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3. The Return Phase: Was Earth’s Recovery Smooth or Episodic? The third paper examines the final stage of the sequence: how Earth returns toward equilibrium after large-scale disturbance, and whether that return is best described as smooth or episodic. Classical geophysical models typically assume continuous, gradual relaxation governed by...

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Craig Stone

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A viral paper "Language Model Represents Space and Time" recently claims that LLMs learn "world models". As much as I like Max Tegmark's works, I disagree with their definition of world model. World model is a core concept in AI agent and decision making. It is our mental simulation of how the world works given interventions (or lack thereof). A world model captures causality and intuitive physics, telling the agent what is likely and what is impossible. It can and should be used for counterfactual reasoning, i.e. "what ifs": what would happen if I knock over a cup of water? Where would I have been if I had not taken that bus? Yann LeCun Yann LeCun says it well in his position paper ( I quote: "Using such world models, animals can learn new skills with very few trials. They can predict the consequences of their actions, they can reason, plan, explore, and imagine new solutions to problems. Importantly, they can also avoid making dangerous mistakes when facing an unknown situation." The first use of the term World Model in deep policy learning is attributed to hardmaru & Jürgen Schmidhuber: In their seminal paper, an agent masters shooting skills in the popular game Doom (demo below) by learning in imagination, using an internal world model as a "physics simulator". To put in a simple Python math formula, world model learns a function F(s[0:t-1], a) -> s[t:], which takes as input the observed past and current action, and outputs plausible future states. Now the definition of World Model in Tegmark's paper seems to be about predicting GPS coordinates and time eras. I see this as just a classification task with no causal learning and simulation going on. You cannot make meaningful interventions against that model, nor can you optimize any decision making in a closed feedback loop. As for the "space & time neurons", I think they are most similar to the "sentiment neuron" that OpenAI published in 2017: Predicting GPS is conceptually no different from predicting sentiment in my opinion. I don't think their experimental results are wrong - just that their conclusion is on shaky grounds. I welcome any debate! Paper link:
0:15

A viral paper "Language Model Represents Space and Time" recently claims that LLMs learn "world models". As much as I like Max Tegmark's works, I disagree with their definition of world model. World model is a core concept in AI agent and decision making. It is our mental simulation of how the world works given interventions (or lack thereof). A world model captures causality and intuitive physics, telling the agent what is likely and what is impossible. It can and should be used for counterfactual reasoning, i.e. "what ifs": what would happen if I knock over a cup of water? Where would I have been if I had not taken that bus? Yann LeCun Yann LeCun says it well in his position paper ( I quote: "Using such world models, animals can learn new skills with very few trials. They can predict the consequences of their actions, they can reason, plan, explore, and imagine new solutions to problems. Importantly, they can also avoid making dangerous mistakes when facing an unknown situation." The first use of the term World Model in deep policy learning is attributed to hardmaru & Jürgen Schmidhuber: In their seminal paper, an agent masters shooting skills in the popular game Doom (demo below) by learning in imagination, using an internal world model as a "physics simulator". To put in a simple Python math formula, world model learns a function F(s[0:t-1], a) -> s[t:], which takes as input the observed past and current action, and outputs plausible future states. Now the definition of World Model in Tegmark's paper seems to be about predicting GPS coordinates and time eras. I see this as just a classification task with no causal learning and simulation going on. You cannot make meaningful interventions against that model, nor can you optimize any decision making in a closed feedback loop. As for the "space & time neurons", I think they are most similar to the "sentiment neuron" that OpenAI published in 2017: Predicting GPS is conceptually no different from predicting sentiment in my opinion. I don't think their experimental results are wrong - just that their conclusion is on shaky grounds. I welcome any debate! Paper link:

Jim Fan

594,014 Aufrufe • vor 2 Jahren

The term "continual learning" has become overloaded if you see it as an ML problem. One classic thread is about memorization: regularization-based continual learning methods, such as EWC, MAS, and SI, estimate which parameters mattered for previous tasks and resist changing them too much. One modern thread is about adaptation: test-time training and inference-time learning methods, such as TTT, adapt part of the model on the incoming test stream before making predictions. These are sometimes discussed as separate threads. But in modern scalable architectures, I think they are better seen as complementary constraints: a model that learns quickly at test time also benefits from a mechanism for deciding what not to forget. In our #ECCV2026 paper, we study this in large-scale 4D reconstruction: how to build fast spatial memory that can adapt over long observation streams while reducing collapse and forgetting. Instead of using fully plastic test-time updates, we stabilize fast-weight adaptation with an elastic prior that balances adaptation and memory. Key ideas: - Elastic Test-Time Training: Fisher-weighted consolidation for fast-weight updates - EMA anchor weights that provide a moving reference for stability - Chunk-by-chunk inference for long 3D/4D observation streams We show that this scales across large 3D/4D pretraining settings, including both LRM-style and LVSM-style models, and improves reconstruction across benchmarks including Stereo4D, NVIDIA, and DL3DV-140. We release model checkpoints across different design choices: resolution, post-training curriculum, and whether the model uses an explicit 4DGS intermediate representation. - Homepage: - Paper: - Code: - Models: This work is co-led with Xueyang Yu, contributed by Haoyu Zhen Yuncong Yang, and advised by Michigan SLED Lab Chuang Gan.
0:14

The term "continual learning" has become overloaded if you see it as an ML problem. One classic thread is about memorization: regularization-based continual learning methods, such as EWC, MAS, and SI, estimate which parameters mattered for previous tasks and resist changing them too much. One modern thread is about adaptation: test-time training and inference-time learning methods, such as TTT, adapt part of the model on the incoming test stream before making predictions. These are sometimes discussed as separate threads. But in modern scalable architectures, I think they are better seen as complementary constraints: a model that learns quickly at test time also benefits from a mechanism for deciding what not to forget. In our #ECCV2026 paper, we study this in large-scale 4D reconstruction: how to build fast spatial memory that can adapt over long observation streams while reducing collapse and forgetting. Instead of using fully plastic test-time updates, we stabilize fast-weight adaptation with an elastic prior that balances adaptation and memory. Key ideas: - Elastic Test-Time Training: Fisher-weighted consolidation for fast-weight updates - EMA anchor weights that provide a moving reference for stability - Chunk-by-chunk inference for long 3D/4D observation streams We show that this scales across large 3D/4D pretraining settings, including both LRM-style and LVSM-style models, and improves reconstruction across benchmarks including Stereo4D, NVIDIA, and DL3DV-140. We release model checkpoints across different design choices: resolution, post-training curriculum, and whether the model uses an explicit 4DGS intermediate representation. - Homepage: - Paper: - Code: - Models: This work is co-led with Xueyang Yu, contributed by Haoyu Zhen Yuncong Yang, and advised by Michigan SLED Lab Chuang Gan.

Martin Ziqiao Ma

33,617 Aufrufe • vor 1 Monat

Facial reconstruction of a 9,500-year-old man from Gobero, Ténéré Desert, Niger The Kiffian culture was an Early Holocene culture that existed in the Sahara during the African Humid Period, also referred to as the Neolithic Subpluvial. The site of Gobero is known as one of the largest and earliest burial sites of Stone Age people in the Sahara. The Kiffians were skilled hunters. The remains of numerous large savannah animals found in the same area suggest that they inhabited the shores of a lake that existed during the Holocene Wet Phase, when the Sahara was a lush and humid environment. Kiffian remains are notable for their height; the reconstructed individual (G3B8) was approximately 188 cm tall. The Early Holocene skulls from Gobero share a number of traits with populations of the Sahara and Maghreb from the Late Upper Paleolithic and Early Holocene and were therefore described as “Mechtoid” (Sereno et al., 2008). However, the published study does not distinguish anthropologically between “Mechtoids” from the northern and southern parts of the Sahara, although this distinction is clearly evident in other studies. It therefore remains unclear whether the features of the Early Holocene inhabitants of Gobero were predominantly Sub-Saharan or genuinely “Mechtoid.” Moreover, it is possible that the Middle Holocene group had an origin independent of that of the Early Holocene population (S. V. Drobyshevsky, Antropogenez). A more recent study by Christopher M. Stojanowski, Joel D. Irish, and Paul C. Sereno (2026) found that the inter-sample affinities suggest that the successive Gobero populations are dentally indistinguishable, indicating population continuity at the site despite significant climatic deterioration. Both the Early and Middle Holocene samples from Gobero share dental affinities with Nile Valley populations, particularly those from the multicomponent Sudanese site of al-Khiday. However, both samples also display intermediate affinities with northern and Sub-Saharan African populations. These results suggest that, for much of the Holocene, an admixed population inhabited the central Sahara, with a primarily East African/Nile Valley origin. Nevertheless, as the climate deteriorated, interactions with northern and Sub-Saharan African populations cannot be ruled out.
0:19

Facial reconstruction of a 9,500-year-old man from Gobero, Ténéré Desert, Niger The Kiffian culture was an Early Holocene culture that existed in the Sahara during the African Humid Period, also referred to as the Neolithic Subpluvial. The site of Gobero is known as one of the largest and earliest burial sites of Stone Age people in the Sahara. The Kiffians were skilled hunters. The remains of numerous large savannah animals found in the same area suggest that they inhabited the shores of a lake that existed during the Holocene Wet Phase, when the Sahara was a lush and humid environment. Kiffian remains are notable for their height; the reconstructed individual (G3B8) was approximately 188 cm tall. The Early Holocene skulls from Gobero share a number of traits with populations of the Sahara and Maghreb from the Late Upper Paleolithic and Early Holocene and were therefore described as “Mechtoid” (Sereno et al., 2008). However, the published study does not distinguish anthropologically between “Mechtoids” from the northern and southern parts of the Sahara, although this distinction is clearly evident in other studies. It therefore remains unclear whether the features of the Early Holocene inhabitants of Gobero were predominantly Sub-Saharan or genuinely “Mechtoid.” Moreover, it is possible that the Middle Holocene group had an origin independent of that of the Early Holocene population (S. V. Drobyshevsky, Antropogenez). A more recent study by Christopher M. Stojanowski, Joel D. Irish, and Paul C. Sereno (2026) found that the inter-sample affinities suggest that the successive Gobero populations are dentally indistinguishable, indicating population continuity at the site despite significant climatic deterioration. Both the Early and Middle Holocene samples from Gobero share dental affinities with Nile Valley populations, particularly those from the multicomponent Sudanese site of al-Khiday. However, both samples also display intermediate affinities with northern and Sub-Saharan African populations. These results suggest that, for much of the Holocene, an admixed population inhabited the central Sahara, with a primarily East African/Nile Valley origin. Nevertheless, as the climate deteriorated, interactions with northern and Sub-Saharan African populations cannot be ruled out.

Ancestral Whispers

36,495 Aufrufe • vor 3 Tagen

AI Is Moving Beyond “Generating Videos” — Toward “Generating Worlds” Over the past two years, AI video models have advanced at an astonishing pace. From Runway and Pika to Sora and Veo, AI-generated videos have become increasingly realistic and more consistent with the physical laws of the real world. Many people believe the next objective is simply to generate videos that are longer, sharper, and more lifelike. But if we take a step back, we can see that the real transformation is not happening in video itself. It is happening in world models. What Is a World Model? In 1943, psychologist Kenneth Craik proposed an idea that would influence artificial intelligence research for decades. He argued that the human brain does not merely react to the outside world. Instead, it maintains an internal model of how the world works. Because we have this internal model, we can predict the outcome of an action before we actually take it. Before crossing a road, we estimate whether a car will pass by. Before catching a ball, we predict its trajectory. These abilities come from continuously simulating the world in our minds, rather than relying entirely on trial and error. This idea later became known by a more formal term: World Model. A world model does not describe a single image or a fixed video clip. It is an internal representation capable of continuously simulating the rules and dynamics of the real world. Why Is AI Research Turning Toward World Models? Because predicting “what comes next” is becoming increasingly central to how AI systems work. Language models predict the next token. Image models predict the next step in the denoising process. Video models predict the next frame. A world model, however, attempts to predict something broader: What should the world look like in the next moment? In 2018, David Ha and Jürgen Schmidhuber proposed in their paper World Models that an intelligent agent could first learn a model of the world, and then use that internal model to plan its actions. The Dreamer series later demonstrated that many complex tasks could be learned by training agents inside an “imagined world.” At the same time, the development of video models such as Sora and Veo led researchers to another realization: A model capable of continuously generating video has already learned, at least implicitly, many of the rules governing the real world. As a result, these two research directions have gradually begun to converge. But Video Is Not Yet a World This is where the distinction is often misunderstood. For a world model to support meaningful real-time interaction, it must solve several critical problems. Most video models today are essentially answering one question: What should the next frame look like? A true world model needs to answer much more: What happens if I take one step forward? If I walk behind a building and then return, will the building still be there? If I suddenly change the camera angle, will the entire space remain consistent? If I enter a command such as: “Summon a dragon.” Will the world respond immediately? In other words, a world model must do more than generate content. It must understand space. It must understand time. It must understand causality. And it must understand interaction. Moving from watching to participating is where the real difficulty of world models begins. World Models Are Entering the Interactive Era One of the latest attempts in this direction is Alaya World, recently open-sourced by Alaya World, or Alaya Lab. Instead of generating a fixed video clip, it generates a world that users can explore in real time. Users can begin with text, an image, or a video, enter the generated scene, move freely through it, and introduce new prompts at any moment during generation. The world responds immediately. According to the publicly released information, Alaya World provides: Real-time streaming generation at 720p and 24 FPS Stable continuous exploration for more than one minute The ability to switch prompts and trigger skills or events during generation Model weights and inference code released under the Apache 2.0 License Training code and datasets planned for future release What makes these capabilities important is not simply the technical specifications. It is that the generated “world” can now support continuous interaction. The official demo shows that users can genuinely control, transform, and explore the generated environment. AI Is Evolving From a Tool Into an Environment Over the past few years, most discussions around AI have focused on content generation. Generating text. Generating images. Generating videos. But world models raise a fundamentally different question: Can AI generate an environment that people can inhabit, explore, and continuously evolve? If the answer is yes, the impact will extend far beyond video generation. Game development, robotics training, embodied intelligence, digital twins, virtual production, and many other fields could be transformed by the development of world models. World models are still at a very early stage. Yet from Craik’s proposal of an internal mental model more than eighty years ago to the emergence of today’s interactive world-generation systems, a clear evolutionary path is beginning to take shape. Perhaps what AI is ultimately learning has never been limited to images, videos, or language. Perhaps it is learning the world itself. References GitHub: Technical Report:
0:10

AI Is Moving Beyond “Generating Videos” — Toward “Generating Worlds” Over the past two years, AI video models have advanced at an astonishing pace. From Runway and Pika to Sora and Veo, AI-generated videos have become increasingly realistic and more consistent with the physical laws of the real world. Many people believe the next objective is simply to generate videos that are longer, sharper, and more lifelike. But if we take a step back, we can see that the real transformation is not happening in video itself. It is happening in world models. What Is a World Model? In 1943, psychologist Kenneth Craik proposed an idea that would influence artificial intelligence research for decades. He argued that the human brain does not merely react to the outside world. Instead, it maintains an internal model of how the world works. Because we have this internal model, we can predict the outcome of an action before we actually take it. Before crossing a road, we estimate whether a car will pass by. Before catching a ball, we predict its trajectory. These abilities come from continuously simulating the world in our minds, rather than relying entirely on trial and error. This idea later became known by a more formal term: World Model. A world model does not describe a single image or a fixed video clip. It is an internal representation capable of continuously simulating the rules and dynamics of the real world. Why Is AI Research Turning Toward World Models? Because predicting “what comes next” is becoming increasingly central to how AI systems work. Language models predict the next token. Image models predict the next step in the denoising process. Video models predict the next frame. A world model, however, attempts to predict something broader: What should the world look like in the next moment? In 2018, David Ha and Jürgen Schmidhuber proposed in their paper World Models that an intelligent agent could first learn a model of the world, and then use that internal model to plan its actions. The Dreamer series later demonstrated that many complex tasks could be learned by training agents inside an “imagined world.” At the same time, the development of video models such as Sora and Veo led researchers to another realization: A model capable of continuously generating video has already learned, at least implicitly, many of the rules governing the real world. As a result, these two research directions have gradually begun to converge. But Video Is Not Yet a World This is where the distinction is often misunderstood. For a world model to support meaningful real-time interaction, it must solve several critical problems. Most video models today are essentially answering one question: What should the next frame look like? A true world model needs to answer much more: What happens if I take one step forward? If I walk behind a building and then return, will the building still be there? If I suddenly change the camera angle, will the entire space remain consistent? If I enter a command such as: “Summon a dragon.” Will the world respond immediately? In other words, a world model must do more than generate content. It must understand space. It must understand time. It must understand causality. And it must understand interaction. Moving from watching to participating is where the real difficulty of world models begins. World Models Are Entering the Interactive Era One of the latest attempts in this direction is Alaya World, recently open-sourced by Alaya World, or Alaya Lab. Instead of generating a fixed video clip, it generates a world that users can explore in real time. Users can begin with text, an image, or a video, enter the generated scene, move freely through it, and introduce new prompts at any moment during generation. The world responds immediately. According to the publicly released information, Alaya World provides: Real-time streaming generation at 720p and 24 FPS Stable continuous exploration for more than one minute The ability to switch prompts and trigger skills or events during generation Model weights and inference code released under the Apache 2.0 License Training code and datasets planned for future release What makes these capabilities important is not simply the technical specifications. It is that the generated “world” can now support continuous interaction. The official demo shows that users can genuinely control, transform, and explore the generated environment. AI Is Evolving From a Tool Into an Environment Over the past few years, most discussions around AI have focused on content generation. Generating text. Generating images. Generating videos. But world models raise a fundamentally different question: Can AI generate an environment that people can inhabit, explore, and continuously evolve? If the answer is yes, the impact will extend far beyond video generation. Game development, robotics training, embodied intelligence, digital twins, virtual production, and many other fields could be transformed by the development of world models. World models are still at a very early stage. Yet from Craik’s proposal of an internal mental model more than eighty years ago to the emergence of today’s interactive world-generation systems, a clear evolutionary path is beginning to take shape. Perhaps what AI is ultimately learning has never been limited to images, videos, or language. Perhaps it is learning the world itself. References GitHub: Technical Report:

雪踏乌云

113,347 Aufrufe • vor 1 Monat

When a spacecraft leaves Earth, it doesn’t just fire its engines and head straight to its destination. In many missions, especially those going beyond low Earth orbit, there’s a more subtle and elegant strategy at play, one that uses gravity itself as part of the navigation system. This is often called a gravity assist, or a slingshot maneuver. But in the case of missions like #Artemis II, what’s being used is a closely related idea known as a free-return trajectory. At first glance, it might sound simple: the spacecraft goes to the Moon, loops around it, and comes back. But the physics behind it is anything but simple. Instead of relying on continuous propulsion, the spacecraft follows a carefully calculated path through the gravitational field of the Earth–Moon system. It is launched with just the right speed and direction so that, as it approaches the Moon, the Moon’s gravity bends its trajectory. The spacecraft is effectively flung around the Moon, redirected onto a path that naturally brings it back toward Earth. No major engine burn is needed for the return. Small trajectory corrections may still be required, but gravity does the heavy lifting. That’s the key. This kind of trajectory is not just efficient, it’s also safe. If something goes wrong with the spacecraft’s engines or onboard systems, gravity itself ensures the return. It’s an inherent backup plan, built into the trajectory from the very beginning. The same fundamental idea appears in gravity assists used across the Solar System. When a spacecraft flies past a planet, it can gain or lose speed by exchanging momentum with that planet. From the spacecraft’s point of view, it’s as if it has been accelerated without using fuel. In reality, it has borrowed a tiny amount of orbital energy from the planet itself. That’s how missions like Voyager reached the outer planets, and how probes continue to explore regions far beyond what their onboard fuel alone would allow. But there’s an important distinction. An interplanetary gravity assist is typically used to change speed and direction, often increasing the spacecraft’s energy. A free-return trajectory, like the one used in Artemis II, is designed for something more specific: a path that naturally loops back to Earth without requiring additional propulsion. It’s less about gaining energy, and more about shaping a trajectory that guarantees a return. To understand why this works, it helps to stop thinking in straight lines. In space, motion follows curves defined by gravity. The spacecraft is constantly falling, first toward Earth, then toward the Moon, and then back toward Earth again. What looks like a loop is really a continuous free fall through a changing gravitational landscape. This way of navigating space reveals something deeper. We tend to think of engines as the drivers of motion, but once a spacecraft is on its way, gravity does most of the work. The art of spaceflight is not just about thrust. It’s about knowing when not to use it. #GoodLuck #Artemis NASA Artemis
0:11

When a spacecraft leaves Earth, it doesn’t just fire its engines and head straight to its destination. In many missions, especially those going beyond low Earth orbit, there’s a more subtle and elegant strategy at play, one that uses gravity itself as part of the navigation system. This is often called a gravity assist, or a slingshot maneuver. But in the case of missions like #Artemis II, what’s being used is a closely related idea known as a free-return trajectory. At first glance, it might sound simple: the spacecraft goes to the Moon, loops around it, and comes back. But the physics behind it is anything but simple. Instead of relying on continuous propulsion, the spacecraft follows a carefully calculated path through the gravitational field of the Earth–Moon system. It is launched with just the right speed and direction so that, as it approaches the Moon, the Moon’s gravity bends its trajectory. The spacecraft is effectively flung around the Moon, redirected onto a path that naturally brings it back toward Earth. No major engine burn is needed for the return. Small trajectory corrections may still be required, but gravity does the heavy lifting. That’s the key. This kind of trajectory is not just efficient, it’s also safe. If something goes wrong with the spacecraft’s engines or onboard systems, gravity itself ensures the return. It’s an inherent backup plan, built into the trajectory from the very beginning. The same fundamental idea appears in gravity assists used across the Solar System. When a spacecraft flies past a planet, it can gain or lose speed by exchanging momentum with that planet. From the spacecraft’s point of view, it’s as if it has been accelerated without using fuel. In reality, it has borrowed a tiny amount of orbital energy from the planet itself. That’s how missions like Voyager reached the outer planets, and how probes continue to explore regions far beyond what their onboard fuel alone would allow. But there’s an important distinction. An interplanetary gravity assist is typically used to change speed and direction, often increasing the spacecraft’s energy. A free-return trajectory, like the one used in Artemis II, is designed for something more specific: a path that naturally loops back to Earth without requiring additional propulsion. It’s less about gaining energy, and more about shaping a trajectory that guarantees a return. To understand why this works, it helps to stop thinking in straight lines. In space, motion follows curves defined by gravity. The spacecraft is constantly falling, first toward Earth, then toward the Moon, and then back toward Earth again. What looks like a loop is really a continuous free fall through a changing gravitational landscape. This way of navigating space reveals something deeper. We tend to think of engines as the drivers of motion, but once a spacecraft is on its way, gravity does most of the work. The art of spaceflight is not just about thrust. It’s about knowing when not to use it. #GoodLuck #Artemis NASA Artemis

Erika 

234,886 Aufrufe • vor 4 Monaten

Here’s how the Book of Enoch tells you when judgment, or the Geophysical Event, is near: The years become shorter, the rain is withheld, fruit ripens late, and the Moon and stars depart from their established order. This section is among the oldest parts of the book, and copies of it were found in Cave 4 at Qumran. So, what does this mean that even an AI like Grok fails to understand? Let me explain it to you... The Moon and stars going “out of order” is connected to axial and apsidal precession. The rising of the stars that once announced the seasons gradually ceases to align with the same seasonal events. For example, in ancient Egypt, the heliacal rising of Sirius, its first appearance in the dawn sky after a period of invisibility, announced the approaching annual flooding of the Nile and the beginning of the Egyptian New Year. However, because of axial precession, this rising would eventually no longer announce the flooding at the same time. The celestial markers slowly shift in relation to the seasons. Because of this slow change in the Earth’s orientation toward the Sun, along with small changes in the distance between the Earth and the Sun, the climate begins to change and shift gradually. However, these changes build toward a tipping point, after which the climate completely collapses and reorganizes. That is how the Sahara became a desert around 6,000 years ago. These climatic cycles are known as Bond events. Hapgood suggested that these slow changes affect the buildup and melting of ice, which could then potentially cause the Earth’s outer crust to slip, triggering the biblical flood or causing the oceans to spill across the continents! Today’s climate change is connected to axial and apsidal precession and the approach toward a tipping point. The Book of Enoch is correct. We are close to Judgment! I made a short video to show you how the stars change their positions relative to the Moon, the Sun, and the five visible planets over a long period of time. That is why the Book of Enoch has been sidelined by the elites. They do not want you to know the truth!
0:13

Here’s how the Book of Enoch tells you when judgment, or the Geophysical Event, is near: The years become shorter, the rain is withheld, fruit ripens late, and the Moon and stars depart from their established order. This section is among the oldest parts of the book, and copies of it were found in Cave 4 at Qumran. So, what does this mean that even an AI like Grok fails to understand? Let me explain it to you... The Moon and stars going “out of order” is connected to axial and apsidal precession. The rising of the stars that once announced the seasons gradually ceases to align with the same seasonal events. For example, in ancient Egypt, the heliacal rising of Sirius, its first appearance in the dawn sky after a period of invisibility, announced the approaching annual flooding of the Nile and the beginning of the Egyptian New Year. However, because of axial precession, this rising would eventually no longer announce the flooding at the same time. The celestial markers slowly shift in relation to the seasons. Because of this slow change in the Earth’s orientation toward the Sun, along with small changes in the distance between the Earth and the Sun, the climate begins to change and shift gradually. However, these changes build toward a tipping point, after which the climate completely collapses and reorganizes. That is how the Sahara became a desert around 6,000 years ago. These climatic cycles are known as Bond events. Hapgood suggested that these slow changes affect the buildup and melting of ice, which could then potentially cause the Earth’s outer crust to slip, triggering the biblical flood or causing the oceans to spill across the continents! Today’s climate change is connected to axial and apsidal precession and the approach toward a tipping point. The Book of Enoch is correct. We are close to Judgment! I made a short video to show you how the stars change their positions relative to the Moon, the Sun, and the five visible planets over a long period of time. That is why the Book of Enoch has been sidelined by the elites. They do not want you to know the truth!

Open Minded Approach

151,664 Aufrufe • vor 3 Tagen

Depth Any Video with Scalable Synthetic Data AI physicists and chemists continue to make strides in depth estimation from video. Check out this new paper featuring some impressive examples. See the thread for more details (unfortunately no code yet). Abstract: Video depth estimation has long been hindered by the scarcity of consistent and scalable ground truth data, leading to inconsistent and unreliable results. In this paper, we introduce Depth Any Video, a model that tackles the challenge through two key innovations. First, we develop a scalable synthetic data pipeline, capturing real-time video depth data from diverse game environments, yielding 40,000 video clips of 5-second duration, each with precise depth annotations. Second, we leverage the powerful priors of generative video diffusion models to handle real-world videos effectively, integrating advanced techniques such as rotary position encoding and flow matching to further enhance flexibility and efficiency. Unlike previous models, which are limited to fixed-length video sequences, our approach introduces a novel mixed-duration training strategy that handles videos of varying lengths and performs robustly across different frame rates 0 - even on single frames. At inference, we propose a depth interpolation method that enables our model to infer high-resolution video depth across sequences of up to 150 frames. Our model outperforms all previous generative depth models in terms of spatial accuracy and temporal consistency.
0:13

Depth Any Video with Scalable Synthetic Data AI physicists and chemists continue to make strides in depth estimation from video. Check out this new paper featuring some impressive examples. See the thread for more details (unfortunately no code yet). Abstract: Video depth estimation has long been hindered by the scarcity of consistent and scalable ground truth data, leading to inconsistent and unreliable results. In this paper, we introduce Depth Any Video, a model that tackles the challenge through two key innovations. First, we develop a scalable synthetic data pipeline, capturing real-time video depth data from diverse game environments, yielding 40,000 video clips of 5-second duration, each with precise depth annotations. Second, we leverage the powerful priors of generative video diffusion models to handle real-world videos effectively, integrating advanced techniques such as rotary position encoding and flow matching to further enhance flexibility and efficiency. Unlike previous models, which are limited to fixed-length video sequences, our approach introduces a novel mixed-duration training strategy that handles videos of varying lengths and performs robustly across different frame rates 0 - even on single frames. At inference, we propose a depth interpolation method that enables our model to infer high-resolution video depth across sequences of up to 150 frames. Our model outperforms all previous generative depth models in terms of spatial accuracy and temporal consistency.

MrNeRF

27,428 Aufrufe • vor 1 Jahr

Slow Progress is the New Reality of Modern Wars There are many people commenting on Russian difficulties and coming up with numerous reasons for their alleged collapse. I absolutely do not see any collapse; on the contrary, Russia has already normalized a state of war in Ukraine, which boosts its economic and industrial growth. The difficulties faced by Russia today are the same as at the beginning of the war, with soldiers having to spend up to half of their initial salaries on their own equipment, delays in pensions, and a series of other problems that are mirrored on the Ukrainian side, even with all the support from allies. The war today is much less lethal than it was months ago, and both Russian and Ukrainian losses have significantly decreased during this phase. Lethality has fallen, but Russian advances persist. And why are they slow? Is it a Ukrainian tactic? No. They are slow because they are accompanied by the deployment of communication infrastructure like signal repeaters for drones, the advancement of artillery guided by drones, maneuvers using FAB and more recently ODAB bombs against Ukrainian drone operators' facilities, infiltration by reconnaissance teams, saboteurs, and a series of protocols they have developed. That conventional war with rapid advances no longer exists, and in the context of a battlefield dominated by drones, it is unlikely to return. Modern wars will have slow progress, as seen in the attacks and counter-attacks of the Russians and Ukrainians in this war. Contemporary military forces are still unable to see this new reality of war. When analyzing the Russian advance, it's important to consider that all these maneuvers take days to unfold, but the point is that the Russians have already adapted to this pace, not bothered whether a particular advance will take 3 weeks or 3 months. They advance continuously following tactical protocols with little threat from Ukrainian forces, which, without new equipment, have had their defensive tactics remain almost unchanged over the past two years, relying mainly on drones and making it easier for Russian studies of countermeasures. And what can Ukraine do in this situation? I see few options, but one would be to delay the Russian advance with a good number of missiles, though personally, I find it unlikely that they will reach Ukrainian hands. It wouldn't have the power to change the balance of the war, but it would guarantee more time, which is important because the battlefield is dynamic, and the implementation of new tactics based on innovative weapons can change everything overnight. A war that seems almost lost today could take a different turn in weeks. Time is crucial for Ukraine, which has fronts only about 130 km from cities like Zaporizhia and Dnipro.
0:17

Slow Progress is the New Reality of Modern Wars There are many people commenting on Russian difficulties and coming up with numerous reasons for their alleged collapse. I absolutely do not see any collapse; on the contrary, Russia has already normalized a state of war in Ukraine, which boosts its economic and industrial growth. The difficulties faced by Russia today are the same as at the beginning of the war, with soldiers having to spend up to half of their initial salaries on their own equipment, delays in pensions, and a series of other problems that are mirrored on the Ukrainian side, even with all the support from allies. The war today is much less lethal than it was months ago, and both Russian and Ukrainian losses have significantly decreased during this phase. Lethality has fallen, but Russian advances persist. And why are they slow? Is it a Ukrainian tactic? No. They are slow because they are accompanied by the deployment of communication infrastructure like signal repeaters for drones, the advancement of artillery guided by drones, maneuvers using FAB and more recently ODAB bombs against Ukrainian drone operators' facilities, infiltration by reconnaissance teams, saboteurs, and a series of protocols they have developed. That conventional war with rapid advances no longer exists, and in the context of a battlefield dominated by drones, it is unlikely to return. Modern wars will have slow progress, as seen in the attacks and counter-attacks of the Russians and Ukrainians in this war. Contemporary military forces are still unable to see this new reality of war. When analyzing the Russian advance, it's important to consider that all these maneuvers take days to unfold, but the point is that the Russians have already adapted to this pace, not bothered whether a particular advance will take 3 weeks or 3 months. They advance continuously following tactical protocols with little threat from Ukrainian forces, which, without new equipment, have had their defensive tactics remain almost unchanged over the past two years, relying mainly on drones and making it easier for Russian studies of countermeasures. And what can Ukraine do in this situation? I see few options, but one would be to delay the Russian advance with a good number of missiles, though personally, I find it unlikely that they will reach Ukrainian hands. It wouldn't have the power to change the balance of the war, but it would guarantee more time, which is important because the battlefield is dynamic, and the implementation of new tactics based on innovative weapons can change everything overnight. A war that seems almost lost today could take a different turn in weeks. Time is crucial for Ukraine, which has fronts only about 130 km from cities like Zaporizhia and Dnipro.

Patricia Marins

39,318 Aufrufe • vor 1 Jahr

Facial reconstruction of a hunter-gatherer from Georgia His closest cranial affinities are with two EHG individuals, as well as with a selection of Proto-Indo-Iranic Balanovo culture crania. Given his location in northwest Georgia, the presence of a pure EHG individual in the northwest Caucasus (Satanay), and the observed cranial and archaeological affinities, it is possible that this individual was EHG or EHG-admixed CHG. His remains were presented in Mikhail Gerasimov’s 1951 book as those of a “Neolithic man from Georgia,” without any clarification regarding the precise location of the find. Decades later, in a 2020 publication, the anthropologist David Kandelaki clarified that the individual was discovered in Abkhazia, at a cave known as the “Cold Grotto.” The individual was described by Mikhail Gerasimov as gracile, with distinctive traits that place him closer to Sub-Saharan African and other equatorial populations. Gerasimov noted a close resemblance to an EHG from Gavrilovka, as well as to several crania from the Proto-Indo-Iranian Balanovo culture, which likewise exhibit this pronounced Sub-Saharan/equatorial morphological tendency. In a 2017 publication, the anthropologist Alexander Khokhlov also observed that a female EHG skull from Chekalino closely matches this forager from Cold Grotto. Considering the Chekalino and Gavrilovka affinities, and the presence of a genetically pure EHG individual from the Satanay Grotto in the Northwest Caucasus, along with archaeological links between the Apiancha forager group (to which the Cold Grotto individual belonged) and the Gubs group (associated with the Satanay), it is possible that this reconstructed individual harbored a significant degree of EHG ancestry, or was fully EHG. The individual was described by the anthropologist Mikhail Gerasimov as follows: It appears that this skull may date to the period of Early Neolithic formation, or possibly to an earlier time (perhaps Epipaleolithic/Tardenoisian). Morphologically, the skull is highly distinctive and displays traits uncharacteristic of later Caucasian populations. This prompted us to present a description of the skull and a graphic reconstruction of the head, in the hope that this preliminary publication will later help clarify the time and context of the find. Despite the abundance of Upper Paleolithic and Early Neolithic sites in Georgia, virtually nothing is known about the anthropological type of the population of this period, aside from a lower jaw, apparently belonging to a very young woman, from the Aurignacian layer of Devís-Khvreli Cave. The skull is small, gracile, and light, with shortened proportions and rounded outlines, and weak surface relief. Cranial sutures are obliterated, suggesting an age of approximately 25 years. Cranial dimensions include a small length of 176 mm, small cranial breadth of 136 mm, and a large bizygomatic width of 137 mm. The frontal bone is broad and convex; the sagittal keel and frontal eminences are weakly expressed. Supraorbital ridges are broad and moderately developed. The nasal part of the frontal bone is wide and very short. The parietal bones are shortened, and the occipital bone is fractured. The mastoid process is large and robust. Facial height is relatively great, with shortened proportions and notable depth. The face occupies an intermediate position between pentagonal and triangular forms. It is gracile, small, weakly profiled, and prognathic. The nose is low, narrow, and projecting; the chin is weakly developed. The orbits are irregularly oval, with thin, blunted margins, a slightly inclined profile, horizontal orientation, small eyes. Zygomatic bones are thin and weakly profiled. Canine fossae are deep; the alveolar region is high, broad, and prognathic. The total combination of descriptive and metric traits excludes any indication of East Asian affinity. In its foundation, the skull is closest to the Europoid type; however, a number of features - weak nasal projection, shortened proportions of the piriform aperture, pronounced general and alveolar prognathism, and maxillary dental prognathism - impart an SSA appearance to the skull. The proposed graphic reconstruction further emphasizes these SSA traits against an overall Europoid background. Such features have been noted in early variants of Homo sapiens, and in this case likely reflect incomplete differentiation of Euro-African traits. This may indicate either the antiquity of the find or the persistence of relic features of an ancient anthropological form. Although people with clearly expressed later Cro-Magnon traits already lived in the Crimea and the Dnieper region at this time, the Georgian skull shows no such characteristics. Despite its overall gracility, it retains certain archaic features, including relatively strong glabellar projection, developed supraorbital ridges, vertical orbital profiling, and facial prognathism. This specimen represents a local variant of Homo sapiens not typical of the Neolithic or Late Archaic period. Comparable features are observed in skulls from Gavrilovka and in certain specimens of the Balanovo culture.
0:10

Facial reconstruction of a hunter-gatherer from Georgia His closest cranial affinities are with two EHG individuals, as well as with a selection of Proto-Indo-Iranic Balanovo culture crania. Given his location in northwest Georgia, the presence of a pure EHG individual in the northwest Caucasus (Satanay), and the observed cranial and archaeological affinities, it is possible that this individual was EHG or EHG-admixed CHG. His remains were presented in Mikhail Gerasimov’s 1951 book as those of a “Neolithic man from Georgia,” without any clarification regarding the precise location of the find. Decades later, in a 2020 publication, the anthropologist David Kandelaki clarified that the individual was discovered in Abkhazia, at a cave known as the “Cold Grotto.” The individual was described by Mikhail Gerasimov as gracile, with distinctive traits that place him closer to Sub-Saharan African and other equatorial populations. Gerasimov noted a close resemblance to an EHG from Gavrilovka, as well as to several crania from the Proto-Indo-Iranian Balanovo culture, which likewise exhibit this pronounced Sub-Saharan/equatorial morphological tendency. In a 2017 publication, the anthropologist Alexander Khokhlov also observed that a female EHG skull from Chekalino closely matches this forager from Cold Grotto. Considering the Chekalino and Gavrilovka affinities, and the presence of a genetically pure EHG individual from the Satanay Grotto in the Northwest Caucasus, along with archaeological links between the Apiancha forager group (to which the Cold Grotto individual belonged) and the Gubs group (associated with the Satanay), it is possible that this reconstructed individual harbored a significant degree of EHG ancestry, or was fully EHG. The individual was described by the anthropologist Mikhail Gerasimov as follows: It appears that this skull may date to the period of Early Neolithic formation, or possibly to an earlier time (perhaps Epipaleolithic/Tardenoisian). Morphologically, the skull is highly distinctive and displays traits uncharacteristic of later Caucasian populations. This prompted us to present a description of the skull and a graphic reconstruction of the head, in the hope that this preliminary publication will later help clarify the time and context of the find. Despite the abundance of Upper Paleolithic and Early Neolithic sites in Georgia, virtually nothing is known about the anthropological type of the population of this period, aside from a lower jaw, apparently belonging to a very young woman, from the Aurignacian layer of Devís-Khvreli Cave. The skull is small, gracile, and light, with shortened proportions and rounded outlines, and weak surface relief. Cranial sutures are obliterated, suggesting an age of approximately 25 years. Cranial dimensions include a small length of 176 mm, small cranial breadth of 136 mm, and a large bizygomatic width of 137 mm. The frontal bone is broad and convex; the sagittal keel and frontal eminences are weakly expressed. Supraorbital ridges are broad and moderately developed. The nasal part of the frontal bone is wide and very short. The parietal bones are shortened, and the occipital bone is fractured. The mastoid process is large and robust. Facial height is relatively great, with shortened proportions and notable depth. The face occupies an intermediate position between pentagonal and triangular forms. It is gracile, small, weakly profiled, and prognathic. The nose is low, narrow, and projecting; the chin is weakly developed. The orbits are irregularly oval, with thin, blunted margins, a slightly inclined profile, horizontal orientation, small eyes. Zygomatic bones are thin and weakly profiled. Canine fossae are deep; the alveolar region is high, broad, and prognathic. The total combination of descriptive and metric traits excludes any indication of East Asian affinity. In its foundation, the skull is closest to the Europoid type; however, a number of features - weak nasal projection, shortened proportions of the piriform aperture, pronounced general and alveolar prognathism, and maxillary dental prognathism - impart an SSA appearance to the skull. The proposed graphic reconstruction further emphasizes these SSA traits against an overall Europoid background. Such features have been noted in early variants of Homo sapiens, and in this case likely reflect incomplete differentiation of Euro-African traits. This may indicate either the antiquity of the find or the persistence of relic features of an ancient anthropological form. Although people with clearly expressed later Cro-Magnon traits already lived in the Crimea and the Dnieper region at this time, the Georgian skull shows no such characteristics. Despite its overall gracility, it retains certain archaic features, including relatively strong glabellar projection, developed supraorbital ridges, vertical orbital profiling, and facial prognathism. This specimen represents a local variant of Homo sapiens not typical of the Neolithic or Late Archaic period. Comparable features are observed in skulls from Gavrilovka and in certain specimens of the Balanovo culture.

Ancestral Whispers

30,362 Aufrufe • vor 7 Monaten

Model-Free Reinforcement Learning (MFRL) has been alluring, especially with supercharged compute with physics on GPU. However, the methods use 0-th order gradients, and are often not the best optimizers. Can we do better than PPO in continuous control for robotics? Turns out yes! 🥳 tl;dr: Faster, better RL than PPO in continuous control 💪 The answer lies in using more information from the simulation. We are juicing the simulation on GPU as it is, why not use it for gradients as well? This has been a driving question in a series of our works. We first studied this problem in ICLR 2022 paper on Short Horizon Actor Critic Naive gradient based methods are stuck in local minima and have exploding/vanishing gradients. SHAC solved this problem truncated rollouts and model based value estimation, where the model is Differentiable Sim. This boosted sample efficiency and wall-clock time immensely especially in high dimensional systems such as humanoids Yet, given enough compute PPO often caught up. Our follow up paper on on Adaptive Horizon Actor Critic at ICML 2024 discovers the cause and provides a fix. However, we find that even when given ground-truth dynamics, not all gradients are useful due to sample error. 1st-Order Model-Based Reinforcement Learning methods employing differentiable simulation provide gradients with reduced variance but are susceptible to bias in scenarios involving stiff dynamics, such as physical contact. We find that back-propagating through contact and long trajectories drastically reduces gradient accuracy. Using this insight, we propose AHAC to dynamically adapt its roll-out horizon to avoid differentiating through stiff contact. AHAC is a first-order model-based RL algorithm that learns high-dimensional tasks in minutes (wall clock) and outperforms PPO by 40%, even in the limit of data provided to PPO. This work is led by Ignat Georgiev alongside Krishnan Srinivasan, Jie Xu, Eric Heiden and ample assistance from warp team at NVIDIA Robotics (Miles Macklin)
0:10

Model-Free Reinforcement Learning (MFRL) has been alluring, especially with supercharged compute with physics on GPU. However, the methods use 0-th order gradients, and are often not the best optimizers. Can we do better than PPO in continuous control for robotics? Turns out yes! 🥳 tl;dr: Faster, better RL than PPO in continuous control 💪 The answer lies in using more information from the simulation. We are juicing the simulation on GPU as it is, why not use it for gradients as well? This has been a driving question in a series of our works. We first studied this problem in ICLR 2022 paper on Short Horizon Actor Critic Naive gradient based methods are stuck in local minima and have exploding/vanishing gradients. SHAC solved this problem truncated rollouts and model based value estimation, where the model is Differentiable Sim. This boosted sample efficiency and wall-clock time immensely especially in high dimensional systems such as humanoids Yet, given enough compute PPO often caught up. Our follow up paper on on Adaptive Horizon Actor Critic at ICML 2024 discovers the cause and provides a fix. However, we find that even when given ground-truth dynamics, not all gradients are useful due to sample error. 1st-Order Model-Based Reinforcement Learning methods employing differentiable simulation provide gradients with reduced variance but are susceptible to bias in scenarios involving stiff dynamics, such as physical contact. We find that back-propagating through contact and long trajectories drastically reduces gradient accuracy. Using this insight, we propose AHAC to dynamically adapt its roll-out horizon to avoid differentiating through stiff contact. AHAC is a first-order model-based RL algorithm that learns high-dimensional tasks in minutes (wall clock) and outperforms PPO by 40%, even in the limit of data provided to PPO. This work is led by Ignat Georgiev alongside Krishnan Srinivasan, Jie Xu, Eric Heiden and ample assistance from warp team at NVIDIA Robotics (Miles Macklin)

Animesh Garg

52,308 Aufrufe • vor 2 Jahren

Part Two (2/2): In order to graze or pierce the top of the ear, the round would had to have been fired directly in front or behind, not at a perpendicular angle. First, You can know this is part of plan by Laws and Orders and how this sets up the finale. Second, you can read articles of the bullet whizzing by President Trump at the angle and trajectory reported by CNN… So, where did that bullet thump in the same time sequence when the other two bullets hit their targets sooner at the same angle and trajectory? Also, CNN says the snipers returned fire… There were 5 rounds in return, not in Burst Rounds, all single 1-1-1-1-1. The return fire by the “snipers” sounds exactly the same as the first 3 rounds fired at PDJT. And they sound NOTHING like an AR-15 whatsoever. Then there’s one lone round 11 seconds after the 5 rounds returned by “snipers” who are all around and had the target identified as the media says “they were told by Secret Service to hold their fire until the shooter engaged”… PLUS those on the roof behind President Trump were closer to the location of the gunman than PDJT, so, 350-450 feet away. So, are you telling me that trained snipers needed more than one round for a lone shooter? Are you telling me that trained snipers with an identified target were told to “stand down” until engagement? Are you tellling me that that trained snipers with an identified target allowed a lone round 11 seconds after return engagement? And/or needed another round 11 seconds after 5 returned on a lone wolf shooter? You clearly know NOTHING about snipers if you say ‘yes’ on one, a combo, or all of those. That alone doesn’t even need to follow up with the location, sound, angle, trajectory, but just because… A perpendicularly shot given the evidence… wouldn’t be the top of the ear pierced. This was ALL for those who have no clue what’s going on by Laws and Orders the past 7.5 years of a Military Occupation and COOP. This sets up the grand finale of ALL the evidence that will be brought against the dudes going to GITMO for the normies. Normies and “so-called Patriots” who reacted to this: stop listening to people who don’t know the Plan which is a World Special Operation with multi-faceted layers of operations taking place all outlined in Military and Federal Laws and Orders that clearly outline and define a Military Occupation and Continuity of Operations Plan. Which means stop listening to people who just want some likes, shares, and to “be the face” that you run to when it’s all about them and not an Oath. The evidence on this is clear and in order to prove it you needed: Location Angle Trajectory Velocity Weapon Style Caliber The same as “politics” requires: Laws Orders Acts Bills Codes Or you just have a lot of 🗣️💨💩 Trust the Plan, we are on the home stretch 💯🐂🇺🇸
0:23

Part Two (2/2): In order to graze or pierce the top of the ear, the round would had to have been fired directly in front or behind, not at a perpendicular angle. First, You can know this is part of plan by Laws and Orders and how this sets up the finale. Second, you can read articles of the bullet whizzing by President Trump at the angle and trajectory reported by CNN… So, where did that bullet thump in the same time sequence when the other two bullets hit their targets sooner at the same angle and trajectory? Also, CNN says the snipers returned fire… There were 5 rounds in return, not in Burst Rounds, all single 1-1-1-1-1. The return fire by the “snipers” sounds exactly the same as the first 3 rounds fired at PDJT. And they sound NOTHING like an AR-15 whatsoever. Then there’s one lone round 11 seconds after the 5 rounds returned by “snipers” who are all around and had the target identified as the media says “they were told by Secret Service to hold their fire until the shooter engaged”… PLUS those on the roof behind President Trump were closer to the location of the gunman than PDJT, so, 350-450 feet away. So, are you telling me that trained snipers needed more than one round for a lone shooter? Are you telling me that trained snipers with an identified target were told to “stand down” until engagement? Are you tellling me that that trained snipers with an identified target allowed a lone round 11 seconds after return engagement? And/or needed another round 11 seconds after 5 returned on a lone wolf shooter? You clearly know NOTHING about snipers if you say ‘yes’ on one, a combo, or all of those. That alone doesn’t even need to follow up with the location, sound, angle, trajectory, but just because… A perpendicularly shot given the evidence… wouldn’t be the top of the ear pierced. This was ALL for those who have no clue what’s going on by Laws and Orders the past 7.5 years of a Military Occupation and COOP. This sets up the grand finale of ALL the evidence that will be brought against the dudes going to GITMO for the normies. Normies and “so-called Patriots” who reacted to this: stop listening to people who don’t know the Plan which is a World Special Operation with multi-faceted layers of operations taking place all outlined in Military and Federal Laws and Orders that clearly outline and define a Military Occupation and Continuity of Operations Plan. Which means stop listening to people who just want some likes, shares, and to “be the face” that you run to when it’s all about them and not an Oath. The evidence on this is clear and in order to prove it you needed: Location Angle Trajectory Velocity Weapon Style Caliber The same as “politics” requires: Laws Orders Acts Bills Codes Or you just have a lot of 🗣️💨💩 Trust the Plan, we are on the home stretch 💯🐂🇺🇸

Derek Johnson

271,656 Aufrufe • vor 2 Jahren

Facial reconstruction of a Neolithic man from Ukraine In 1949–1950, M. Ya. Rudinsky carried out excavations of a large Neolithic burial ground that he had discovered earlier, known as the Volnish burial ground. Based on the type of burials, the position of the skeletons, the grave goods, and the abundance of red ochre, this burial ground was closely related to the well-known Mariupol cemetery. As in the Mariupol burial ground, the burials here are arranged in dense rows, closely packed next to one another. The majority of the interments are accompanied by rich grave inventories. All skeletons lie on their backs, with the arms tightly pressed to the body and the legs extended. The inventory is represented by a large number of stone and bone tools. In addition, fish and animal bones were often found on and around the skeletons. The skull, densely covered in red ochre, was described by Mikhail Gerasimov as large and massive, with a very great cranial length of 196 mm, a small cranial breadth of 136 mm, and a wide cheek breadth of 142 mm. Mikhail Gerasimov describes the skull as follows: The brow ridges protrude strongly; their projection exceeds that of the glabella. The brow ridges are wide and fused with the upper margins of the orbits. The bases of the zygomatic arches are wide. The zygomatic processes of the temporal bones are massive. The temporal lines are very well developed. The mastoid processes are very large and wide; their surface is flat. The occipital bone is long, very narrow, and laterally compressed in the area of the superior nuchal line. The occipital protuberance is weakly expressed; it is rounded and has a smooth surface. The face is pentagonal in shape, very high, with wide and massive cheekbones and strongly protruding angles of the heavy lower jaw; it is strongly profiled. The nasal bridge is high. The orbits are small. The alveolar part is low and orthognathic. The chin is strongly projecting. The orbits are rectangular, with strongly rounded corners. The orbital margins are thickened. The lower orbital margins are elevated. The eye sockets are closed. The lacrimal fossae are deepened. The supraorbital tubercles are weakly expressed and represented by broad, flat surfaces. The inclination of the palpebral fissure is slight. The frontal orientation of the orbits is elevated. The orbital profile is inclined. The zygomatic bones are massive and very wide. The lower portions of the zygomatic bones are displaced forward and elevated. The zygomatic tubercles are strongly developed. The frontal processes of the zygomatic bones are wide and flat. The canine fossae are weakly expressed. The areas for muscle attachment are well developed. The maxillary notches are deep. The alveolar part is very low (12 mm) and orthognathic. The teeth are wide and short, set orthognathically. The height of the first incisor is 6.5 mm. Wear on the incisors and canines has reached complete cross-section of the tooth. The mandible is large, with strongly developed angles, and is very massive. The body of the mandible is very high. The horizontal rami are weakly flared. The mental protuberance reaches 9 points on the scale. The rami of the mandible are very wide and high and diverge at a right angle. The mandibular angles are rounded and strongly everted, with a pronounced crest. The coronoid processes of the mandible are also everted. In terms of its anthropological characteristics, this represents the same type that is already well known to us from skulls from Murzak-Koba, Sursky Island (Type A), and Vinogradny Island. Since the archaeological material has not yet been published, we are currently deprived of the opportunity to precisely establish the chronological position of this individual. If we assume that this burial ground dates to the same period as the early burials of the Mariupol cemetery, then it belongs to the period of the developed Neolithic stage. This gives us the right to state that the anthropological type of ancient Cro-Magnons was conservatively preserved in remote regions up until the 4th–3rd millennia BC. Apparently, survivals of this same anthropological type can also be traced in the skulls of the Yamnaya culture of this region.
0:10

Facial reconstruction of a Neolithic man from Ukraine In 1949–1950, M. Ya. Rudinsky carried out excavations of a large Neolithic burial ground that he had discovered earlier, known as the Volnish burial ground. Based on the type of burials, the position of the skeletons, the grave goods, and the abundance of red ochre, this burial ground was closely related to the well-known Mariupol cemetery. As in the Mariupol burial ground, the burials here are arranged in dense rows, closely packed next to one another. The majority of the interments are accompanied by rich grave inventories. All skeletons lie on their backs, with the arms tightly pressed to the body and the legs extended. The inventory is represented by a large number of stone and bone tools. In addition, fish and animal bones were often found on and around the skeletons. The skull, densely covered in red ochre, was described by Mikhail Gerasimov as large and massive, with a very great cranial length of 196 mm, a small cranial breadth of 136 mm, and a wide cheek breadth of 142 mm. Mikhail Gerasimov describes the skull as follows: The brow ridges protrude strongly; their projection exceeds that of the glabella. The brow ridges are wide and fused with the upper margins of the orbits. The bases of the zygomatic arches are wide. The zygomatic processes of the temporal bones are massive. The temporal lines are very well developed. The mastoid processes are very large and wide; their surface is flat. The occipital bone is long, very narrow, and laterally compressed in the area of the superior nuchal line. The occipital protuberance is weakly expressed; it is rounded and has a smooth surface. The face is pentagonal in shape, very high, with wide and massive cheekbones and strongly protruding angles of the heavy lower jaw; it is strongly profiled. The nasal bridge is high. The orbits are small. The alveolar part is low and orthognathic. The chin is strongly projecting. The orbits are rectangular, with strongly rounded corners. The orbital margins are thickened. The lower orbital margins are elevated. The eye sockets are closed. The lacrimal fossae are deepened. The supraorbital tubercles are weakly expressed and represented by broad, flat surfaces. The inclination of the palpebral fissure is slight. The frontal orientation of the orbits is elevated. The orbital profile is inclined. The zygomatic bones are massive and very wide. The lower portions of the zygomatic bones are displaced forward and elevated. The zygomatic tubercles are strongly developed. The frontal processes of the zygomatic bones are wide and flat. The canine fossae are weakly expressed. The areas for muscle attachment are well developed. The maxillary notches are deep. The alveolar part is very low (12 mm) and orthognathic. The teeth are wide and short, set orthognathically. The height of the first incisor is 6.5 mm. Wear on the incisors and canines has reached complete cross-section of the tooth. The mandible is large, with strongly developed angles, and is very massive. The body of the mandible is very high. The horizontal rami are weakly flared. The mental protuberance reaches 9 points on the scale. The rami of the mandible are very wide and high and diverge at a right angle. The mandibular angles are rounded and strongly everted, with a pronounced crest. The coronoid processes of the mandible are also everted. In terms of its anthropological characteristics, this represents the same type that is already well known to us from skulls from Murzak-Koba, Sursky Island (Type A), and Vinogradny Island. Since the archaeological material has not yet been published, we are currently deprived of the opportunity to precisely establish the chronological position of this individual. If we assume that this burial ground dates to the same period as the early burials of the Mariupol cemetery, then it belongs to the period of the developed Neolithic stage. This gives us the right to state that the anthropological type of ancient Cro-Magnons was conservatively preserved in remote regions up until the 4th–3rd millennia BC. Apparently, survivals of this same anthropological type can also be traced in the skulls of the Yamnaya culture of this region.

Ancestral Whispers

54,961 Aufrufe • vor 7 Monaten

vPay offshore accounts and physical cards have been getting field-tested IRL for a while now, and we’ll open them to the public as soon as we’re fully confident in the UX. But before offshore accounts go public, I want to address a few points: Some might point out that - vPay isn’t the first crypto card - vPay doesn’t have the lowest fees - So why choose vPay instead of the Coinbase 🛡️ Card or MetaMask 🦊 Card or KAST or or Tria, or any of the other big names? Now to address: Privacy | The biggest differentiator that sets vPay completely apart is Private Banking. The majority of the crypto card providers on the market use Rain infra. Even if you’ve never heard of them, that's what your favorite "NeoBank" uses. And due to their legal jurisdictions, they will report your finances to authorities since they're CRS and FACTA compliant. We are not. As an OmniBank, we work with different banking partners, and although KYC is required to use our services, our offshore banks are non-CRS and non-FACTA. Tax reporting is the responsibility and choice of the user. Offshore Accounts vs Physical Cards | I've tried to highlight this a few times so far. vPay has 3 offerings on the banking side of things. Virtual cards - live now. Physical cards - coming Q1 2026. The first two are similar to what everyone else on the market offers. The offshore accounts are not. which are coming this week. They allow unlimited spending, ATM withdrawals, and international SWIFT transfers, which very few “Neobanks” provide. Offshore accounts are coming this week. Self-Custody | We're not 100% non-custodial yet, as that is near impossible at the moment but it's something we're working towards. And we try to keep the users' self-custodial wallets in the loop as much as possible for maximum control. Those who have tried the vPay app know that almost every move asks for permission from their wallet, and we always encourage users to keep their funds in their non-custodial wallets until the very last moment, since our top-ups usually only take seconds to a minute to process. Fees | All of the card providers mentioned above either raised millions from VCs or in presales or have a huge org backing them. We have neither. vPay was self-funded and community-owned since day 1, launched under Virtuals Protocol Genesis V1 launch model, an objectively bad launch model and hugely unfavorable toward project teams. So even though vPay has been generating revenue and profitable from early on, we do not have the luxury of offering 0% fees yet, since they're mostly a marketing gimmick paid for by millions in VC money and not a sustainable business model for early-stage companies. What we're working towards instead, is true co-ownership of vPay and revenue-share with users. OmniBank vs NeoBank | I’m not a fan of the term “NeoBank.” It implies just a bank, but make it crypto. That’s not vPay. Our goals have always been clear: A) Anything and everything users need to do with their money and assets, both Web2 and Web3, all in one hub. Powered by a constellation of partner agents. The cards and the bank accounts are just the foundation. B) To eventually build independent financial rails for crypto and decouple from the chokehold of Visa/Mastercard. vLink is the first step toward this vision. This turned out to be a rather long tweet, but context matters. Questions and feedback welcome in replies or DMs. See you all with your vPay vCards very soon.
0:08

vPay offshore accounts and physical cards have been getting field-tested IRL for a while now, and we’ll open them to the public as soon as we’re fully confident in the UX. But before offshore accounts go public, I want to address a few points: Some might point out that - vPay isn’t the first crypto card - vPay doesn’t have the lowest fees - So why choose vPay instead of the Coinbase 🛡️ Card or MetaMask 🦊 Card or KAST or or Tria, or any of the other big names? Now to address: Privacy | The biggest differentiator that sets vPay completely apart is Private Banking. The majority of the crypto card providers on the market use Rain infra. Even if you’ve never heard of them, that's what your favorite "NeoBank" uses. And due to their legal jurisdictions, they will report your finances to authorities since they're CRS and FACTA compliant. We are not. As an OmniBank, we work with different banking partners, and although KYC is required to use our services, our offshore banks are non-CRS and non-FACTA. Tax reporting is the responsibility and choice of the user. Offshore Accounts vs Physical Cards | I've tried to highlight this a few times so far. vPay has 3 offerings on the banking side of things. Virtual cards - live now. Physical cards - coming Q1 2026. The first two are similar to what everyone else on the market offers. The offshore accounts are not. which are coming this week. They allow unlimited spending, ATM withdrawals, and international SWIFT transfers, which very few “Neobanks” provide. Offshore accounts are coming this week. Self-Custody | We're not 100% non-custodial yet, as that is near impossible at the moment but it's something we're working towards. And we try to keep the users' self-custodial wallets in the loop as much as possible for maximum control. Those who have tried the vPay app know that almost every move asks for permission from their wallet, and we always encourage users to keep their funds in their non-custodial wallets until the very last moment, since our top-ups usually only take seconds to a minute to process. Fees | All of the card providers mentioned above either raised millions from VCs or in presales or have a huge org backing them. We have neither. vPay was self-funded and community-owned since day 1, launched under Virtuals Protocol Genesis V1 launch model, an objectively bad launch model and hugely unfavorable toward project teams. So even though vPay has been generating revenue and profitable from early on, we do not have the luxury of offering 0% fees yet, since they're mostly a marketing gimmick paid for by millions in VC money and not a sustainable business model for early-stage companies. What we're working towards instead, is true co-ownership of vPay and revenue-share with users. OmniBank vs NeoBank | I’m not a fan of the term “NeoBank.” It implies just a bank, but make it crypto. That’s not vPay. Our goals have always been clear: A) Anything and everything users need to do with their money and assets, both Web2 and Web3, all in one hub. Powered by a constellation of partner agents. The cards and the bank accounts are just the foundation. B) To eventually build independent financial rails for crypto and decouple from the chokehold of Visa/Mastercard. vLink is the first step toward this vision. This turned out to be a rather long tweet, but context matters. Questions and feedback welcome in replies or DMs. See you all with your vPay vCards very soon.

The Dude

20,558 Aufrufe • vor 8 Monaten

Most recent diffusion language model research (that I’ve seen) seems to be using masking as the noising process. It looks like, however, most closed-source models (Google Gemini Diffusion and possibly Inception Labs’ Mercury) use a different noising process, where instead of masking tokens, they replace them with different tokens (either with a random token or a semantically similar token). I wondered how they were getting such high throughput with the latter noising process, since I believed that optimizing inference with KVCache approximation would be more difficult (for various reasons). I visualized this noising process with tiny-diffusion and compared it to normal unmasking, and was very surprised to see how fast the generation “settles” into a reasonable output, and then only slightly refines afterwards, requiring much fewer steps in total. Unmasking (where tokens are never remasked, the typical implementation) is inherently limited in generation speed by the fact that an increase in tokens decoded per step leads to more errors due to the mismatch between individual and marginal token probability distributions we sample from. The token replacement noising process seems to have a much different set of characteristics. Because we sample each token per step, every token makes “progress” towards the final output each iteration (in addition to *potentially* giving other tokens more information in future steps). Generally, masking has outperformed other noising processes, which is probably why most research focused on it (using smaller models). But the paper referred to in the retweet shows that random replacement as a noising process may scale better as model size increases. Big labs might have noticed these results much earlier (due to having drastically more training resources and being able to test larger models), which may explain the discrepancy in the choice of noising process. I’m gonna test this with larger models, since tiny-diffusion only has 10M parameters.
0:13

Most recent diffusion language model research (that I’ve seen) seems to be using masking as the noising process. It looks like, however, most closed-source models (Google Gemini Diffusion and possibly Inception Labs’ Mercury) use a different noising process, where instead of masking tokens, they replace them with different tokens (either with a random token or a semantically similar token). I wondered how they were getting such high throughput with the latter noising process, since I believed that optimizing inference with KVCache approximation would be more difficult (for various reasons). I visualized this noising process with tiny-diffusion and compared it to normal unmasking, and was very surprised to see how fast the generation “settles” into a reasonable output, and then only slightly refines afterwards, requiring much fewer steps in total. Unmasking (where tokens are never remasked, the typical implementation) is inherently limited in generation speed by the fact that an increase in tokens decoded per step leads to more errors due to the mismatch between individual and marginal token probability distributions we sample from. The token replacement noising process seems to have a much different set of characteristics. Because we sample each token per step, every token makes “progress” towards the final output each iteration (in addition to *potentially* giving other tokens more information in future steps). Generally, masking has outperformed other noising processes, which is probably why most research focused on it (using smaller models). But the paper referred to in the retweet shows that random replacement as a noising process may scale better as model size increases. Big labs might have noticed these results much earlier (due to having drastically more training resources and being able to test larger models), which may explain the discrepancy in the choice of noising process. I’m gonna test this with larger models, since tiny-diffusion only has 10M parameters.

nathan (in sf)

40,440 Aufrufe • vor 7 Monaten

Seedance V2 This used to take people month of study and practice on Adobe Flash, now it can be done with Seedance V2, not perfect but this is the worst it will be. 15 seconds, stylized 2D hand-drawn animation, overhead battlefield on aged yellow lined notebook paper, clear blue horizontal ruled lines and a red left margin line always visible, fine paper grain, pencil marks, ink strokes, minimal classroom-material aesthetic at the start. The entire video must preserve the same paper world from start to finish. No live action, no 3D rendering, no realistic human faces, no modern objects, no narration, no subtitles. Core concept: A childish classroom doodle of an ancient war gradually transforms into a legendary illustrated battlefield, then collapses back into scribbles after the climax. The escalation must feel smooth, intentional, and visually magical, as if imagination is taking over the page. Army design: Two opposing ancient armies drawn first as simple colored stick figures, one faction in red, one faction in blue. Dense infantry blocks with spears and swords, cavalry units with long lances, banner carriers, archers. At first they are crude doodles with simple line limbs and circular heads. As the battle intensifies, they evolve step by step into more detailed inked warriors with clearer armor silhouettes, horses, weapons, helmets, capes, and expressive movement, but still remain inside a hand-drawn 2D illustrated style on paper. Visual progression and timing: 0-3 seconds: Wide top-down view of a large notebook-paper battlefield. Rough stick-figure armies face each other across the page. The drawing feels playful and simple at first. The camera slowly glides forward over the paper as both sides begin charging. Tiny horses gallop, infantry rushes, arrows are sketched into existence and start falling. Everything still looks like rough schoolbook doodles. 3-7 seconds: The first major collision. Spears thrust, swords swing, cavalry crashes into cavalry, formations break apart. With each impact, the art style upgrades. Simple stick limbs become stronger ink lines, bodies gain armor shapes, horses gain muscular form, banners gain flowing detail, shadows and dust marks appear. The battlefield becomes denser, faster, more dramatic. Red and blue strokes smear across the page with the force of combat. 7-11 seconds: The battle reaches full transformation. The once-crude doodles are now a glorious hand-illustrated ancient war scene, still clearly drawn on notebook paper but far more detailed and cinematic. The camera pushes into a central duel between two opposing generals on horseback. Their weapons clash with a powerful burst of ink lines and paper tremor. Around them, infantry and cavalry continue fighting in layered motion, arrows rain down, fallen soldiers scatter across the ruled lines. 11-15 seconds: At the peak of the duel, one final strike lands. A shockwave ripples through the page. The detailed warriors, horses, banners, and battle effects suddenly break apart into loose pencil scribbles, sketch fragments, and drifting paper-line debris. The great war rapidly collapses back into childish rough doodles, then into scattered marks and unfinished lines, as if the imagination has burned out. End on the overhead notebook page with the battlefield reduced to messy hand-drawn remnants. Animation and motion: Smooth fluid motion, strong timing, readable silhouettes, at least 24fps feel. The escalation from crude doodle to epic illustrated warfare must be gradual and continuous, not abrupt. Impacts should feel sharp and rhythmic. Keep all action legible from overhead. Maintain strong contrast between the innocent notebook-paper setting and the seriousness of the war. Atmosphere: Starts playful and curious, grows intense and heroic, peaks as a mythic battlefield, then ends with a strange quiet after the collapse. The whole piece should feel like a child’s imagination turning into an epic war vision on paper.
0:15

Seedance V2 This used to take people month of study and practice on Adobe Flash, now it can be done with Seedance V2, not perfect but this is the worst it will be. 15 seconds, stylized 2D hand-drawn animation, overhead battlefield on aged yellow lined notebook paper, clear blue horizontal ruled lines and a red left margin line always visible, fine paper grain, pencil marks, ink strokes, minimal classroom-material aesthetic at the start. The entire video must preserve the same paper world from start to finish. No live action, no 3D rendering, no realistic human faces, no modern objects, no narration, no subtitles. Core concept: A childish classroom doodle of an ancient war gradually transforms into a legendary illustrated battlefield, then collapses back into scribbles after the climax. The escalation must feel smooth, intentional, and visually magical, as if imagination is taking over the page. Army design: Two opposing ancient armies drawn first as simple colored stick figures, one faction in red, one faction in blue. Dense infantry blocks with spears and swords, cavalry units with long lances, banner carriers, archers. At first they are crude doodles with simple line limbs and circular heads. As the battle intensifies, they evolve step by step into more detailed inked warriors with clearer armor silhouettes, horses, weapons, helmets, capes, and expressive movement, but still remain inside a hand-drawn 2D illustrated style on paper. Visual progression and timing: 0-3 seconds: Wide top-down view of a large notebook-paper battlefield. Rough stick-figure armies face each other across the page. The drawing feels playful and simple at first. The camera slowly glides forward over the paper as both sides begin charging. Tiny horses gallop, infantry rushes, arrows are sketched into existence and start falling. Everything still looks like rough schoolbook doodles. 3-7 seconds: The first major collision. Spears thrust, swords swing, cavalry crashes into cavalry, formations break apart. With each impact, the art style upgrades. Simple stick limbs become stronger ink lines, bodies gain armor shapes, horses gain muscular form, banners gain flowing detail, shadows and dust marks appear. The battlefield becomes denser, faster, more dramatic. Red and blue strokes smear across the page with the force of combat. 7-11 seconds: The battle reaches full transformation. The once-crude doodles are now a glorious hand-illustrated ancient war scene, still clearly drawn on notebook paper but far more detailed and cinematic. The camera pushes into a central duel between two opposing generals on horseback. Their weapons clash with a powerful burst of ink lines and paper tremor. Around them, infantry and cavalry continue fighting in layered motion, arrows rain down, fallen soldiers scatter across the ruled lines. 11-15 seconds: At the peak of the duel, one final strike lands. A shockwave ripples through the page. The detailed warriors, horses, banners, and battle effects suddenly break apart into loose pencil scribbles, sketch fragments, and drifting paper-line debris. The great war rapidly collapses back into childish rough doodles, then into scattered marks and unfinished lines, as if the imagination has burned out. End on the overhead notebook page with the battlefield reduced to messy hand-drawn remnants. Animation and motion: Smooth fluid motion, strong timing, readable silhouettes, at least 24fps feel. The escalation from crude doodle to epic illustrated warfare must be gradual and continuous, not abrupt. Impacts should feel sharp and rhythmic. Keep all action legible from overhead. Maintain strong contrast between the innocent notebook-paper setting and the seriousness of the war. Atmosphere: Starts playful and curious, grows intense and heroic, peaks as a mythic battlefield, then ends with a strange quiet after the collapse. The whole piece should feel like a child’s imagination turning into an epic war vision on paper.

Emily

35,023 Aufrufe • vor 4 Monaten

Vulnerable Oil Pipelines and the UAE’s Existential Delusion If the problem is the closure of the Strait of Hormuz in the event of a conflict, I would say that these ideas only last until the conflict actually begins. Once it starts, both the terminals and the oil pipelines will be bombed. The Gulf countries have not yet understood that they need to reach some kind of arrangement among themselves. And the United States should encourage this, focusing on regional stability. It has become clear that U.S military control of the region no longer exists. The bases only serve to waste money and expose the Arab countries to confrontation with Iran. The Emirates are living in an existential delusion, believing they can return to previous levels of prosperity while maintaining a confrontational stance toward Iran. The UAE is face-to-face with Iran. After what happened in this war, who will invest in Dubai without the certainty that the country has reached an understanding with Iran? The illusion of American protection no longer exists. The same applies to all Gulf countries. Both the appeasement of Iranian-backed militias and the reduction of the American military presence around Iran are political decisions that need to mature through greater dialogue, something that will not be achieved with the confrontational tone the Emirates have maintained. On the contrary, the Emirates are deluded and will see their economy face serious difficulties if they continue down this path. Carrying out persuasion through military encirclement with bases against a missile power is no longer viable today. Decades ago, those bases might have received the occasional imprecise Scud. Today, they face showers of missiles and drones. These bases are no longer practical in the current era, and the same applies to bases in Asia surrounding China, or NATO bases surrounding Russia. The new reality is simple: only underground bases supported by a vast ecosystem will survive future conflicts. The Cold War strategy of containment has proven to be a failure in modern wars. It merely exposes troops to grave danger without delivering real security. The entire model must be completely rethought. The war with Iran has demonstrated that military bases now require a minimum safe distance from adversary missile and drone threats, and even then, they must be built underground. Surface bases have become liabilities rather than assets.
0:15

Vulnerable Oil Pipelines and the UAE’s Existential Delusion If the problem is the closure of the Strait of Hormuz in the event of a conflict, I would say that these ideas only last until the conflict actually begins. Once it starts, both the terminals and the oil pipelines will be bombed. The Gulf countries have not yet understood that they need to reach some kind of arrangement among themselves. And the United States should encourage this, focusing on regional stability. It has become clear that U.S military control of the region no longer exists. The bases only serve to waste money and expose the Arab countries to confrontation with Iran. The Emirates are living in an existential delusion, believing they can return to previous levels of prosperity while maintaining a confrontational stance toward Iran. The UAE is face-to-face with Iran. After what happened in this war, who will invest in Dubai without the certainty that the country has reached an understanding with Iran? The illusion of American protection no longer exists. The same applies to all Gulf countries. Both the appeasement of Iranian-backed militias and the reduction of the American military presence around Iran are political decisions that need to mature through greater dialogue, something that will not be achieved with the confrontational tone the Emirates have maintained. On the contrary, the Emirates are deluded and will see their economy face serious difficulties if they continue down this path. Carrying out persuasion through military encirclement with bases against a missile power is no longer viable today. Decades ago, those bases might have received the occasional imprecise Scud. Today, they face showers of missiles and drones. These bases are no longer practical in the current era, and the same applies to bases in Asia surrounding China, or NATO bases surrounding Russia. The new reality is simple: only underground bases supported by a vast ecosystem will survive future conflicts. The Cold War strategy of containment has proven to be a failure in modern wars. It merely exposes troops to grave danger without delivering real security. The entire model must be completely rethought. The war with Iran has demonstrated that military bases now require a minimum safe distance from adversary missile and drone threats, and even then, they must be built underground. Surface bases have become liabilities rather than assets.

Patricia Marins

218,335 Aufrufe • vor 4 Monaten

🇮🇷🇮🇱| A little more to understand... My opinion: The war may end in a few days or last a lot longer—all depending on whether or not the US joins Israel. Israel is not able to fight against Iran for much longer; its interceptors have a certain capability threshold, and reports already suggest it is rationing its interceptor missiles, and we've seen how 12-17 interceptors were launched, yet Iranian missiles still made an impact. It all comes down to whether the US is willing to go to war with Iran or not; it's likely they will if you consider Trump's administrative behavior since he took office. But honestly, if they are smart, they will not. - A little about Iran's missile situation: Israel says it's conducting operations in northwestern and western Iran to prevent the reactivation of missile bases in Tabriz, Kermanshah, and Khorramabad. A significant portion of the Israeli Air Force is now focused on these 3 sites to prevent further missile launches from those locations. This suggests that the sites they claim they destroyed did not suffer strategic damage, and the destroyed hangars were of minimal value. As a result, Israel continuously carries out operations and remains engaged to prevent Iran from reactivating its capabilities in the west. If these 3 regions are abandoned, Israel will move on to target other strategic sites. These 3 missile bases are among Iran's key assets. The missiles are intact and untouched, it's the launchers that are affected, and they can easily be reactivated, but it's difficult under continuous attacks. Other missile bases in the south remain untouched and have so far not been used, as they are probably being reserved for strikes against the US. Missile launches are mainly launched from the center and north of Iran, some from the western regions as well. The reason for limited and isolated launches these past few days has several explanations, mainly that Iran is testing to recognize new patterns everytime a serious attack is carried out, as Israel continuously changes its defensive behavior to prevent Iran's intelligence from learning it; thus, isolated launches, followed by a bigger, deadlier, and more accurate attack with 30 launches as we saw today.
0:09

🇮🇷🇮🇱| A little more to understand... My opinion: The war may end in a few days or last a lot longer—all depending on whether or not the US joins Israel. Israel is not able to fight against Iran for much longer; its interceptors have a certain capability threshold, and reports already suggest it is rationing its interceptor missiles, and we've seen how 12-17 interceptors were launched, yet Iranian missiles still made an impact. It all comes down to whether the US is willing to go to war with Iran or not; it's likely they will if you consider Trump's administrative behavior since he took office. But honestly, if they are smart, they will not. - A little about Iran's missile situation: Israel says it's conducting operations in northwestern and western Iran to prevent the reactivation of missile bases in Tabriz, Kermanshah, and Khorramabad. A significant portion of the Israeli Air Force is now focused on these 3 sites to prevent further missile launches from those locations. This suggests that the sites they claim they destroyed did not suffer strategic damage, and the destroyed hangars were of minimal value. As a result, Israel continuously carries out operations and remains engaged to prevent Iran from reactivating its capabilities in the west. If these 3 regions are abandoned, Israel will move on to target other strategic sites. These 3 missile bases are among Iran's key assets. The missiles are intact and untouched, it's the launchers that are affected, and they can easily be reactivated, but it's difficult under continuous attacks. Other missile bases in the south remain untouched and have so far not been used, as they are probably being reserved for strikes against the US. Missile launches are mainly launched from the center and north of Iran, some from the western regions as well. The reason for limited and isolated launches these past few days has several explanations, mainly that Iran is testing to recognize new patterns everytime a serious attack is carried out, as Israel continuously changes its defensive behavior to prevent Iran's intelligence from learning it; thus, isolated launches, followed by a bigger, deadlier, and more accurate attack with 30 launches as we saw today.

Arya - آریا

55,523 Aufrufe • vor 1 Jahr

🚨 SPACEX IS ABOUT TO TEST A RADICALLY DIFFERENT KIND OF SPACECRAFT AND IT COULD UPEND THE ENTIRE ORBITAL MANUFACTURING INDUSTRY. On Tuesday, SpaceX plans to fly the first prototype of Starfall, a flat, disk-shaped reentry capsule designed to return up to 1,000 kilograms of cargo from orbit in a single flight. That’s roughly 30 times more payload capacity than current commercial return vehicles (like those from Varda Space Industries). It’s not a scaled-down Dragon it’s a completely different approach: no onboard deorbit engine, a wide flat disk geometry, and Starlink terminals mounted to maintain communication through the plasma blackout during reentry. Why this matters: • Current orbital manufacturing companies are limited to returning only dozens of kilograms per mission • Starfall’s design could make large-scale commercial production in space economically viable for the first time • SpaceX would be directly competing with companies (like Varda) that currently pay SpaceX to launch their capsules • Successfully testing Starlink through reentry plasma would be a major technical win with applications across SpaceX’s vehicles The deeper implication: SpaceX is quietly expanding its vertical integration. They already dominate launch. Now they’re moving into the return leg of the orbital manufacturing supply chain the part that has been the biggest bottleneck for companies trying to make products in microgravity and bring them back to Earth. If Starfall works at scale, it doesn’t just give SpaceX another revenue stream. It gives them significant control over the economics of an entire emerging industry. The disk shape and high-capacity design suggest they’re thinking about high-cadence, lower-cost returns rather than the traditional high-value, low-volume approach. This is classic SpaceX: take an existing problem (expensive, low-capacity return from orbit), apply first-principles thinking to the vehicle design, and try to make it dramatically cheaper and higher volume. How do you think this move into orbital return changes the competitive landscape for companies trying to build businesses in space manufacturing? Follow for more analysis on SpaceX’s expanding role across the space economy.
0:10

🚨 SPACEX IS ABOUT TO TEST A RADICALLY DIFFERENT KIND OF SPACECRAFT AND IT COULD UPEND THE ENTIRE ORBITAL MANUFACTURING INDUSTRY. On Tuesday, SpaceX plans to fly the first prototype of Starfall, a flat, disk-shaped reentry capsule designed to return up to 1,000 kilograms of cargo from orbit in a single flight. That’s roughly 30 times more payload capacity than current commercial return vehicles (like those from Varda Space Industries). It’s not a scaled-down Dragon it’s a completely different approach: no onboard deorbit engine, a wide flat disk geometry, and Starlink terminals mounted to maintain communication through the plasma blackout during reentry. Why this matters: • Current orbital manufacturing companies are limited to returning only dozens of kilograms per mission • Starfall’s design could make large-scale commercial production in space economically viable for the first time • SpaceX would be directly competing with companies (like Varda) that currently pay SpaceX to launch their capsules • Successfully testing Starlink through reentry plasma would be a major technical win with applications across SpaceX’s vehicles The deeper implication: SpaceX is quietly expanding its vertical integration. They already dominate launch. Now they’re moving into the return leg of the orbital manufacturing supply chain the part that has been the biggest bottleneck for companies trying to make products in microgravity and bring them back to Earth. If Starfall works at scale, it doesn’t just give SpaceX another revenue stream. It gives them significant control over the economics of an entire emerging industry. The disk shape and high-capacity design suggest they’re thinking about high-cadence, lower-cost returns rather than the traditional high-value, low-volume approach. This is classic SpaceX: take an existing problem (expensive, low-capacity return from orbit), apply first-principles thinking to the vehicle design, and try to make it dramatically cheaper and higher volume. How do you think this move into orbital return changes the competitive landscape for companies trying to build businesses in space manufacturing? Follow for more analysis on SpaceX’s expanding role across the space economy.

TheNewPhysics

445,776 Aufrufe • vor 1 Monat