentioned the possibility of Pi being an algorithmic stablecoin since only algorithmic stablecoins do not need to be backed by U.S. dollars. However, algorithmic stablecoins have faced significant failures in the past. The collapse of the Terra (LUNA) cryptocurrency resulted in a loss of at least $40 billion in market capitalization, with estimates reaching as high as $60 billion. TerraUSD (UST), an algorithmic stablecoin, lost its peg to the U.S. dollar, contributing to its overall collapse. The new stablecoin legislation recently passed through the Senate effectively ties the U.S. Treasury to crypto, as it essentially bets the government’s cash flow on digital tokens and market speculation. This legislation requires stablecoins to be backed by short-term Treasury bills, generating an estimated $2–$3 trillion in new demand for government debt, which is nearly half the current size of the T-bill market. On paper, this looks beneficial, but in reality, it creates a circular feedback loop: crypto demand fuels stablecoins, stablecoins buy T-bills, and T-bills fund government deficits. The government becomes reliant on speculative capital flows. Thus, we should understand why the U.S. government will not support the Pi Network as a stablecoin, as they require stablecoin issuers to buy T-bills and can no longer trust algorithmic stablecoins. So, what is the future of the Pi Network as a currency? From my perspective, Pi is already listed on exchange markets. It cannot be classified as a security because it is mined freely and is not an ICO. Instead, it should be categorized as a commodity, similar to Bitcoin and ETH. When a currency is listed for trading on an exchange, its price is determined by the balance of supply and demand. However, Pi is a currency in its own right; it has inherent value from Pi holders -Pioneers. Historically, currency has served as a medium of exchange. A medium of exchange is a widely accepted item for buying goods and services in an economy. It facilitates transactions by eliminating the need for a barter system, where goods are directly exchanged for other goods. In modern economies, money (such as currency) serves as the primary medium of exchange. **Functions of Money:** One of the core functions of money is to serve as a medium of exchange, enabling the smooth transfer of value between buyers and sellers, thereby simplifying trade and economic activity. **Examples:** In modern economies, this typically includes currency (paper money, coins) or digital money. In specific historical contexts, other items, such as cigarettes in prisoner-of-war camps, have also served as mediums of exchange. **Importance of Acceptance:** For a medium of exchange to function effectively, it must be widely accepted and trusted within the relevant community. **Not the Same as a Payment Method:** While credit cards and checks are used for payments, they do not serve as mediums of exchange themselves. Therefore, stablecoin is not a new currency. It is more likely to have a credit card or check character. It is a USD digital status. From the analysis presented, we can draw the following conclusions: The current price of Pi on the exchange market primarily serves as a temporary measure to facilitate broad expansion. While this is not our primary objective, it constitutes a strategic approach towards achieving our mission. To gain a clearer perspective, we must adopt a higher-level view of the overall vision for the Pi Network. The mission and vision of Pi Network clearly articulate that it is not intended to function as a commodity for sale, nor is it meant to be an investment vehicle or a speculative security. Instead, it is crucial to recognize that Pi is designed to be a medium of exchange—a new form of currency. As pioneers in this venture, we have the unique opportunity to acquire Pi through free mining. However, it is important to note that the current mining rate is relatively slow. To overcome this limitation and to further our goal of mass adoption, it is essential for more individuals to join the Pi Network and participate in holding Pi. One efficient way to accelerate this process is by allowing Pi to be traded on the exchange market, which can result in rapid and widespread adoption. Since Pi can be mined for free, a lower price could make it more accessible to a larger number of people. It's important to focus on our primary goal during this pre-full Open Mainnet (OM) phase: mass adoption, rather than aiming for high prices, which many pioneers expected. Some pioneers want to sell when the price increases, but if too many sell, it could undermine our goal of achieving mass adoption. This scenario is reminiscent of historical instances when shells served as currency—readily accessible from the sea or buy from the village market. For shells to function effectively as currency, a collective effort was needed to hold and circulate them within the village. If only a select few individuals possess the shells, the currency lacks the necessary circulation to sustain an economy. Hence, our goal should not be centered on achieving a high price; instead, we should strive to make Pi more affordable so that a greater number of individuals can acquire and hold it, thereby fostering a thriving economic ecosystem. Of course, the rising price will build up merchants' confidence to accept it as payment. This is why we refer to it as a buyback campaign, which aims to achieve mass adoption and foster ecosystem confidence. As Pi evolves into a currency, the question of its value becomes pertinent. Given that it is a new currency, its value is not immediately clear. This presents an opportunity for us, the pioneers, to play a crucial role in defining it. The determination of Pi's value is not the responsibility of a central authority such as CT, the government, or the exchange. Instead, it will emerge from a decentralized consensus within the community, which collectively owns Pi. This concept is akin to ancient times when the value of shells was not determined by the sellers. Rather, the value was derived from the collective agreement of the village that utilized them as currency. I hope this elaboration clarifies the distinction between value and price, enabling a deeper understanding of the foundational principles that drive our mission with Pi Network. Pi represents a groundbreaking innovation—a revolution that is poised for long-term economic development on a global scale, rather than perpetuating cycles of plunder and exploitation. By harnessing the power of blockchain technology, Pi empowers ordinary individuals, which creates an inherent conflict of interest with the U.S. government in the short term. Should the U.S. government endorse the Pi Network, it raises questions about the viability of U.S. treasuries and who would ultimately purchase them. Consequently, the government may prioritize support for stablecoins backed by the U.S. dollar and U.S. Treasury securities, as this can help alleviate the U.S. government's issues with limited demand. However, I previously mentioned the potential for Pi to emerge as an algorithmic stablecoin. At that time, the Genius Bill had not yet been enacted. If the Pi Network gains acceptance from the U.S. government, its growth could become rapid and expansive, leading to widespread adoption in other nations. This path would position Pi as a legitimate currency in nearly every country, contingent upon certain conditions. For instance, if the price of Pi in the exchange market can align with the GCV, this could be achieved through a buyback mechanism involving 10 million pioneers. Such a scenario would indicate that Pi differs significantly from past algorithmic stablecoin failures, presenting a compelling case for the U.S. government to view Pi as a low-risk asset. However, it presents a significant challenge to be collectively reached by pioneers, and there are other conditions that we cannot achieve in a short time. While it might appear that Pi Network conflicts with the U.S. dollar or stablecoins in the short term, it has the potential to address the broader issue of overprinting currency, which has plagued the U.S. and many other nations. This would benefit international trade by alleviating concerns about currency appreciation or depreciation in international transactions. The global economy indeed requires a super sovereign currency—one that ensures stability for future generations and fosters lasting peace and prosperity. To comprehend Pi as a currency, it is crucial to recognize that we must cultivate long-term value by generating GCV data. In the short term, our focus needs to be on establishing a robust exchange market and decentralized applications (DApps) to drive mass adoption. If this is understood, there should be no need to feel discouraged by the current low price of Pi. The true value of Pi as a currency derives not from the exchange market, trading platforms, or governmental endorsement, but rather from our community's collective efforts and engagement. You might wonder how a government could adopt Pi, given that it does not take the form of a stablecoin. I would counter with the example of Bitcoin, which has thrived even in environments where many countries have imposed bans. Currently, Pi is transitioning from its traditional commodity status to being recognized as a currency, meaning governmental awareness of Pi Network is still in development. As such, existing regulations generally pertain to older forms of cryptocurrency rather than our innovative approach. Our branding as a digital currency, rather than a cryptocurrency, is intentional. Dr. Nicolas has expressed concerns that many aspects of conventional cryptocurrencies pose challenges to government frameworks and public trust, often leading to economic harm rather than benefit. Our commitment to Know Your Customer (KYC) and Know Your Business (KYB) protocols distinguishes us by mitigating money laundering risks and protecting Pi holders from speculative practices. Many businesses face bankruptcy or closure because consumers lack the disposable income to engage in spending. Imagine how Pi could enable those businesses to survive and thrive—people could utilize Pi to make purchases and easily convert it into fiat currency to sustain operations, thereby preserving many jobs. The function in our wallet that allows users to "buy" Pi is not merely a feature; it represents a vision for the future where conversion to fiat currency can happen immediately, without dependency on third-party exchanges. Moving forward, we can establish a fixed rate (the GCV) for conversions. Once larger institutions and prominent companies recognize the low-risk profile of joining Pi Network due to its GCV stability, we can expect a considerable influx of participants seeking to gain a competitive advantage. You may ask how companies would finance the purchase of Pi at GCV rates. This is an insightful question. My perspective is that the demand for Pi’s stable value will inherently incentivize investments. Much like why individuals purchase stablecoins for their convenience in facilitating cross-border transactions, Pi will appeal to consumers and businesses alike, particularly because we are leveraging Web 3.0 blockchain technology, AI-driven platforms, and a rich ecosystem of decentralized applications (DApps). We are cultivating a loyal customer base that recognizes the value of this innovation. We understand that high-net-worth individuals seek safe investment opportunities. While U.S. treasury bonds currently represent a secure asset class, they are not without risk. Therefore, if Pi Network can maintain a limited supply coupled with blockchain technology and a consistent GCV, it is plausible that affluent investors would allocate a portion of their capital to acquire Pi. This would lead to fiat inflows whenever there is increased demand for Pi, establishing an equilibrium between Pi and fiat currencies. This interplay is why I believe DApps are critically significant. We need broader usage of Pi in real-world applications. I hope my analysis has helped clarify why the price of Pi should not overly concern us. Buying Pi to hold onto it allows pioneers to accumulate more, while building merchant confidence is essential to kickstart the ecosystem. Merchants will be motivated to see Pi’s price appreciation since this removes the risks for DApps and service providers who depend on exchange market prices. A rise in demand for Pi will subsequently reduce its supply, which is beneficial for price increases. I look forward to discussing Pi GCV army management in another session. Thank you for your time. Let’s continue striving for greatness together. Doris Yin 🪷🪷🪷 Founder, Global GCV Movement Disclaimer: This speech is intended solely for educational purposes within the GCV community. The views and content shared here represent my personal perspective and are part of the GCV movement, but do not reflect the official position of the Pi Core Team (PCT). Pi Network represents a new revolution, meaning there is no existing example for us to follow and no guiding manual. As Dr. Fan mentioned, we cannot predict what will happen around the next corner. Therefore, we must practice and forge our own path. As more people traverse this journey, the road will become clearer.

Doris Yin 东方紫莲🪷

17,742 Aufrufe • vor 1 Jahr

Futuristic dome grows food by itself – with help from some fish | Bronwyn Thompson, New Atlas Inspired by the humble old greenhouse, a futuristic self-contained food ecosystem was recently on display at Expo 2025 Osaka-Kansai in Japan, offering us a glimpse at a how we might one day have "farm to table" on our apartment block rooftops or in small urban spaces. Think of it as a tiny house of produce. Known as “Inochi no Izumi” (いのちの湧水), or "Source of Life," the dome became one of the standout pieces of ingenuity on show at the Osaka Health Pavilion on Yumeshima Island. The 6.4-m-high (21-ft), 7-m-diameter (23-ft) dome, built to symbolize the Earth, sits atop a base containing four different water zones that provide the living engine of this closed-loop ecoystem. The dome itself has a surface area of 128 sq m (1,378 sq ft), made up of transparent ethylene tetrafluoroethylene (ETFE) panels for the outer skin, supported by VikingDome's T-STAR system of 245 steel structural bars, connected with 76 joins on the sphere and 10 on the base. The entire unit, with materials transported to the site in three pallets, weighs a massive 2,111 kg (4,655 lb). The dome itself is designed to maximize sunlight and maintain a stable internal climate – but the real innovation is found inside it. A horizontally divided aquatic base supports four vertically stacked plant layers. Rather than a conventional floor-by-floor layout, the dome houses a kind of ecological cross-section, where different salinity zones and their matching crops work together in a regulated loop. At the base are four compartments housing seawater, brackish water and two freshwater tanks. Each supports aquatic species adapted to those conditions, from marine fish and shellfish to carp-like freshwater species. These animals form the starting point of the nutrient cycle crucial for the life above. As they excrete ammonia-rich waste, specialized microbes convert it first into nitrites and then into plant-usable nitrates. Directly above the tanks, four matching bands of hydroponic growing beds rise in a tiered column. Salt-tolerant plants, such as halophytes, grow above the seawater tank, thriving on nutrient-rich water that would kill conventional vegetables. Sea grapes grow in the water, while sea purslane and sea asparagus grow above the tank housed with red seabream, black porgy, red-spotted grouper and Malabar grouper. Then, semi-salt-tolerant vegetables plants like tomato sit above the brackish-water zone, which houses Japanese pufferfish and ornamental carp. Above one of the freshwater systems are “functional vegetables” – greens like herbs and lettuces with concentrated nutrients that do particularly well in controlled hydroponic conditions. Their water comes from the tank occupied by sturgeon. The beds can also be rotated horizontally via a built-in motor. At the very top, on the fourth tier, are rows of edible flowers including nasturtium, bougainvillea and marigold. Water from each tank is pumped upward to irrigate its corresponding plant zone. As the plants remove nitrogen compounds and other nutrients, the purified water flows back into each aquatic zone below. As such, the system produces next to no waste – a cyclic ecosystem modeled on nature (like wetlands cycling, for example) but optimized for human food production. Developed in collaboration with Osaka Metropolitan University’s Plant Factory R&D Center and the Tokyo University of Marine Science and Technology, the system demonstrates how future cities could grow food in such compact, resource-efficient ecosystems. It also highlights a different kind of biodiversity – that of agriculture. The broader the range of usable plant and aquatic species, the more resilient – and self-sufficient – the system becomes. While the Source of Life was indeed an exhibition, so there are some practical questions about the fish housed in the tanks at the base, it nonetheless demonstrated how natural recycling can help to produce food and reduce waste, and it's not a stretch to imagine this modular aquaponics system becoming a reality on rooftops, in dense urban areas or in land-poor regions where farming is limited. In disaster-prone areas, closed-loop systems like this could offer stable, decentralized food production independent of soil quality or access to large supplies of water. It's also a clever look at how sustainable agriculture doesn’t always require new technologies so much as a deeper understanding of ecology – something that's often lost in agribusiness and large-scale commercial farming. As we look for low-emission, sustainable ways to feed growing urban populations, this dome proposes that one answer may lie in us better harnessing the power of nature. Read more:
0:43

Futuristic dome grows food by itself – with help from some fish | Bronwyn Thompson, New Atlas Inspired by the humble old greenhouse, a futuristic self-contained food ecosystem was recently on display at Expo 2025 Osaka-Kansai in Japan, offering us a glimpse at a how we might one day have "farm to table" on our apartment block rooftops or in small urban spaces. Think of it as a tiny house of produce. Known as “Inochi no Izumi” (いのちの湧水), or "Source of Life," the dome became one of the standout pieces of ingenuity on show at the Osaka Health Pavilion on Yumeshima Island. The 6.4-m-high (21-ft), 7-m-diameter (23-ft) dome, built to symbolize the Earth, sits atop a base containing four different water zones that provide the living engine of this closed-loop ecoystem. The dome itself has a surface area of 128 sq m (1,378 sq ft), made up of transparent ethylene tetrafluoroethylene (ETFE) panels for the outer skin, supported by VikingDome's T-STAR system of 245 steel structural bars, connected with 76 joins on the sphere and 10 on the base. The entire unit, with materials transported to the site in three pallets, weighs a massive 2,111 kg (4,655 lb). The dome itself is designed to maximize sunlight and maintain a stable internal climate – but the real innovation is found inside it. A horizontally divided aquatic base supports four vertically stacked plant layers. Rather than a conventional floor-by-floor layout, the dome houses a kind of ecological cross-section, where different salinity zones and their matching crops work together in a regulated loop. At the base are four compartments housing seawater, brackish water and two freshwater tanks. Each supports aquatic species adapted to those conditions, from marine fish and shellfish to carp-like freshwater species. These animals form the starting point of the nutrient cycle crucial for the life above. As they excrete ammonia-rich waste, specialized microbes convert it first into nitrites and then into plant-usable nitrates. Directly above the tanks, four matching bands of hydroponic growing beds rise in a tiered column. Salt-tolerant plants, such as halophytes, grow above the seawater tank, thriving on nutrient-rich water that would kill conventional vegetables. Sea grapes grow in the water, while sea purslane and sea asparagus grow above the tank housed with red seabream, black porgy, red-spotted grouper and Malabar grouper. Then, semi-salt-tolerant vegetables plants like tomato sit above the brackish-water zone, which houses Japanese pufferfish and ornamental carp. Above one of the freshwater systems are “functional vegetables” – greens like herbs and lettuces with concentrated nutrients that do particularly well in controlled hydroponic conditions. Their water comes from the tank occupied by sturgeon. The beds can also be rotated horizontally via a built-in motor. At the very top, on the fourth tier, are rows of edible flowers including nasturtium, bougainvillea and marigold. Water from each tank is pumped upward to irrigate its corresponding plant zone. As the plants remove nitrogen compounds and other nutrients, the purified water flows back into each aquatic zone below. As such, the system produces next to no waste – a cyclic ecosystem modeled on nature (like wetlands cycling, for example) but optimized for human food production. Developed in collaboration with Osaka Metropolitan University’s Plant Factory R&D Center and the Tokyo University of Marine Science and Technology, the system demonstrates how future cities could grow food in such compact, resource-efficient ecosystems. It also highlights a different kind of biodiversity – that of agriculture. The broader the range of usable plant and aquatic species, the more resilient – and self-sufficient – the system becomes. While the Source of Life was indeed an exhibition, so there are some practical questions about the fish housed in the tanks at the base, it nonetheless demonstrated how natural recycling can help to produce food and reduce waste, and it's not a stretch to imagine this modular aquaponics system becoming a reality on rooftops, in dense urban areas or in land-poor regions where farming is limited. In disaster-prone areas, closed-loop systems like this could offer stable, decentralized food production independent of soil quality or access to large supplies of water. It's also a clever look at how sustainable agriculture doesn’t always require new technologies so much as a deeper understanding of ecology – something that's often lost in agribusiness and large-scale commercial farming. As we look for low-emission, sustainable ways to feed growing urban populations, this dome proposes that one answer may lie in us better harnessing the power of nature. Read more:

Owen Gregorian

43,956 Aufrufe • vor 8 Monaten

Hey True Earthers... If you get tired of globers bitching about a model, or sunrise angles, or star trails, or sunlight, or eclipses, anyone can ALWAYS reference THIS MODEL The reason it is called "Shane's Mode;" is strictly so YOU can use it, and I can take all the criticism, insults, ridicule, jokes, attacks, etc. The general idea is that the community gets the considerable benefit of presenting an accurate model and using it to explain several normal phenomena at once. Then, only I get the drawbacks of all that will surely come from it, and everyone else will benefit. I planned it this way, because I largely don't care about what any of the globers piling the hate over here so we can press forward. Or.. you know, f*ck me for saying the word model, and for bendy light or for whatever. If that's the case, no hard feelings. One last thing, the smaller dome in the model simply represents the limit of an observers view, a spherical limit with a radius of 3959. The math that supports that is here... and here. The descriptions are entirely reworked, mostly spelling error free, and entirely plausible. So feel free to bring it up in debates, forums, streams, podcasts, or whatever you like. The model adequately emulates and explains all of these observations: Sunrise, Sunset, Moonrise, Moonset, Moon Phases, Moon's apparent rotation, Sun's position on Equinox, Seasons, some aspects of Solar and Lunar Eclipses, Star trails, 24 hours Day/Night at the North-pole and Antarctica, Celestial Poles, Why people south of the equator can see the same Stars rotate clockwise around a singe celestial pole at the same time at different continents [Southern Cross Observations] Cheers everyone! The FULL Description is below, and it is LONG. Sorry. The Model This model does not assume a physical Sun nor Moon which will show a collective convergence for every observer on Earth. It only matches their apparent positions as observed across the plane. The Bislin model acknowledges this and moves all celestial bodies to a nearly infinite distance away. This does nothing more than create a triangle large enough that you can mathematically abstract your way into the inverse of everything you experience. The truth is there is a limit to one's visual space. And this limit is necessarily geometrically spherical. Because one never observes objects in anything but their 'apparent location' within one's personal celestial sphere, there is no need to explain a tiny ball of heat mysteriously powering itself along at 3100 miles above the plane. This is not reality. We feel we only have to model the exact apparent position for each observer. We do not have to provide an explanation for what you think should be required. This model relays the apparent size and positions of Sun, Moon and star constellations. It depicts their paths as well as the day-night terminator. Simply by observing reality and plotting that data on a planar map we demonstrate that the Sun, Moon and stars can move beyond the limit of one's vision and become unresolvable by the naked eye. We show how this can be conflated with the assertion that objects ACTUALLY drop down below the horizon when, in reality, they are only apparently dipping below the horizon when exceed limit of your vision. It is elegantly simple and easy to understand without the bullshit. Sun/Moon tracks: In 24 hours, the fixed stars rotate about 1 degree more than 360 degrees so that, in 365.25 days, the star constellations return to the same place in the sky. This is seen by incrementally advancing DayOfYear (click the field and use Arrow Up or Down). The Dome grid will advance each day by about 1 degree. Advance the time in 24 hours steps and the Sun noticeably moves between the Solstice lines. The Sun will also trace a figure 8. This is caused by the Sun's Ecliptic plane at 23.44 degrees to the orbital plane. The paths of the Sun and Moon are visible against the fixed star background (Dome Grid) by checking the options Sun track and Moon track. For a description of the tracks, click the Eclipses button. They correspond to observable reality. The tracks are derived from the solar and lunar cycles and are absolutely not exclusive to either model. It would be extremely dishonest to claim anything else. Sorry, Walter. Retrograde Motion of the Moon's track: The Sun's path stays fixed on the Dome Grid. But, the Moon's path slowly rotates retrograde against the Dome Grid and rotates one full rotation in 6,798 days. This is due to the oscillation and intersection of the Moon's orbit caused by the distant Sun. Currently, the Moon Ecliptic is such that the path of the Moon extends the path of the Sun, North/South, by about five degrees. Approximately 3,400 days later, the path of the Moon moves inside the path of the Sun by about 5 degrees. This observation is simply translated to the planar model. Eclipses: The intersection points of the Sun and Moon's paths are called Knots. Two Knots are marked by a green dot. If the Sun and Moon are on two opposing Knots, a Lunar Eclipse occurs. The Sun and Moon on the same Knot will result in a Solar Eclipse (play Demo Eclipses from Step 6 on). This Flat Earth model can predict Solar and Lunar Eclipses. It can also absolutely predict the optical effect conflated with the Moon's alleged shadow on Earth during a Solar Eclipses or vice versa. It uses a ratio of the cycle that is based on the radius of a shadow, as postulated by Phillippe de La Hire, in the 1700s. It was first calculated for a Lunar Eclipse. But, the ratio applies to all future eclipses which belong to an appropriate series. This ratio is then applied to the predicted path to dynamically widen or shorten the path in order to accommodate the penumbral and umbral radial intersection as a visible sphere on the plane. We then apply this integer as a scalar to correctly approximate the size of the optical effects conflated with shadows. All of the maps onto which the eclipse can be projected use the same globular coordinate system, unfortunately. Now, it can be shown that heliocentrism cannot predict eclipses at all. They can only interpret the cycle data in the same way the ancients did and apply more refined mathematics. Moon Phases and Orientation: The model shows the Moon phases and the orientation of the Moon with respect to the Observer's horizon. The apparent rotation of the Moon during the day is due to the fact that the camera's up vector remains perpendicular to the surface of Earth while following the path of the Moon. This perfectly matches reality. Equinox: This model produces the correct apparent Sun positions during an Equinox. The Sun rises due East at 6:00 AM and sets due West at 6:00 PM. Poles: This model produces a 24 hour day and night on the North Pole and in Antarctica. Heliocentric Model: Simple observations mathematically translated to this planar projection perfectly map the paths of the Sun, Moon and stars (star trails) as they appear to the Observer inside their personal celestial sphere. As with all other celestial observations, the Equinox, the Solstice Knots and the Day-Night terminator can be derived from basic observation and data applied to the planar projection. No need for baseless assumption. The Heliocentric model utterly fails here. Newton's laws can be reduced to exclude mass and still manage to describe the same periodicity and, thusly, the same relationship. No need for an exclusivity claim here at all, is there, Walter? Shapes on the Dome: The shape of Sun, Moon and star constellations appear on the personal celestial sphere exactly as they do in reality, and when projected onto the globe. Again, because we invoke the same radius to describe the spherical limit of our celestial view, the very same observations become easily explainable when using all of the normal conventions, with no need to invent branches of physics and invert reality. All features of this model are derived only from observations of the sky. Observations of the sky have always been kinematically equivalent - equally applicable to geocentric and heliocentric model. This was rather the point of the invention of Special and General Relativity (nonsense). Problems with the Shane's Flat Earth Model Distances: Many people misunderstand distances on map projections. On the AE map, distances measured in an exactly North-South direction are correct. Other measurements are also proportionately correct. Data translation between projections is tied to the coordinates we use. The longitude and latitude we use in any of the appropriate 200 map projections will ensure the distances between those points remain accounted for, at scale. Please learn how map scaling works if this seems inadequate to you. Only an absolute moron would expect visual distance to be equal in an equal area, or equal distance, cartographic transformation. Right, Walter? Personal Celestial Sphere: The Sun and Moon trace specific paths across the celestial sphere. The paths of the celestial bodies are directly mapped from observation to the planar projection. They also follow the cycle of the Heavens, with no need for gravity, Newton, nor the very lackluster performance of gravity based predictions of systems with 2 or more bodies. It was jaw dropping to see that poor Walter actually wrote that gravity caused this. I assume it was because he knew he would never have to answer any challenges. Show me the math which uses the gravitation from all of the forces Walter listed and I will immediately remove this section. Moon Phases and Field Rotation: Moon phase and apparent orientation, as shown, perfectly represent what every observer on Earth sees, correct to their location. The 15 year solar cycle and the 18 (10/11) month lunar cycle have been understood for so long that people eventually forgot and are now incorrectly perceive their paths. Only in modernity do the vast majority of people wander about under their own personal clock without the ability to read it. How sad. The Day/Night Terminator: The shape that matches reality is a bit peculiar and it changes over the course of a year. The shape not only depends on the location of the Sun but its height and speed as well. Again, we know the Sun circles the plane at a 23.4 degree tilt. And this perfectly defines the terminator line. There is absolutely no reason to invoke bendy light in order to explain any of these observations. The model simply matches what we see. It represents reality. Missing The Third Dimension: We need to correct the inherent misunderstanding in the assumption of the physicality of any 'dome'. Modeled here is a personal celestial sphere. It uses a radius. It just so happens that Shane has been arguing this concept and this radius since the day he showed Walter Bislan's model as evidence, amid the jeers of the uneducated masses. As it turns out, the personal celestial sphere is a visual limit imposed on one's spherical view of the heavens. It most simply describes the particular visible slice of the heavens. And it moves that amount with you where ever you go. This is such an elegant, beautiful explanation to what had been perplexing the flat Earth community for years: how the stars work. The personal celestial sphere, once properly understood, is a perfect explanation for everything we see in the sky. It explains the curved nature of the arcs of summer and winter, the behaviors of the Sun and Moon, as well as the apparent non movement of the static stars in relation to each other. Every single stellar observation is explained as well as, if not better than, any Heliocentric explanation. Any person who incorrectly assumes a visual distance scale also assumes things to be visually identical in size and demonstrates a massive misunderstanding of proper distance scaling inherent in all map projections - particularly in the AE map. It's as if everyone has forgotten that the AE map is equal to the Globe map, which is also equal to 199 other map projections. The choice of projection does not matter. They are all the same. They all represent the same distances. We can make predictions based on cycles as well as the next guy. So, we wont need help there. As we keep saying, every observation in the sky is equal between geocentric and heliocentric perspectives. People seem to be INTENTIONALLY misunderstanding that, at this point. Light-Bending: absolutely not required in any way shape nor form. Observable reality matches the model in every way; I cannot imagine a better fit. To now try to invent a need for bendy light would only publicly highlight the ineptitude of a lower tier glober - and their inability to learn and adapt, a vital skill in these times. Our model perfectly represents azimuth and elevation of every celestial object in its apparent position. This is all that we ever see. There is no need to explain what has never been observed. The visualization of the South Pole in action is actually what brought Shane to the ultimate understanding of the celestial wheels. So, thank you again, Walter! Light Bending Over Night-Shadow: to match the 24 hour Daylight in Antarctica data from the light forms a shape congruent to a coffee cup caustic effect. Shadows Of Eclipses: although this model can predict the date of Eclipses, it was argued that it can be used for nothing else. Please check the provided links to review the absurdity of those claims. Conclusion Some observations, like the positions of the Sun, Moon and Star Constellations as well as Sun/Moon-rise/set can be explained by a Flat Earth Model - if we allow ourselves to adhere to the mathematical principle of equivalence. What a concession. Some final thoughts: 1) Distances on the AE Map are 100% 1:1 equivalent when you comprehend how to accordingly use the scale provided with the ruler which represents longitude. 2) LEARN ABOUT MAPS. Hopefully, the covariant scaling and lossless unlimited translations between the projections will teach you this valuable lesson. Equinox, Solstice, Azimuth, Elevation This model draws a perfectly circular orbit of the Earth around the Sun and a perfectly circular orbit of the Moon around the globe Earth. This is because the planar Earth has no moronic need for elipicity because they didn't back themselves into a logical corner by making shit up. This model chooses to match: Spring Equinox at 12:00 UT, March 20, 2017 Solar Eclipse at 18:00 UTC, August 21, 2017 Azimuth and Elevation of the Sun and Moon are also slightly inaccurate (according to the assumed Heliocentric requirement) due to the use of circular instead of elliptical orbits. This affects also Moon phase. Computing Day-Night Terminator The Day-Night terminator is derived from to match reality as follows: 1. A circle perpendicular to the Earth-Sun axis in the Sun coordinate system is computed depending on the Sun's position at a point in time relative to the intersection knot of the Equatorial plane of the Earth and the ecliptic plane of the Sun. This is entirely possible in both models. 2. This circle is then transformed to the globe Earth coordinate system. There is no way around using this coordinate system. If Walter Bislan comes asking for his source code, tell him thank again, from shane. Any questions can be sent to Shane_St_Pierre@protonmail.com
12:25

Hey True Earthers... If you get tired of globers bitching about a model, or sunrise angles, or star trails, or sunlight, or eclipses, anyone can ALWAYS reference THIS MODEL The reason it is called "Shane's Mode;" is strictly so YOU can use it, and I can take all the criticism, insults, ridicule, jokes, attacks, etc. The general idea is that the community gets the considerable benefit of presenting an accurate model and using it to explain several normal phenomena at once. Then, only I get the drawbacks of all that will surely come from it, and everyone else will benefit. I planned it this way, because I largely don't care about what any of the globers piling the hate over here so we can press forward. Or.. you know, f*ck me for saying the word model, and for bendy light or for whatever. If that's the case, no hard feelings. One last thing, the smaller dome in the model simply represents the limit of an observers view, a spherical limit with a radius of 3959. The math that supports that is here... and here. The descriptions are entirely reworked, mostly spelling error free, and entirely plausible. So feel free to bring it up in debates, forums, streams, podcasts, or whatever you like. The model adequately emulates and explains all of these observations: Sunrise, Sunset, Moonrise, Moonset, Moon Phases, Moon's apparent rotation, Sun's position on Equinox, Seasons, some aspects of Solar and Lunar Eclipses, Star trails, 24 hours Day/Night at the North-pole and Antarctica, Celestial Poles, Why people south of the equator can see the same Stars rotate clockwise around a singe celestial pole at the same time at different continents [Southern Cross Observations] Cheers everyone! The FULL Description is below, and it is LONG. Sorry. The Model This model does not assume a physical Sun nor Moon which will show a collective convergence for every observer on Earth. It only matches their apparent positions as observed across the plane. The Bislin model acknowledges this and moves all celestial bodies to a nearly infinite distance away. This does nothing more than create a triangle large enough that you can mathematically abstract your way into the inverse of everything you experience. The truth is there is a limit to one's visual space. And this limit is necessarily geometrically spherical. Because one never observes objects in anything but their 'apparent location' within one's personal celestial sphere, there is no need to explain a tiny ball of heat mysteriously powering itself along at 3100 miles above the plane. This is not reality. We feel we only have to model the exact apparent position for each observer. We do not have to provide an explanation for what you think should be required. This model relays the apparent size and positions of Sun, Moon and star constellations. It depicts their paths as well as the day-night terminator. Simply by observing reality and plotting that data on a planar map we demonstrate that the Sun, Moon and stars can move beyond the limit of one's vision and become unresolvable by the naked eye. We show how this can be conflated with the assertion that objects ACTUALLY drop down below the horizon when, in reality, they are only apparently dipping below the horizon when exceed limit of your vision. It is elegantly simple and easy to understand without the bullshit. Sun/Moon tracks: In 24 hours, the fixed stars rotate about 1 degree more than 360 degrees so that, in 365.25 days, the star constellations return to the same place in the sky. This is seen by incrementally advancing DayOfYear (click the field and use Arrow Up or Down). The Dome grid will advance each day by about 1 degree. Advance the time in 24 hours steps and the Sun noticeably moves between the Solstice lines. The Sun will also trace a figure 8. This is caused by the Sun's Ecliptic plane at 23.44 degrees to the orbital plane. The paths of the Sun and Moon are visible against the fixed star background (Dome Grid) by checking the options Sun track and Moon track. For a description of the tracks, click the Eclipses button. They correspond to observable reality. The tracks are derived from the solar and lunar cycles and are absolutely not exclusive to either model. It would be extremely dishonest to claim anything else. Sorry, Walter. Retrograde Motion of the Moon's track: The Sun's path stays fixed on the Dome Grid. But, the Moon's path slowly rotates retrograde against the Dome Grid and rotates one full rotation in 6,798 days. This is due to the oscillation and intersection of the Moon's orbit caused by the distant Sun. Currently, the Moon Ecliptic is such that the path of the Moon extends the path of the Sun, North/South, by about five degrees. Approximately 3,400 days later, the path of the Moon moves inside the path of the Sun by about 5 degrees. This observation is simply translated to the planar model. Eclipses: The intersection points of the Sun and Moon's paths are called Knots. Two Knots are marked by a green dot. If the Sun and Moon are on two opposing Knots, a Lunar Eclipse occurs. The Sun and Moon on the same Knot will result in a Solar Eclipse (play Demo Eclipses from Step 6 on). This Flat Earth model can predict Solar and Lunar Eclipses. It can also absolutely predict the optical effect conflated with the Moon's alleged shadow on Earth during a Solar Eclipses or vice versa. It uses a ratio of the cycle that is based on the radius of a shadow, as postulated by Phillippe de La Hire, in the 1700s. It was first calculated for a Lunar Eclipse. But, the ratio applies to all future eclipses which belong to an appropriate series. This ratio is then applied to the predicted path to dynamically widen or shorten the path in order to accommodate the penumbral and umbral radial intersection as a visible sphere on the plane. We then apply this integer as a scalar to correctly approximate the size of the optical effects conflated with shadows. All of the maps onto which the eclipse can be projected use the same globular coordinate system, unfortunately. Now, it can be shown that heliocentrism cannot predict eclipses at all. They can only interpret the cycle data in the same way the ancients did and apply more refined mathematics. Moon Phases and Orientation: The model shows the Moon phases and the orientation of the Moon with respect to the Observer's horizon. The apparent rotation of the Moon during the day is due to the fact that the camera's up vector remains perpendicular to the surface of Earth while following the path of the Moon. This perfectly matches reality. Equinox: This model produces the correct apparent Sun positions during an Equinox. The Sun rises due East at 6:00 AM and sets due West at 6:00 PM. Poles: This model produces a 24 hour day and night on the North Pole and in Antarctica. Heliocentric Model: Simple observations mathematically translated to this planar projection perfectly map the paths of the Sun, Moon and stars (star trails) as they appear to the Observer inside their personal celestial sphere. As with all other celestial observations, the Equinox, the Solstice Knots and the Day-Night terminator can be derived from basic observation and data applied to the planar projection. No need for baseless assumption. The Heliocentric model utterly fails here. Newton's laws can be reduced to exclude mass and still manage to describe the same periodicity and, thusly, the same relationship. No need for an exclusivity claim here at all, is there, Walter? Shapes on the Dome: The shape of Sun, Moon and star constellations appear on the personal celestial sphere exactly as they do in reality, and when projected onto the globe. Again, because we invoke the same radius to describe the spherical limit of our celestial view, the very same observations become easily explainable when using all of the normal conventions, with no need to invent branches of physics and invert reality. All features of this model are derived only from observations of the sky. Observations of the sky have always been kinematically equivalent - equally applicable to geocentric and heliocentric model. This was rather the point of the invention of Special and General Relativity (nonsense). Problems with the Shane's Flat Earth Model Distances: Many people misunderstand distances on map projections. On the AE map, distances measured in an exactly North-South direction are correct. Other measurements are also proportionately correct. Data translation between projections is tied to the coordinates we use. The longitude and latitude we use in any of the appropriate 200 map projections will ensure the distances between those points remain accounted for, at scale. Please learn how map scaling works if this seems inadequate to you. Only an absolute moron would expect visual distance to be equal in an equal area, or equal distance, cartographic transformation. Right, Walter? Personal Celestial Sphere: The Sun and Moon trace specific paths across the celestial sphere. The paths of the celestial bodies are directly mapped from observation to the planar projection. They also follow the cycle of the Heavens, with no need for gravity, Newton, nor the very lackluster performance of gravity based predictions of systems with 2 or more bodies. It was jaw dropping to see that poor Walter actually wrote that gravity caused this. I assume it was because he knew he would never have to answer any challenges. Show me the math which uses the gravitation from all of the forces Walter listed and I will immediately remove this section. Moon Phases and Field Rotation: Moon phase and apparent orientation, as shown, perfectly represent what every observer on Earth sees, correct to their location. The 15 year solar cycle and the 18 (10/11) month lunar cycle have been understood for so long that people eventually forgot and are now incorrectly perceive their paths. Only in modernity do the vast majority of people wander about under their own personal clock without the ability to read it. How sad. The Day/Night Terminator: The shape that matches reality is a bit peculiar and it changes over the course of a year. The shape not only depends on the location of the Sun but its height and speed as well. Again, we know the Sun circles the plane at a 23.4 degree tilt. And this perfectly defines the terminator line. There is absolutely no reason to invoke bendy light in order to explain any of these observations. The model simply matches what we see. It represents reality. Missing The Third Dimension: We need to correct the inherent misunderstanding in the assumption of the physicality of any 'dome'. Modeled here is a personal celestial sphere. It uses a radius. It just so happens that Shane has been arguing this concept and this radius since the day he showed Walter Bislan's model as evidence, amid the jeers of the uneducated masses. As it turns out, the personal celestial sphere is a visual limit imposed on one's spherical view of the heavens. It most simply describes the particular visible slice of the heavens. And it moves that amount with you where ever you go. This is such an elegant, beautiful explanation to what had been perplexing the flat Earth community for years: how the stars work. The personal celestial sphere, once properly understood, is a perfect explanation for everything we see in the sky. It explains the curved nature of the arcs of summer and winter, the behaviors of the Sun and Moon, as well as the apparent non movement of the static stars in relation to each other. Every single stellar observation is explained as well as, if not better than, any Heliocentric explanation. Any person who incorrectly assumes a visual distance scale also assumes things to be visually identical in size and demonstrates a massive misunderstanding of proper distance scaling inherent in all map projections - particularly in the AE map. It's as if everyone has forgotten that the AE map is equal to the Globe map, which is also equal to 199 other map projections. The choice of projection does not matter. They are all the same. They all represent the same distances. We can make predictions based on cycles as well as the next guy. So, we wont need help there. As we keep saying, every observation in the sky is equal between geocentric and heliocentric perspectives. People seem to be INTENTIONALLY misunderstanding that, at this point. Light-Bending: absolutely not required in any way shape nor form. Observable reality matches the model in every way; I cannot imagine a better fit. To now try to invent a need for bendy light would only publicly highlight the ineptitude of a lower tier glober - and their inability to learn and adapt, a vital skill in these times. Our model perfectly represents azimuth and elevation of every celestial object in its apparent position. This is all that we ever see. There is no need to explain what has never been observed. The visualization of the South Pole in action is actually what brought Shane to the ultimate understanding of the celestial wheels. So, thank you again, Walter! Light Bending Over Night-Shadow: to match the 24 hour Daylight in Antarctica data from the light forms a shape congruent to a coffee cup caustic effect. Shadows Of Eclipses: although this model can predict the date of Eclipses, it was argued that it can be used for nothing else. Please check the provided links to review the absurdity of those claims. Conclusion Some observations, like the positions of the Sun, Moon and Star Constellations as well as Sun/Moon-rise/set can be explained by a Flat Earth Model - if we allow ourselves to adhere to the mathematical principle of equivalence. What a concession. Some final thoughts: 1) Distances on the AE Map are 100% 1:1 equivalent when you comprehend how to accordingly use the scale provided with the ruler which represents longitude. 2) LEARN ABOUT MAPS. Hopefully, the covariant scaling and lossless unlimited translations between the projections will teach you this valuable lesson. Equinox, Solstice, Azimuth, Elevation This model draws a perfectly circular orbit of the Earth around the Sun and a perfectly circular orbit of the Moon around the globe Earth. This is because the planar Earth has no moronic need for elipicity because they didn't back themselves into a logical corner by making shit up. This model chooses to match: Spring Equinox at 12:00 UT, March 20, 2017 Solar Eclipse at 18:00 UTC, August 21, 2017 Azimuth and Elevation of the Sun and Moon are also slightly inaccurate (according to the assumed Heliocentric requirement) due to the use of circular instead of elliptical orbits. This affects also Moon phase. Computing Day-Night Terminator The Day-Night terminator is derived from to match reality as follows: 1. A circle perpendicular to the Earth-Sun axis in the Sun coordinate system is computed depending on the Sun's position at a point in time relative to the intersection knot of the Equatorial plane of the Earth and the ecliptic plane of the Sun. This is entirely possible in both models. 2. This circle is then transformed to the globe Earth coordinate system. There is no way around using this coordinate system. If Walter Bislan comes asking for his source code, tell him thank again, from shane. Any questions can be sent to [email protected]

Shane St Pierre

90,809 Aufrufe • vor 2 Jahren

Fiber-Optic Drones: Russia's Game-Changing Leap in Precision Warfare As a seasoned analyst of modern conflict dynamics, particularly of the SMO, I must commend the ingenuity of Russian military engineers in unveiling the latest iteration of fiber-optic guided drones— dubbed "ghost lines" in operational circles. These uncrewed aerial vehicles, boasting an operational radius of NOW up to 50 kilometers, represent a paradigm shift away from the chaotic, short-leashed FPV (First Person View) kamikaze drones that have dominated low-intensity skirmishes. Russia's fiber-optic drones, tethered by unbreakable spools of high-strength optical cable, deliver surgical strikes with the reliability of a scalpel, rendering the FPV model obsolete in any theater demanding endurance & precision. The paramount advantage lies in absolute immunity to electronic countermeasures—an Achilles' heel that has hobbled FPV drones since their proliferation. FPV operators, reliant on vulnerable radio frequencies, watch their feeds dissolve into static under a barrage of jamming systems. In contrast, fiber-optic drones transmit crystal-clear, uncompressed video & control signals through a physical conduit, impervious to spectrum saturation or directional jamming. This tethering allows for real-time, high-definition reconnaissance & targeting over 50 kilometers—5 to ten 10 the effective range of FPV units, which gasp out at 5-10 kilometers under ideal conditions. Imagine a Ukrainian forward position, smug in its drone-denial bubble: a Russian fiber-optic bird uncoils its 50-km lifeline from a concealed launch site, slithering through valleys & over treelines undetected, delivering a tandem warhead to the heart of command nodes without a whisper of electromagnetic betrayal. Precision targeting emerges as another decisive edge. FPV drones, piloted by adrenaline-fueled amateurs via twitchy goggles, suffer from latency-induced wobbles & human error, often veering off-course into harmless soil or self-destructing prematurely. Fiber-optic systems, however, integrate inertial navigation and AI-assisted guidance along the cable's data stream, achieving sub-meter accuracy even in GPS-denied environments. This enables loitering munitions to hover indefinitely—up to hours, limited only by fuel—scanning for high-value targets like Leopard tanks or HIMARS launchers before unleashing payloads of 5-10 kilograms of thermobaric fury. In recent field tests along the Donbass front, these drones have neutralized entrenched artillery batteries at standoff distances, preserving Russian infantry from the ambushes that FPV swarms provoke in close-quarters brawls. Logistically, the fiber-optic design outshines its wireless kin. Compact spools weighing under 2 kilograms, deployable from standard infantry backpacks or vehicle mounts, with modular warheads interchangeable for anti-personnel, anti-armor, or electronic disruption roles. Maintenance is trivial—no finicky antennas to calibrate—& production scales effortlessly in Urals factories, churning out 1000s monthly at costs competitive with FPV disposables, yet with reusable launch platforms for sustained ops. Critics may whine about the cable's vulnerability to snags or cuts, but this is a red herring peddled by those unfamiliar with tactical deployment. Routed low & fast, the fiber-optic line mimics a serpent's trail, evading small-arms fire &shrapnel that shreds FPV airframes In urban sieges like in Artyomovsk, where FPV duels devolve into mutual attrition, fiber-optic drones dictate the tempo, striking from afar while adversaries exhaust their short-ranged arsenals in futile pursuit. These 50-km phantoms are asymmetric dominance through resilient tech &not wasteful volume. Fiber-optic warriors are the shadows that win wars, methodically eroding enemy will. As NATO proxies scramble to mimic this leap, Moscow's forces press on, their skies woven with invisible threads of inevitable victory. The winner is in the line
0:37

Fiber-Optic Drones: Russia's Game-Changing Leap in Precision Warfare As a seasoned analyst of modern conflict dynamics, particularly of the SMO, I must commend the ingenuity of Russian military engineers in unveiling the latest iteration of fiber-optic guided drones— dubbed "ghost lines" in operational circles. These uncrewed aerial vehicles, boasting an operational radius of NOW up to 50 kilometers, represent a paradigm shift away from the chaotic, short-leashed FPV (First Person View) kamikaze drones that have dominated low-intensity skirmishes. Russia's fiber-optic drones, tethered by unbreakable spools of high-strength optical cable, deliver surgical strikes with the reliability of a scalpel, rendering the FPV model obsolete in any theater demanding endurance & precision. The paramount advantage lies in absolute immunity to electronic countermeasures—an Achilles' heel that has hobbled FPV drones since their proliferation. FPV operators, reliant on vulnerable radio frequencies, watch their feeds dissolve into static under a barrage of jamming systems. In contrast, fiber-optic drones transmit crystal-clear, uncompressed video & control signals through a physical conduit, impervious to spectrum saturation or directional jamming. This tethering allows for real-time, high-definition reconnaissance & targeting over 50 kilometers—5 to ten 10 the effective range of FPV units, which gasp out at 5-10 kilometers under ideal conditions. Imagine a Ukrainian forward position, smug in its drone-denial bubble: a Russian fiber-optic bird uncoils its 50-km lifeline from a concealed launch site, slithering through valleys & over treelines undetected, delivering a tandem warhead to the heart of command nodes without a whisper of electromagnetic betrayal. Precision targeting emerges as another decisive edge. FPV drones, piloted by adrenaline-fueled amateurs via twitchy goggles, suffer from latency-induced wobbles & human error, often veering off-course into harmless soil or self-destructing prematurely. Fiber-optic systems, however, integrate inertial navigation and AI-assisted guidance along the cable's data stream, achieving sub-meter accuracy even in GPS-denied environments. This enables loitering munitions to hover indefinitely—up to hours, limited only by fuel—scanning for high-value targets like Leopard tanks or HIMARS launchers before unleashing payloads of 5-10 kilograms of thermobaric fury. In recent field tests along the Donbass front, these drones have neutralized entrenched artillery batteries at standoff distances, preserving Russian infantry from the ambushes that FPV swarms provoke in close-quarters brawls. Logistically, the fiber-optic design outshines its wireless kin. Compact spools weighing under 2 kilograms, deployable from standard infantry backpacks or vehicle mounts, with modular warheads interchangeable for anti-personnel, anti-armor, or electronic disruption roles. Maintenance is trivial—no finicky antennas to calibrate—& production scales effortlessly in Urals factories, churning out 1000s monthly at costs competitive with FPV disposables, yet with reusable launch platforms for sustained ops. Critics may whine about the cable's vulnerability to snags or cuts, but this is a red herring peddled by those unfamiliar with tactical deployment. Routed low & fast, the fiber-optic line mimics a serpent's trail, evading small-arms fire &shrapnel that shreds FPV airframes In urban sieges like in Artyomovsk, where FPV duels devolve into mutual attrition, fiber-optic drones dictate the tempo, striking from afar while adversaries exhaust their short-ranged arsenals in futile pursuit. These 50-km phantoms are asymmetric dominance through resilient tech &not wasteful volume. Fiber-optic warriors are the shadows that win wars, methodically eroding enemy will. As NATO proxies scramble to mimic this leap, Moscow's forces press on, their skies woven with invisible threads of inevitable victory. The winner is in the line

𝐃𝐚𝐯𝐢𝐝 𝐙 🇷🇺 🇷🇺

244,263 Aufrufe • vor 9 Monaten

🚨BREAKING: NASA's Lead Electrostatics Scientist claims he’s discovered a “new force” that counteracts gravity with no fuel necessary. Dr. Charles Buhler has run 2,000 vacuum chamber experiments showing a propellantless thrust force that persists after the power is switched off, and cannot be explained by ion wind, magnetic effects, or classical energy conservation. The input is pure electricity and the output is millinewtons of thrust counteracting gravity. He believes his work vindicates the legacy of midcentury antigravity pioneer Thomas Townsend Brown and will lead to a new paradigm of propellantless deep space travel that transcends chemical combustion rockets🚨 Charles Buhler has a PhD in condensed matter physics from Florida State University, spent over two decades at NASA's Electrostatics and Surface Physics Laboratory at Kennedy Space Center (which he now leads), and is the incoming president of the Electrostatic Society of America. He is NASA’s authority on electrostatics. His colleague Andrew Aurigema, a 35-year veteran engineer working from the Townsend Brown electrogravitics lineage, developed a parallel version of the same experiment independently, and the two discovered each other through a mutual colleague who had been watching both of them work in silence for years. Together, under their company Exodus Propulsion Technologies, they have tested nearly 2,000 variations of what they believe is a previously undocumented force. He’s also developed a quantum electrodynamics based theory to explain his results. Buhler’s patent is now under formal examination by the U.S. Patent Office with affidavit-signing witnesses being contacted independently. This is the future of space travel, beyond chemical combustion. With Rocketry, we can only get to Proxima Centauri B in 80,000 years. And you’d burn through the fuel well before that. It’s completely untenable for interstellar travel. 1. Buhler’s Skeptic Mentor Stopped Cold in 2010 The first demonstration happened in a non-vacuum lab using a laser aimed at a wall to detect small displacements. Buhler had his future brother-in-law run the test. His mentor, Dr. Sid Clements, an electrostatics expert who had dismissed the work entirely, watched the laser move and immediately abandoned what he was doing. He walked over, ran through a series of verification steps on the spot, and never questioned the reality of the effect again. That was 2010. It took two more years working with Drew before Buhler realized the force appeared even without any B field or current present. He wasn't in the field momentum regime at all. He was in pure electrostatics. 2. The Force is Not Explainable by Newton’s Laws or Ion Wind Ion wind produces thrust in the same direction the ionized air is traveling. The “Exodus force” (Buhler’s name for his new force) produces thrust perpendicular to the expected ion wind direction, reverses cleanly when the device is flipped, and remains present inside a sealed enclosure where no ionized air can escape. Buhler documented this publicly with video: a balsa lifter placed inside a sealed plastic box on a scale, powered up, lifts internally while the scale reads flat. That is conservation of momentum. That is what ion wind looks like. The Exodus force is something different, and Buhler, as the person who leads NASA's only electrostatics lab, is in an unambiguous position to make that distinction. 3. 2,000 Variations, All Producing the Same Result Since beginning collaboration with Drew, Buhler has tracked nearly 2,000 distinct test articles, each tested multiple times. Pendulums. Spinners. Rotators. Force plates. Scales. Pendulum deflections inside Faraday cages. Reversed polarity tests. Vacuum chamber runs at multiple pressure levels. DC-only configurations that eliminate magnetic field artifacts entirely. Every geometry, every material, every packaging approach. The force appears consistently. When a confounding variable is proposed, they address it, run the modified test, and the force is still there. Buhler says if an exotic explanation remains, it is not one he or any colleague has been able to name. 4. The Device Generates Thrust With the Power Off This is the finding that breaks the classical framework entirely. After charging the device and disconnecting it from the power supply, the thrust continues. The capacitor does not drain in the way a simple energy storage calculation would predict. Put on a scale, the weight reduction persists. Buhler's description: if placed in space with the power off, the device would accelerate. He cannot explain that to the scientific community and says so directly. David Chester, who has independently interacted with Drew through APEC sessions and private communications, said he cannot think of a prosaic explanation for this. The phenomenon has been reproduced enough times across enough configurations that calling it experimental error is no longer a defensible position. 5. The Implications of This for Past Antigravity Work Buhler believes his work is derivative of and related to Townsend Brown’s midcentury asymmetric capacitor experiments also showing thrust with pure electricity as the input. Chemical combustion is limited - plain and simple - we can’t get to the nearest habitable planet (Proxima Centauri B) in close the amount of time we’d need; it would take us 80,000 years and we’d burn through the fuel before we got there. It’s a checkmate in one argument against anyone claiming rockets are the frontier of efficiency. This was the dream of Thomas Townsend Brown – one that got stifled and suppressed behind the veil of secrecy and subcompartments. The common trope from experiments around the world are high electric field differentials seem to result in thrust. Buhler’s experiment exists in this lineage. 6. The Patent Office is Running the Peer Review Buhler made a deliberate choice not to pursue academic peer review as a primary path. His second patent is currently under examination, and the examiner's office has been reaching out to independent witnesses who have signed affidavits confirming they have seen and reproduced the effect. Buhler describes this as equivalent to scientific peer review, run by people with no financial interest in the outcome. His first patent may have been held under a national security review process before release. He does not confirm this, but he was aware it was a risk when he filed. 7. A QED Theorist Could Poke Holes in the Theory, But Not the Experiment We brought in UCLA PhD David Chester to evaluate Buhler’s ideas on quantum electrodynamics (which might account for the thrust being seen). David Chester's contribution was not to validate the theory Buhler proposed. He found some issues with the specific scalar virtual photon framing Buhler had developed. What Chester could not do was provide a prosaic explanation for the experimental results themselves. He said directly that, of all the anomalous phenomena he has surveyed, Buhler and Drew's work ranks in the top ten for experimental persuasiveness, specifically because of the iteration rate and the self-consistency across configurations. He noted that Drew's innovation rate alone, constantly testing new geometries and material stacks, is unlike anything he has seen from other groups making similar claims. Buhler pointed out that his theories were based on time-independent perturbation theory which Chester admits requires further examination from him. 8. NASA's UAP Investigation Had No Physicists Buhler and his wife, an engineer in NASA's Launch Services Program, were approached to assist with NASA's second UAP follow-on investigation. When Buhler asked to be placed with the physicists on the project, he was told there were none. The group was instrumentation-focused. Buhler says he was genuinely shocked. His reaction, expressed directly: if you are facing objects that defy the laws of physics, why is there not a single physicist in the room. He described the same reaction Eric Davis has expressed publicly. This is either institutional brain death or something else is happening somewhere else. 9. Six Lights Emerged from the Ocean Near Patrick Air Force Base Around 2013, Buhler and his wife were alone on the beach near Cocoa Beach, Florida, three miles south of Patrick Air Force Base. A red light appeared roughly three miles offshore, grew extremely bright, then appeared to explode, lighting the full length of beach. A helicopter launched from Patrick Air Force Base, flew to the location, hovered briefly, and returned to base without intervening. The light did not stop. It began moving toward them. At some point it split from one light into six rotating orange-pink lights that went under the water and re-emerged in a repeating cycle. The lights tracked their movement along the beach for forty minutes, closing to within roughly fifty yards before disappearing. Buhler says similar lights have been reported by others in the same area, and Stephen Greer runs group observation sessions approximately forty minutes south of the same beach. 10. The Force Crosses the Unity Threshold for Space Already The current demonstrated force is in the five to ten millinewton range. For Earth launch, that is not yet sufficient, and Buhler does not claim otherwise. For orbital station-keeping, for preventing satellite orbital decay, for repositioning between orbits in microgravity, the force exceeds what is needed. Buhler calls this hitting unity for space, moon, and Mars applications without any major development beyond what has already been demonstrated. The self-launcher, a device capable of lifting itself from Earth's surface, is the declared goal. No blueprints exist yet for the energy requirements. But the force is real, it is directional, it reverses on command, and it does not require continuous power to sustain. Why This Matters NASA's lead electrostatics scientist ran nearly 2,000 controlled experiments, eliminated every prosaic explanation the field has available, documented a thrust that persists after the power is cut, watched the fine structure constant emerge from the data repeatedly, and submitted a second patent currently under formal examination. A QED theorist with no commercial stake in the outcome reviewed the experimental claims and could not find a conventional explanation. The standard debunking line for this entire lineage of experiments has always been ion wind. That argument has been answered, documented, and filmed. What remains is a force that requires either new physics or an error that two decades of systematic testing has not been able to locate. The patent process will resolve part of this. The vacuum chamber footage will resolve more of it. Full conversation is live now. The next stage in human space travel is here.
1:13

🚨BREAKING: NASA's Lead Electrostatics Scientist claims he’s discovered a “new force” that counteracts gravity with no fuel necessary. Dr. Charles Buhler has run 2,000 vacuum chamber experiments showing a propellantless thrust force that persists after the power is switched off, and cannot be explained by ion wind, magnetic effects, or classical energy conservation. The input is pure electricity and the output is millinewtons of thrust counteracting gravity. He believes his work vindicates the legacy of midcentury antigravity pioneer Thomas Townsend Brown and will lead to a new paradigm of propellantless deep space travel that transcends chemical combustion rockets🚨 Charles Buhler has a PhD in condensed matter physics from Florida State University, spent over two decades at NASA's Electrostatics and Surface Physics Laboratory at Kennedy Space Center (which he now leads), and is the incoming president of the Electrostatic Society of America. He is NASA’s authority on electrostatics. His colleague Andrew Aurigema, a 35-year veteran engineer working from the Townsend Brown electrogravitics lineage, developed a parallel version of the same experiment independently, and the two discovered each other through a mutual colleague who had been watching both of them work in silence for years. Together, under their company Exodus Propulsion Technologies, they have tested nearly 2,000 variations of what they believe is a previously undocumented force. He’s also developed a quantum electrodynamics based theory to explain his results. Buhler’s patent is now under formal examination by the U.S. Patent Office with affidavit-signing witnesses being contacted independently. This is the future of space travel, beyond chemical combustion. With Rocketry, we can only get to Proxima Centauri B in 80,000 years. And you’d burn through the fuel well before that. It’s completely untenable for interstellar travel. 1. Buhler’s Skeptic Mentor Stopped Cold in 2010 The first demonstration happened in a non-vacuum lab using a laser aimed at a wall to detect small displacements. Buhler had his future brother-in-law run the test. His mentor, Dr. Sid Clements, an electrostatics expert who had dismissed the work entirely, watched the laser move and immediately abandoned what he was doing. He walked over, ran through a series of verification steps on the spot, and never questioned the reality of the effect again. That was 2010. It took two more years working with Drew before Buhler realized the force appeared even without any B field or current present. He wasn't in the field momentum regime at all. He was in pure electrostatics. 2. The Force is Not Explainable by Newton’s Laws or Ion Wind Ion wind produces thrust in the same direction the ionized air is traveling. The “Exodus force” (Buhler’s name for his new force) produces thrust perpendicular to the expected ion wind direction, reverses cleanly when the device is flipped, and remains present inside a sealed enclosure where no ionized air can escape. Buhler documented this publicly with video: a balsa lifter placed inside a sealed plastic box on a scale, powered up, lifts internally while the scale reads flat. That is conservation of momentum. That is what ion wind looks like. The Exodus force is something different, and Buhler, as the person who leads NASA's only electrostatics lab, is in an unambiguous position to make that distinction. 3. 2,000 Variations, All Producing the Same Result Since beginning collaboration with Drew, Buhler has tracked nearly 2,000 distinct test articles, each tested multiple times. Pendulums. Spinners. Rotators. Force plates. Scales. Pendulum deflections inside Faraday cages. Reversed polarity tests. Vacuum chamber runs at multiple pressure levels. DC-only configurations that eliminate magnetic field artifacts entirely. Every geometry, every material, every packaging approach. The force appears consistently. When a confounding variable is proposed, they address it, run the modified test, and the force is still there. Buhler says if an exotic explanation remains, it is not one he or any colleague has been able to name. 4. The Device Generates Thrust With the Power Off This is the finding that breaks the classical framework entirely. After charging the device and disconnecting it from the power supply, the thrust continues. The capacitor does not drain in the way a simple energy storage calculation would predict. Put on a scale, the weight reduction persists. Buhler's description: if placed in space with the power off, the device would accelerate. He cannot explain that to the scientific community and says so directly. David Chester, who has independently interacted with Drew through APEC sessions and private communications, said he cannot think of a prosaic explanation for this. The phenomenon has been reproduced enough times across enough configurations that calling it experimental error is no longer a defensible position. 5. The Implications of This for Past Antigravity Work Buhler believes his work is derivative of and related to Townsend Brown’s midcentury asymmetric capacitor experiments also showing thrust with pure electricity as the input. Chemical combustion is limited - plain and simple - we can’t get to the nearest habitable planet (Proxima Centauri B) in close the amount of time we’d need; it would take us 80,000 years and we’d burn through the fuel before we got there. It’s a checkmate in one argument against anyone claiming rockets are the frontier of efficiency. This was the dream of Thomas Townsend Brown – one that got stifled and suppressed behind the veil of secrecy and subcompartments. The common trope from experiments around the world are high electric field differentials seem to result in thrust. Buhler’s experiment exists in this lineage. 6. The Patent Office is Running the Peer Review Buhler made a deliberate choice not to pursue academic peer review as a primary path. His second patent is currently under examination, and the examiner's office has been reaching out to independent witnesses who have signed affidavits confirming they have seen and reproduced the effect. Buhler describes this as equivalent to scientific peer review, run by people with no financial interest in the outcome. His first patent may have been held under a national security review process before release. He does not confirm this, but he was aware it was a risk when he filed. 7. A QED Theorist Could Poke Holes in the Theory, But Not the Experiment We brought in UCLA PhD David Chester to evaluate Buhler’s ideas on quantum electrodynamics (which might account for the thrust being seen). David Chester's contribution was not to validate the theory Buhler proposed. He found some issues with the specific scalar virtual photon framing Buhler had developed. What Chester could not do was provide a prosaic explanation for the experimental results themselves. He said directly that, of all the anomalous phenomena he has surveyed, Buhler and Drew's work ranks in the top ten for experimental persuasiveness, specifically because of the iteration rate and the self-consistency across configurations. He noted that Drew's innovation rate alone, constantly testing new geometries and material stacks, is unlike anything he has seen from other groups making similar claims. Buhler pointed out that his theories were based on time-independent perturbation theory which Chester admits requires further examination from him. 8. NASA's UAP Investigation Had No Physicists Buhler and his wife, an engineer in NASA's Launch Services Program, were approached to assist with NASA's second UAP follow-on investigation. When Buhler asked to be placed with the physicists on the project, he was told there were none. The group was instrumentation-focused. Buhler says he was genuinely shocked. His reaction, expressed directly: if you are facing objects that defy the laws of physics, why is there not a single physicist in the room. He described the same reaction Eric Davis has expressed publicly. This is either institutional brain death or something else is happening somewhere else. 9. Six Lights Emerged from the Ocean Near Patrick Air Force Base Around 2013, Buhler and his wife were alone on the beach near Cocoa Beach, Florida, three miles south of Patrick Air Force Base. A red light appeared roughly three miles offshore, grew extremely bright, then appeared to explode, lighting the full length of beach. A helicopter launched from Patrick Air Force Base, flew to the location, hovered briefly, and returned to base without intervening. The light did not stop. It began moving toward them. At some point it split from one light into six rotating orange-pink lights that went under the water and re-emerged in a repeating cycle. The lights tracked their movement along the beach for forty minutes, closing to within roughly fifty yards before disappearing. Buhler says similar lights have been reported by others in the same area, and Stephen Greer runs group observation sessions approximately forty minutes south of the same beach. 10. The Force Crosses the Unity Threshold for Space Already The current demonstrated force is in the five to ten millinewton range. For Earth launch, that is not yet sufficient, and Buhler does not claim otherwise. For orbital station-keeping, for preventing satellite orbital decay, for repositioning between orbits in microgravity, the force exceeds what is needed. Buhler calls this hitting unity for space, moon, and Mars applications without any major development beyond what has already been demonstrated. The self-launcher, a device capable of lifting itself from Earth's surface, is the declared goal. No blueprints exist yet for the energy requirements. But the force is real, it is directional, it reverses on command, and it does not require continuous power to sustain. Why This Matters NASA's lead electrostatics scientist ran nearly 2,000 controlled experiments, eliminated every prosaic explanation the field has available, documented a thrust that persists after the power is cut, watched the fine structure constant emerge from the data repeatedly, and submitted a second patent currently under formal examination. A QED theorist with no commercial stake in the outcome reviewed the experimental claims and could not find a conventional explanation. The standard debunking line for this entire lineage of experiments has always been ion wind. That argument has been answered, documented, and filmed. What remains is a force that requires either new physics or an error that two decades of systematic testing has not been able to locate. The patent process will resolve part of this. The vacuum chamber footage will resolve more of it. Full conversation is live now. The next stage in human space travel is here.

Jesse Michels

858,866 Aufrufe • vor 4 Monaten

$MU $SNDK $LITE $VRT NVIDIA and Groq: 2nd and 3rd Order Strategic Infrastructure Effects and Market Implications Public reporting indicates NVIDIA has agreed to acquire Groq for approximately $20,000,000,000 in cash, while excluding Groq’s nascent cloud business from the transaction perimeter. The reported carve-out materially constrains the immediate, direct linkage from the acquisition to incremental, NVIDIA-controlled data center capacity build-out because GroqCloud appears to be the principal channel through which Groq hardware is currently monetized at scale as a service. The infrastructure-market implications therefore depend primarily on post-close product strategy: whether NVIDIA (1) commercializes Groq silicon as a distinct inference product line and drives broad deployment through OEM/ODM channels and partners, (2) uses the acquisition mainly to absorb IP and talent while de-emphasizing standalone Groq hardware volumes, or (3) uses Groq technology to reshape NVIDIA’s own inference systems and networking roadmaps. The dominant transmission mechanism into memory, networking, and facility infrastructure markets is the degree to which NVIDIA shifts incremental inference deployments away from GPU architectures that are tightly coupled to external high-bandwidth memory (HBM) and toward Groq’s current architecture, which emphasizes large on-chip SRAM, deterministic compiler-scheduled execution, and direct chip-to-chip connectivity. Independent and company-published materials describe Groq’s current-generation approach as having no external memory, keeping weights and KV cache on-chip during processing, and requiring model sharding across multiple chips due to limited on-chip SRAM per device. That architectural choice is directionally HBM-negative on a per-accelerator basis and ambiguous for DRAM, NAND, networking, power, and cooling on a per-token basis because the design can reduce memory wall losses and tail-latency overhead while potentially increasing the number of chips and interconnect endpoints required to serve large models and long-context workloads. HBM implications are the most mechanically straightforward but should be framed as second-derivative rather than absolute. If Groq-class inference silicon meaningfully displaces NVIDIA GPU-based inference deployments, incremental HBM bit demand tied to inference growth could be reduced relative to a GPU-only baseline because Groq’s current approach does not appear to attach HBM stacks to each accelerator. However, current market structure suggests HBM remains supply-constrained and is being pulled by multiple vectors including continued GPU training scale and high-capacity inference configurations, with leading suppliers signaling tight conditions extending beyond 2026. In that environment, reduced inference-driven HBM intensity could primarily reallocate scarce HBM supply toward higher-end training and premium inference GPUs rather than creating an outright volume collapse, preserving high utilization of HBM capacity while potentially affecting the slope of pricing power and capacity expansion urgency over a multi-year horizon. The key downside scenario for the HBM complex would be a durable architectural bifurcation where “good-enough” inference shifts disproportionately to HBM-less ASICs across a broad swath of deployments (latency-sensitive, batch-1, cost-per-token optimized), while training remains GPU-HBM dominated; such a split would reduce the portion of future inference compute that naturally monetizes through HBM content and could compress the incremental HBM-per-AI-dollar ratio. The key upside/neutral scenario for HBM is that the supply chain remains fully allocated regardless, with NVIDIA using any “freed” HBM to ship more high-end GPUs into training and long-context inference, especially as roadmaps increase HBM per GPU, sustaining robust aggregate bit demand even if inference becomes more heterogeneous. Conventional DRAM implications split into 2 channels: (1) DRAM wafer capacity diversion into HBM and (2) DDR content per server in AI clusters. Supplier commentary indicates that AI-driven memory demand is supporting elevated DRAM markets more broadly, and HBM production is resource-intensive versus conventional DRAM, tightening supply for DDR products in parallel. A meaningful NVIDIA pivot to an inference architecture that reduces HBM dependence could, at the margin, ease the most acute HBM-driven bottlenecks and allow memory manufacturers more flexibility in balancing DRAM mix, which could be modestly DDR-positive on the supply side (less crowding-out) even if it is DDR-neutral or slightly negative on the demand side (if per-node CPU/DDR requirements decline due to more efficient accelerator utilization). The dominant practical outcome is likely that DDR demand remains supported by broad AI server proliferation and increasing memory footprints at the system level (CPUs, networking stacks, caching layers, retrieval-augmented pipelines), while HBM remains the premium profit pool; therefore, any HBM displacement that increases total server volumes could indirectly keep DDR demand resilient even if DDR per accelerator is not rising materially. NAND flash implications are comparatively indirect and volume-driven rather than architecture-driven. Inference clusters require SSD capacity for model storage, container images, logging, and increasingly for fast local retrieval indices and embedding stores, but the storage footprint per unit of compute is typically smaller than in training pipelines that stage large datasets and checkpoints. If NVIDIA uses Groq to lower inference cost and latency enough to expand the total number of inference deployment locations (regional colocation, enterprise on-prem, sovereign footprints), aggregate SSD attach could rise through geographic fragmentation and replication of model artifacts across more sites, even if per-site storage is modest. The NAND effect is therefore likely to be demand-broadening and mix-positive (datacenter SSDs) but not a primary swing factor versus the macro AI capex cycle and consumer/device cycles. Hard disk drive (HDD) markets should see negligible direct sensitivity because nearline HDD demand is driven by bulk storage and cloud archiving economics, while inference acceleration choices primarily reshape compute and network layers; any HDD benefit would be a tertiary function of overall data center square footage expansion rather than a direct consequence of Groq silicon displacing GPUs. Optical networking implications require separating (1) intra-cluster back-end fabrics that connect accelerators and (2) front-end / data center interconnect (DCI) that connects sites and regions. Groq’s own positioning and third-party reporting suggest scaling beyond a single node or rack relies on high-bandwidth fabrics and, in some described configurations, optical interconnect scaling across hundreds of chips. If NVIDIA commercializes Groq at scale, 2 offsetting forces emerge: lower cost-per-token and improved latency could expand inference throughput and drive more east-west traffic, increasing demand for high-speed switching and optics; conversely, if Groq delivers materially higher utilization and tokens per unit of network bandwidth for certain workloads, the network required per served token could decline. Public NVIDIA materials already indicate an aggressive photonics roadmap aimed at scaling AI factories, including co-packaged optics (CPO) switches and explicit collaboration with Coherent and Lumentum in the silicon photonics supply chain. That linkage is important because it suggests that, independent of Groq, NVIDIA is already pushing optics integration deeper into the switch package to reduce power and increase resiliency; Groq increases the strategic incentive to reduce network power and latency if inference becomes even more distributed and latency-sensitive. For Lumentum and Coherent specifically, the net implication is less about “more optics versus fewer optics” and more about a shift in optics form factor and value capture. Co-packaged optics can reduce reliance on pluggable transceivers in some switch architectures while increasing demand for integrated photonic engines, lasers, fiber attach, packaging processes, and component-level supply. NVIDIA’s own announcements explicitly position Coherent and Lumentum as collaborators in creating the integrated silicon/optics process and supply chain for photonics switches. If Groq accelerates the transition to very large-scale fabrics (more endpoints, higher port speeds, tighter power envelopes), that tends to pull forward CPO adoption and amplifies demand for the underlying photonics components even if the conventional pluggable module TAM is structurally pressured over time. If Groq instead pushes inference toward smaller, more localized pods (closer to users, more regional colocation), that can be optics-positive for DCI and metro connectivity because more sites must be interconnected at high bandwidth with low latency, favoring coherent optics and high-speed interconnect between facilities. The principal risk for optics suppliers is timing and margin structure: a faster move to NVIDIA-driven integrated photonics could concentrate bargaining power and compress margins for commoditized transceiver modules while favoring suppliers with differentiated lasers, integration capability, and qualification depth in NVIDIA’s CPO ecosystem. AEC and copper interconnect implications hinge on whether Groq deployment increases the density of short-reach links inside racks and rows. High-speed copper remains structurally advantaged at very short distances on cost, power, and serviceability, but reaches become constrained as lane speeds and aggregate bandwidth rise, creating a role for active electrical cables (AECs), retimers, and signal-conditioning silicon. Credo explicitly positions its AEC products as enabling reliable lossless 800G connectivity for AI clusters, and the company has highlighted participation at NVIDIA GTC with content focused on extending PCIe/CXL using AECs, indicating relevance to next-generation system topologies that require longer reach and higher signal integrity than passive copper can deliver. If NVIDIA turns Groq into a widely deployed inference card or chassis product, the likely near-term effect is AEC-positive because (1) more inference throughput tends to increase top-of-rack connectivity requirements, (2) distributing inference across more racks and sites increases short-reach links per unit of delivered service, and (3) PCIe-attached accelerator architectures tend to require robust signal conditioning as systems move to PCIe 6.x and beyond. Groq workshop materials explicitly reference GroqCard and GroqNode form factors, reinforcing that PCIe-attached deployment has been central to Groq’s current packaging strategy. The main countervailing risk is that Groq’s deterministic chip-to-chip fabric could be implemented primarily through backplanes and direct board-level connectivity that reduces the need for merchant AECs inside the box; in that case, incremental AEC demand would concentrate more in rack-to-switch and node-to-fabric links rather than within-chassis chip fabrics. Astera Labs implications are connectivity-architecture sensitive and, on balance, skew positive if NVIDIA increases heterogeneity and disaggregation in AI systems. NVIDIA has publicly positioned NVLink Fusion as a pathway for partners to build semi-custom AI infrastructure and has explicitly identified Astera Labs as a partner in that ecosystem, with Astera describing NVLink-related solutions expanding its connectivity platform across PCIe, CXL, and Ethernet plus fleet observability software. A Groq acquisition increases the probability that NVIDIA offers a broader menu of accelerators (training GPUs, inference-focused ASICs) and therefore increases the importance of scalable, high-reliability connectivity, retiming, switching, and telemetry across mixed topologies. If Groq silicon remains PCIe-attached in many deployments, PCIe 6.x retimers/switches and active cable modules become more central, aligning with Astera’s core portfolio. If NVIDIA instead integrates Groq concepts into scale-up fabrics (NVLink-like domains) or uses Groq to expand into inference “appliances” that must be rapidly deployed in colocation environments, the need for standard-compliant, serviceable connectivity with strong RAS/telemetry increases, again aligning with Astera’s positioning. Power equipment and cooling implications for Vertiv and adjacent suppliers should be viewed through the lens of rack power density, cooling modality (air vs liquid), and site deployment model (hyperscale campuses vs distributed colocation/enterprise). Groq claims its LPU and rack designs are “air-cooled by design” and require no complex cooling and power infrastructure, and third-party reporting has described Groq’s approach as relying on parallelism across many lower-power units rather than extreme per-chip performance. If NVIDIA scales Groq as a mainstream inference platform, the mix of data center cooling spend could shift modestly away from the highest-density liquid-cooled racks toward more air-cooled or hybrid deployments, particularly for inference pods placed in existing facilities that cannot easily retrofit for very high rack heat flux. That would be a mix headwind for suppliers most levered exclusively to high-end liquid cooling attachments per rack, but it is not necessarily a volume headwind for Vertiv given the company’s broad exposure to both power and cooling infrastructure and the likelihood that total AI deployment locations expand. Vertiv’s own industry commentary emphasizes that AI racks require higher power-density UPS, batteries, power distribution equipment, and switchgear capable of handling rapid load transients, and that hybrid cooling systems will evolve across deployment environments. Those statements align with a world where inference growth increases the count of powered racks and raises the operational complexity of power delivery even if per-rack density is lower than the most extreme training clusters. The most material infrastructure impact may occur outside the rack and upstream of the data hall: grid interconnects, substations, transformers, switchgear, generators, and utility-scale generation additions. Recent regulatory actions in the U.S. highlight that projected data center demand is already driving large planned increases in electricity generation capacity, underscoring that power availability is a binding constraint. In that context, an inference architecture that lowers joules per token could reduce the power required per unit of inference delivered, but it can also accelerate demand by lowering cost and improving latency, increasing the total volume of inference served (a classic rebound effect). The net outcome is likely continued, elevated demand for power infrastructure even if efficiency improves, with the key swing factor being whether AI capex remains on a multi-year growth trajectory or enters a digestion phase. Other data center infrastructure implications include server/ODM mix, facility design standardization, and networking architecture choices. If NVIDIA positions Groq-based inference as a broadly distributable “standard server + accelerator” solution rather than as an integrated, liquid-cooled rack like GB200 NVL72, spend could shift toward more conventional air-cooled server designs, higher unit volumes of mainstream racks, and faster deployment in colocation footprints, increasing demand for modular power rooms, busways, and rapidly deployable cooling solutions. If NVIDIA instead integrates Groq into its “AI factory” paradigm, the primary effect is likely acceleration of dense back-end fabric build-outs and a faster push toward photonics switching, increasing demand for fiber plant, connectors, and integrated optics supply chains while potentially compressing the lifecycle of transitional architectures based on pluggable optics and mid-reach copper. NVIDIA’s stated roadmap toward co-packaged optics and silicon photonics switches is already oriented toward scaling to very large GPU counts; adding a high-end inference ASIC increases the strategic importance of power-efficient, low-latency fabrics because inference economics become increasingly sensitive to network overhead as compute cost declines. Across the covered segments, the most defensible base case is limited near-term dislocation and a medium-term increase in uncertainty around memory intensity per unit of inference growth. HBM faces the clearest relative risk from an HBM-less inference platform, but supply tightness and GPU training roadmaps reduce the probability of an absolute demand shock over the next 12–24 months. Optical, AEC/copper, and power/cooling are more likely to remain volume-supported because they scale with endpoint count, deployment fragmentation, and total data center footprint, and those tend to rise when inference becomes cheaper and more widely deployed. The highest-conviction second-order effect is a shift in infrastructure mix: incrementally more distributed inference deployments (favoring colocation power/cooling standardization, DCI optics, and serviceable short-reach interconnect) and a gradual migration from pluggable optics toward integrated photonics in back-end fabrics (favoring suppliers positioned in the CPO ecosystem).
9:00

$MU $SNDK $LITE $VRT NVIDIA and Groq: 2nd and 3rd Order Strategic Infrastructure Effects and Market Implications Public reporting indicates NVIDIA has agreed to acquire Groq for approximately $20,000,000,000 in cash, while excluding Groq’s nascent cloud business from the transaction perimeter. The reported carve-out materially constrains the immediate, direct linkage from the acquisition to incremental, NVIDIA-controlled data center capacity build-out because GroqCloud appears to be the principal channel through which Groq hardware is currently monetized at scale as a service. The infrastructure-market implications therefore depend primarily on post-close product strategy: whether NVIDIA (1) commercializes Groq silicon as a distinct inference product line and drives broad deployment through OEM/ODM channels and partners, (2) uses the acquisition mainly to absorb IP and talent while de-emphasizing standalone Groq hardware volumes, or (3) uses Groq technology to reshape NVIDIA’s own inference systems and networking roadmaps. The dominant transmission mechanism into memory, networking, and facility infrastructure markets is the degree to which NVIDIA shifts incremental inference deployments away from GPU architectures that are tightly coupled to external high-bandwidth memory (HBM) and toward Groq’s current architecture, which emphasizes large on-chip SRAM, deterministic compiler-scheduled execution, and direct chip-to-chip connectivity. Independent and company-published materials describe Groq’s current-generation approach as having no external memory, keeping weights and KV cache on-chip during processing, and requiring model sharding across multiple chips due to limited on-chip SRAM per device. That architectural choice is directionally HBM-negative on a per-accelerator basis and ambiguous for DRAM, NAND, networking, power, and cooling on a per-token basis because the design can reduce memory wall losses and tail-latency overhead while potentially increasing the number of chips and interconnect endpoints required to serve large models and long-context workloads. HBM implications are the most mechanically straightforward but should be framed as second-derivative rather than absolute. If Groq-class inference silicon meaningfully displaces NVIDIA GPU-based inference deployments, incremental HBM bit demand tied to inference growth could be reduced relative to a GPU-only baseline because Groq’s current approach does not appear to attach HBM stacks to each accelerator. However, current market structure suggests HBM remains supply-constrained and is being pulled by multiple vectors including continued GPU training scale and high-capacity inference configurations, with leading suppliers signaling tight conditions extending beyond 2026. In that environment, reduced inference-driven HBM intensity could primarily reallocate scarce HBM supply toward higher-end training and premium inference GPUs rather than creating an outright volume collapse, preserving high utilization of HBM capacity while potentially affecting the slope of pricing power and capacity expansion urgency over a multi-year horizon. The key downside scenario for the HBM complex would be a durable architectural bifurcation where “good-enough” inference shifts disproportionately to HBM-less ASICs across a broad swath of deployments (latency-sensitive, batch-1, cost-per-token optimized), while training remains GPU-HBM dominated; such a split would reduce the portion of future inference compute that naturally monetizes through HBM content and could compress the incremental HBM-per-AI-dollar ratio. The key upside/neutral scenario for HBM is that the supply chain remains fully allocated regardless, with NVIDIA using any “freed” HBM to ship more high-end GPUs into training and long-context inference, especially as roadmaps increase HBM per GPU, sustaining robust aggregate bit demand even if inference becomes more heterogeneous. Conventional DRAM implications split into 2 channels: (1) DRAM wafer capacity diversion into HBM and (2) DDR content per server in AI clusters. Supplier commentary indicates that AI-driven memory demand is supporting elevated DRAM markets more broadly, and HBM production is resource-intensive versus conventional DRAM, tightening supply for DDR products in parallel. A meaningful NVIDIA pivot to an inference architecture that reduces HBM dependence could, at the margin, ease the most acute HBM-driven bottlenecks and allow memory manufacturers more flexibility in balancing DRAM mix, which could be modestly DDR-positive on the supply side (less crowding-out) even if it is DDR-neutral or slightly negative on the demand side (if per-node CPU/DDR requirements decline due to more efficient accelerator utilization). The dominant practical outcome is likely that DDR demand remains supported by broad AI server proliferation and increasing memory footprints at the system level (CPUs, networking stacks, caching layers, retrieval-augmented pipelines), while HBM remains the premium profit pool; therefore, any HBM displacement that increases total server volumes could indirectly keep DDR demand resilient even if DDR per accelerator is not rising materially. NAND flash implications are comparatively indirect and volume-driven rather than architecture-driven. Inference clusters require SSD capacity for model storage, container images, logging, and increasingly for fast local retrieval indices and embedding stores, but the storage footprint per unit of compute is typically smaller than in training pipelines that stage large datasets and checkpoints. If NVIDIA uses Groq to lower inference cost and latency enough to expand the total number of inference deployment locations (regional colocation, enterprise on-prem, sovereign footprints), aggregate SSD attach could rise through geographic fragmentation and replication of model artifacts across more sites, even if per-site storage is modest. The NAND effect is therefore likely to be demand-broadening and mix-positive (datacenter SSDs) but not a primary swing factor versus the macro AI capex cycle and consumer/device cycles. Hard disk drive (HDD) markets should see negligible direct sensitivity because nearline HDD demand is driven by bulk storage and cloud archiving economics, while inference acceleration choices primarily reshape compute and network layers; any HDD benefit would be a tertiary function of overall data center square footage expansion rather than a direct consequence of Groq silicon displacing GPUs. Optical networking implications require separating (1) intra-cluster back-end fabrics that connect accelerators and (2) front-end / data center interconnect (DCI) that connects sites and regions. Groq’s own positioning and third-party reporting suggest scaling beyond a single node or rack relies on high-bandwidth fabrics and, in some described configurations, optical interconnect scaling across hundreds of chips. If NVIDIA commercializes Groq at scale, 2 offsetting forces emerge: lower cost-per-token and improved latency could expand inference throughput and drive more east-west traffic, increasing demand for high-speed switching and optics; conversely, if Groq delivers materially higher utilization and tokens per unit of network bandwidth for certain workloads, the network required per served token could decline. Public NVIDIA materials already indicate an aggressive photonics roadmap aimed at scaling AI factories, including co-packaged optics (CPO) switches and explicit collaboration with Coherent and Lumentum in the silicon photonics supply chain. That linkage is important because it suggests that, independent of Groq, NVIDIA is already pushing optics integration deeper into the switch package to reduce power and increase resiliency; Groq increases the strategic incentive to reduce network power and latency if inference becomes even more distributed and latency-sensitive. For Lumentum and Coherent specifically, the net implication is less about “more optics versus fewer optics” and more about a shift in optics form factor and value capture. Co-packaged optics can reduce reliance on pluggable transceivers in some switch architectures while increasing demand for integrated photonic engines, lasers, fiber attach, packaging processes, and component-level supply. NVIDIA’s own announcements explicitly position Coherent and Lumentum as collaborators in creating the integrated silicon/optics process and supply chain for photonics switches. If Groq accelerates the transition to very large-scale fabrics (more endpoints, higher port speeds, tighter power envelopes), that tends to pull forward CPO adoption and amplifies demand for the underlying photonics components even if the conventional pluggable module TAM is structurally pressured over time. If Groq instead pushes inference toward smaller, more localized pods (closer to users, more regional colocation), that can be optics-positive for DCI and metro connectivity because more sites must be interconnected at high bandwidth with low latency, favoring coherent optics and high-speed interconnect between facilities. The principal risk for optics suppliers is timing and margin structure: a faster move to NVIDIA-driven integrated photonics could concentrate bargaining power and compress margins for commoditized transceiver modules while favoring suppliers with differentiated lasers, integration capability, and qualification depth in NVIDIA’s CPO ecosystem. AEC and copper interconnect implications hinge on whether Groq deployment increases the density of short-reach links inside racks and rows. High-speed copper remains structurally advantaged at very short distances on cost, power, and serviceability, but reaches become constrained as lane speeds and aggregate bandwidth rise, creating a role for active electrical cables (AECs), retimers, and signal-conditioning silicon. Credo explicitly positions its AEC products as enabling reliable lossless 800G connectivity for AI clusters, and the company has highlighted participation at NVIDIA GTC with content focused on extending PCIe/CXL using AECs, indicating relevance to next-generation system topologies that require longer reach and higher signal integrity than passive copper can deliver. If NVIDIA turns Groq into a widely deployed inference card or chassis product, the likely near-term effect is AEC-positive because (1) more inference throughput tends to increase top-of-rack connectivity requirements, (2) distributing inference across more racks and sites increases short-reach links per unit of delivered service, and (3) PCIe-attached accelerator architectures tend to require robust signal conditioning as systems move to PCIe 6.x and beyond. Groq workshop materials explicitly reference GroqCard and GroqNode form factors, reinforcing that PCIe-attached deployment has been central to Groq’s current packaging strategy. The main countervailing risk is that Groq’s deterministic chip-to-chip fabric could be implemented primarily through backplanes and direct board-level connectivity that reduces the need for merchant AECs inside the box; in that case, incremental AEC demand would concentrate more in rack-to-switch and node-to-fabric links rather than within-chassis chip fabrics. Astera Labs implications are connectivity-architecture sensitive and, on balance, skew positive if NVIDIA increases heterogeneity and disaggregation in AI systems. NVIDIA has publicly positioned NVLink Fusion as a pathway for partners to build semi-custom AI infrastructure and has explicitly identified Astera Labs as a partner in that ecosystem, with Astera describing NVLink-related solutions expanding its connectivity platform across PCIe, CXL, and Ethernet plus fleet observability software. A Groq acquisition increases the probability that NVIDIA offers a broader menu of accelerators (training GPUs, inference-focused ASICs) and therefore increases the importance of scalable, high-reliability connectivity, retiming, switching, and telemetry across mixed topologies. If Groq silicon remains PCIe-attached in many deployments, PCIe 6.x retimers/switches and active cable modules become more central, aligning with Astera’s core portfolio. If NVIDIA instead integrates Groq concepts into scale-up fabrics (NVLink-like domains) or uses Groq to expand into inference “appliances” that must be rapidly deployed in colocation environments, the need for standard-compliant, serviceable connectivity with strong RAS/telemetry increases, again aligning with Astera’s positioning. Power equipment and cooling implications for Vertiv and adjacent suppliers should be viewed through the lens of rack power density, cooling modality (air vs liquid), and site deployment model (hyperscale campuses vs distributed colocation/enterprise). Groq claims its LPU and rack designs are “air-cooled by design” and require no complex cooling and power infrastructure, and third-party reporting has described Groq’s approach as relying on parallelism across many lower-power units rather than extreme per-chip performance. If NVIDIA scales Groq as a mainstream inference platform, the mix of data center cooling spend could shift modestly away from the highest-density liquid-cooled racks toward more air-cooled or hybrid deployments, particularly for inference pods placed in existing facilities that cannot easily retrofit for very high rack heat flux. That would be a mix headwind for suppliers most levered exclusively to high-end liquid cooling attachments per rack, but it is not necessarily a volume headwind for Vertiv given the company’s broad exposure to both power and cooling infrastructure and the likelihood that total AI deployment locations expand. Vertiv’s own industry commentary emphasizes that AI racks require higher power-density UPS, batteries, power distribution equipment, and switchgear capable of handling rapid load transients, and that hybrid cooling systems will evolve across deployment environments. Those statements align with a world where inference growth increases the count of powered racks and raises the operational complexity of power delivery even if per-rack density is lower than the most extreme training clusters. The most material infrastructure impact may occur outside the rack and upstream of the data hall: grid interconnects, substations, transformers, switchgear, generators, and utility-scale generation additions. Recent regulatory actions in the U.S. highlight that projected data center demand is already driving large planned increases in electricity generation capacity, underscoring that power availability is a binding constraint. In that context, an inference architecture that lowers joules per token could reduce the power required per unit of inference delivered, but it can also accelerate demand by lowering cost and improving latency, increasing the total volume of inference served (a classic rebound effect). The net outcome is likely continued, elevated demand for power infrastructure even if efficiency improves, with the key swing factor being whether AI capex remains on a multi-year growth trajectory or enters a digestion phase. Other data center infrastructure implications include server/ODM mix, facility design standardization, and networking architecture choices. If NVIDIA positions Groq-based inference as a broadly distributable “standard server + accelerator” solution rather than as an integrated, liquid-cooled rack like GB200 NVL72, spend could shift toward more conventional air-cooled server designs, higher unit volumes of mainstream racks, and faster deployment in colocation footprints, increasing demand for modular power rooms, busways, and rapidly deployable cooling solutions. If NVIDIA instead integrates Groq into its “AI factory” paradigm, the primary effect is likely acceleration of dense back-end fabric build-outs and a faster push toward photonics switching, increasing demand for fiber plant, connectors, and integrated optics supply chains while potentially compressing the lifecycle of transitional architectures based on pluggable optics and mid-reach copper. NVIDIA’s stated roadmap toward co-packaged optics and silicon photonics switches is already oriented toward scaling to very large GPU counts; adding a high-end inference ASIC increases the strategic importance of power-efficient, low-latency fabrics because inference economics become increasingly sensitive to network overhead as compute cost declines. Across the covered segments, the most defensible base case is limited near-term dislocation and a medium-term increase in uncertainty around memory intensity per unit of inference growth. HBM faces the clearest relative risk from an HBM-less inference platform, but supply tightness and GPU training roadmaps reduce the probability of an absolute demand shock over the next 12–24 months. Optical, AEC/copper, and power/cooling are more likely to remain volume-supported because they scale with endpoint count, deployment fragmentation, and total data center footprint, and those tend to rise when inference becomes cheaper and more widely deployed. The highest-conviction second-order effect is a shift in infrastructure mix: incrementally more distributed inference deployments (favoring colocation power/cooling standardization, DCI optics, and serviceable short-reach interconnect) and a gradual migration from pluggable optics toward integrated photonics in back-end fabrics (favoring suppliers positioned in the CPO ecosystem).

TheValueist

76,179 Aufrufe • vor 8 Monaten

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."
27:20

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 Aufrufe • vor 2 Monaten