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Building on the previous paper, in this study we compare a continuous “smooth return” S2>S1 model with an event-driven one, where long periods of relative calm are punctuated by short, intense episodes of global reorganisation. Both models cover the same time window. Neither uses archaeological data in its construction....

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Craig Stone

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10,899 görüntüleme • 4 ay önce •via X (Twitter)

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According to Plato, Atlantis was the main city of an advanced civilization that was wiped out 9,000 years ago, which places the event right at the Younger Dryas abrupt climate change and the Gothenburg geomagnetic excursion. Some sources about Plato suggest that there were at least a few floods between the event that sank Atlantis and the time when he lived. This also aligns with the 8.2-kiloyear event, the 5.9-kiloyear event, and the collapse of the Bronze Age civilizations — all associated with abrupt climate change and geomagnetic excursions. Right now we are facing the same kind of event, and that’s why the billionaires are building doomsday bunkers.
0:12

According to Plato, Atlantis was the main city of an advanced civilization that was wiped out 9,000 years ago, which places the event right at the Younger Dryas abrupt climate change and the Gothenburg geomagnetic excursion. Some sources about Plato suggest that there were at least a few floods between the event that sank Atlantis and the time when he lived. This also aligns with the 8.2-kiloyear event, the 5.9-kiloyear event, and the collapse of the Bronze Age civilizations — all associated with abrupt climate change and geomagnetic excursions. Right now we are facing the same kind of event, and that’s why the billionaires are building doomsday bunkers.

Open Minded Approach

460,055 görüntüleme • 7 ay önce

According to Plato, Atlantis was the main city of an advanced civilization that was wiped out 9,000 years ago, which places the event right at the Younger Dryas abrupt climate change and the Gothenburg geomagnetic excursion. Some sources about Plato suggest that there were at least a few floods between the event that sank Atlantis and the time when he lived. This also aligns with the 8.2-kiloyear event, the 5.9-kiloyear event, and the collapse of the Bronze Age civilizations — all associated with abrupt climate change and geomagnetic excursions. Right now we are facing the same kind of event, and that’s why the billionaires are building doomsday bunkers.
0:12

According to Plato, Atlantis was the main city of an advanced civilization that was wiped out 9,000 years ago, which places the event right at the Younger Dryas abrupt climate change and the Gothenburg geomagnetic excursion. Some sources about Plato suggest that there were at least a few floods between the event that sank Atlantis and the time when he lived. This also aligns with the 8.2-kiloyear event, the 5.9-kiloyear event, and the collapse of the Bronze Age civilizations — all associated with abrupt climate change and geomagnetic excursions. Right now we are facing the same kind of event, and that’s why the billionaires are building doomsday bunkers.

Open Minded Approach

66,581 görüntüleme • 4 ay önce

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

52,279 görüntüleme • 2 yıl önce

1/ Gemini 2.5 is here, and it’s our most intelligent AI model ever. Our first 2.5 model, Gemini 2.5 Pro Experimental is a state-of-the-art thinking model, leading in a wide range of benchmarks – with impressive improvements in enhanced reasoning and coding and now #1 on Arena by a significant margin. With a model this intelligent, we wanted to get it to people as quickly as possible. Find it on Google AI Studio and in the Google Gemini for Gemini Advanced users now – and in Vertex in the coming weeks. This is the start of a new era of thinking models – and we can’t wait to see where things go from here.
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1/ Gemini 2.5 is here, and it’s our most intelligent AI model ever. Our first 2.5 model, Gemini 2.5 Pro Experimental is a state-of-the-art thinking model, leading in a wide range of benchmarks – with impressive improvements in enhanced reasoning and coding and now #1 on Arena by a significant margin. With a model this intelligent, we wanted to get it to people as quickly as possible. Find it on Google AI Studio and in the Google Gemini for Gemini Advanced users now – and in Vertex in the coming weeks. This is the start of a new era of thinking models – and we can’t wait to see where things go from here.

Sundar Pichai

864,049 görüntüleme • 1 yıl önce

We're now in the range where the GRAF 🦒model is picking up the early stages of the weekend storm. It's had wins and losses at this range, but did well with last weekend's event across the Carolinas. #SCwx #NCwx WMBF News
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We're now in the range where the GRAF 🦒model is picking up the early stages of the weekend storm. It's had wins and losses at this range, but did well with last weekend's event across the Carolinas. #SCwx #NCwx WMBF News

Jamie Arnold WMBF

98,576 görüntüleme • 4 ay önce

Those who say that the Younger Dryas was caused by a meteor hitting the Earth or grazing the atmosphere have not done their homework. The Cyclical Geophysical Event is caused by a combination of axial and apsidal precession, which leads to geomagnetic excursions and, together with Grand Solar Minima, causes abrupt climate change, massive floods, volcanic activity, and the reshaping of the continents. The Younger Dryas is not an exception. Similar events occurred during the 8.2-kiloyear event, the 5.9-kiloyear event, the 4.2-kiloyear event, the Bronze Age Collapse, and are happening today.
0:10

Those who say that the Younger Dryas was caused by a meteor hitting the Earth or grazing the atmosphere have not done their homework. The Cyclical Geophysical Event is caused by a combination of axial and apsidal precession, which leads to geomagnetic excursions and, together with Grand Solar Minima, causes abrupt climate change, massive floods, volcanic activity, and the reshaping of the continents. The Younger Dryas is not an exception. Similar events occurred during the 8.2-kiloyear event, the 5.9-kiloyear event, the 4.2-kiloyear event, the Bronze Age Collapse, and are happening today.

Open Minded Approach

14,728 görüntüleme • 5 gün önce

The Great Flood during the Xia Dynasty describes a catastrophic, decades-long deluge and giant waves that covered mountains and wiped out entire regions. The official narrative places the Xia Dynasty around 2200–2000 BC, right at the time of the 4.2-kiloyear event and the collapse of the ancient Egyptian, Akkadian, and Indus Valley civilizations. During the 4.2-kiloyear event, there was abrupt climate change, a Grand Solar Minimum, and a collapse of major ocean currents. It is also possible that this flood is connected to the 5.9-kiloyear event, the Solovki Geomagnetic Excursion, with supporting evidence found in Chinese sediments, and the desertification of the Sahara. The same event is coming soon!
0:12

The Great Flood during the Xia Dynasty describes a catastrophic, decades-long deluge and giant waves that covered mountains and wiped out entire regions. The official narrative places the Xia Dynasty around 2200–2000 BC, right at the time of the 4.2-kiloyear event and the collapse of the ancient Egyptian, Akkadian, and Indus Valley civilizations. During the 4.2-kiloyear event, there was abrupt climate change, a Grand Solar Minimum, and a collapse of major ocean currents. It is also possible that this flood is connected to the 5.9-kiloyear event, the Solovki Geomagnetic Excursion, with supporting evidence found in Chinese sediments, and the desertification of the Sahara. The same event is coming soon!

Open Minded Approach

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They lie to you by claiming that humans cause climate change as rapidly as it is happening today, and that naturally driven climate change is slow and lasts for thousands of years. Every Bond Event was quick; the 5.9-kiloyear event and the desertification of the Sahara occurred within a 100-year period. There are tipping points where all hell breaks loose, and we are heading toward one. We are in a geomagnetic excursion.
0:11

They lie to you by claiming that humans cause climate change as rapidly as it is happening today, and that naturally driven climate change is slow and lasts for thousands of years. Every Bond Event was quick; the 5.9-kiloyear event and the desertification of the Sahara occurred within a 100-year period. There are tipping points where all hell breaks loose, and we are heading toward one. We are in a geomagnetic excursion.

Open Minded Approach

128,904 görüntüleme • 3 ay önce

HEEBOO redefines how entertainment is created, owned, and experienced. The industry is broken. By validating ideas with Superfans, we’re building a market-driven franchise model where IPs start digital-first, monetize early, scale globally, and are co-owned by their communities. Built by the Claynosaurz team.
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HEEBOO redefines how entertainment is created, owned, and experienced. The industry is broken. By validating ideas with Superfans, we’re building a market-driven franchise model where IPs start digital-first, monetize early, scale globally, and are co-owned by their communities. Built by the Claynosaurz team.

HEEBOO

351,325 görüntüleme • 7 ay önce

World Models are the path for some AI Models in the future. But how can we efficiently train these models to not only see the world the way humans do but to see the world in a new and unique way. By visualizing, what is normally sequenced audio patterns, we can derive much more insights. Here we see Paganini in a visual form that can than be described and transcribed into a World Model. We can observe connections in a manner that may not have been clear prior to the digitalization of music and sound in this way. The company with the most valuable potential in building a World Model is Tesla. Not that this type of visualization is being used, but that the mechanisms are in place, and the technology is in place for the company to thrive in this new form of AI.
0:19

World Models are the path for some AI Models in the future. But how can we efficiently train these models to not only see the world the way humans do but to see the world in a new and unique way. By visualizing, what is normally sequenced audio patterns, we can derive much more insights. Here we see Paganini in a visual form that can than be described and transcribed into a World Model. We can observe connections in a manner that may not have been clear prior to the digitalization of music and sound in this way. The company with the most valuable potential in building a World Model is Tesla. Not that this type of visualization is being used, but that the mechanisms are in place, and the technology is in place for the company to thrive in this new form of AI.

Brian Roemmele

57,424 görüntüleme • 7 ay önce

This also shows up in the representations learned by the model. We plot the model’s representations of human and robot images. As pre-training is scaled up, the representation of humans and robots become more aligned: to a scaled-up model, human videos "look" like robot demos.
0:10

This also shows up in the representations learned by the model. We plot the model’s representations of human and robot images. As pre-training is scaled up, the representation of humans and robots become more aligned: to a scaled-up model, human videos "look" like robot demos.

Physical Intelligence

120,595 görüntüleme • 6 ay önce

You don’t have to use one model (or one provider!) for everything. With Workshop, you can combine frontier and local models in the same workflow. For example: Opus can be the main agent, and delegate specific tasks to Gemma 4 via subagents. Better quality where it matters. Better privacy, speed, and cost where it counts. One workflow, best model for each task.
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You don’t have to use one model (or one provider!) for everything. With Workshop, you can combine frontier and local models in the same workflow. For example: Opus can be the main agent, and delegate specific tasks to Gemma 4 via subagents. Better quality where it matters. Better privacy, speed, and cost where it counts. One workflow, best model for each task.

Workshop AI

799,762 görüntüleme • 1 ay önce

In flow matching, a coupling determines how noise and data samples are paired during training. The choice of coupling is important because it influences the geometry of trajectories at inference time. The simplest choice is the independent coupling, where noise and data points are paired arbitrarily. This can lead to curved trajectories as the model averages over many conflicting pairings. However, if we use optimal transport on batches of pairs, this leads to fewer ambiguous intersections that the model must resolve, leading to straighter trajectories at inference time.
0:18

In flow matching, a coupling determines how noise and data samples are paired during training. The choice of coupling is important because it influences the geometry of trajectories at inference time. The simplest choice is the independent coupling, where noise and data points are paired arbitrarily. This can lead to curved trajectories as the model averages over many conflicting pairings. However, if we use optimal transport on batches of pairs, this leads to fewer ambiguous intersections that the model must resolve, leading to straighter trajectories at inference time.

Alec Helbling

64,978 görüntüleme • 1 ay önce

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 görüntüleme • 1 yıl önce

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

593,909 görüntüleme • 2 yıl önce

Soon we will launch our new model at MagicPath. It will be the best model in the world for interface design. Design is, and always will be, the moat. Here are some one-shot results from an early checkpoint.
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Soon we will launch our new model at MagicPath. It will be the best model in the world for interface design. Design is, and always will be, the moat. Here are some one-shot results from an early checkpoint.

Pietro Schirano

35,714 görüntüleme • 7 ay önce

New Generation Model! 🚨 We're introducing the o3m model to our expanding lineup of generation models. While still in its beta phase, we're actively refining its output to push its capabilities even further. So far, we've observed a significant leap in physics calculations, AI-driven mechanics, and the overall fluidity of app and game interactions. As we continue to tweak and optimize, expect even greater advancements in precision, automation, and creative execution.
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New Generation Model! 🚨 We're introducing the o3m model to our expanding lineup of generation models. While still in its beta phase, we're actively refining its output to push its capabilities even further. So far, we've observed a significant leap in physics calculations, AI-driven mechanics, and the overall fluidity of app and game interactions. As we continue to tweak and optimize, expect even greater advancements in precision, automation, and creative execution.

ALCHEMIST AI 🔮

17,306 görüntüleme • 1 yıl önce

🚨NEWS: $TSLA DEVELOPING NEW SMALLER, CHEAPER EV SUV • Tesla is developing an all-new compact electric SUV, according to four people familiar with the matter. • The vehicle is not a variant of the Model 3 or Model Y. ✅ Key Details • Length: approximately 4.28 meters (14 feet) — significantly shorter than the Model Y (about 15.7 feet). • Planned production locations: primarily China (Shanghai factory), with potential expansion to the United States and Europe. • Expected pricing: substantially lower than the entry-level Model 3 (which starts at ~$37,000 in the US and $34,000 in China). ✅ Design Approach • Smaller battery for cost reduction (resulting in shorter range than the Model Y’s 306–327 miles). • Single electric motor instead of dual motors. • Significantly lighter weight target (~1.5 metric tons vs. ~2 tons for Model Y). ✅ Strategic Context • This project comes after Elon Musk scrapped a previous low-cost EV plan in 2024 to focus on robotaxis and Optimus. • The new SUV could be offered in both fully autonomous and human-driven versions to suit different global markets and regulations. • The development is still in early stages, with production unlikely to start in 2026.
0:11

🚨NEWS: $TSLA DEVELOPING NEW SMALLER, CHEAPER EV SUV • Tesla is developing an all-new compact electric SUV, according to four people familiar with the matter. • The vehicle is not a variant of the Model 3 or Model Y. ✅ Key Details • Length: approximately 4.28 meters (14 feet) — significantly shorter than the Model Y (about 15.7 feet). • Planned production locations: primarily China (Shanghai factory), with potential expansion to the United States and Europe. • Expected pricing: substantially lower than the entry-level Model 3 (which starts at ~$37,000 in the US and $34,000 in China). ✅ Design Approach • Smaller battery for cost reduction (resulting in shorter range than the Model Y’s 306–327 miles). • Single electric motor instead of dual motors. • Significantly lighter weight target (~1.5 metric tons vs. ~2 tons for Model Y). ✅ Strategic Context • This project comes after Elon Musk scrapped a previous low-cost EV plan in 2024 to focus on robotaxis and Optimus. • The new SUV could be offered in both fully autonomous and human-driven versions to suit different global markets and regulations. • The development is still in early stages, with production unlikely to start in 2026.

Tsla Archive

56,232 görüntüleme • 2 ay önce

Agentz is a revolutionary platform that merges personalized AI agents with a user-driven marketplace, creating a dynamic ecosystem for AI development. Users benefit from customized AI agents that enhance productivity across various tasks while simultaneously contributing to the evolution of AI. This collaborative model creates a continuous improvement cycle, where user input refines AI performance, and enhanced AI capabilities draw in more users. By bridging the gap between AI consumers and contributors, Agentz is pioneering a new paradigm in artificial intelligence. The platform empowers every user to benefit from and actively participate in shaping AI technology, driving a future where AI is both user-driven and continuously evolving. Agentz is incubated by the DePIN AI Intel Layer - Rivalz Network, to guarantee state of the art data management and privacy.
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Agentz is a revolutionary platform that merges personalized AI agents with a user-driven marketplace, creating a dynamic ecosystem for AI development. Users benefit from customized AI agents that enhance productivity across various tasks while simultaneously contributing to the evolution of AI. This collaborative model creates a continuous improvement cycle, where user input refines AI performance, and enhanced AI capabilities draw in more users. By bridging the gap between AI consumers and contributors, Agentz is pioneering a new paradigm in artificial intelligence. The platform empowers every user to benefit from and actively participate in shaping AI technology, driving a future where AI is both user-driven and continuously evolving. Agentz is incubated by the DePIN AI Intel Layer - Rivalz Network, to guarantee state of the art data management and privacy.

Church of The Overseer

68,312 görüntüleme • 1 yıl önce

How CO2 Pollutes Our Atmosphere The video maps the movements of carbon dioxide in the Earth's atmosphere using data collected from ground stations and satellites. This visualization shows how CO2 emissions, mainly from cities and industrial sites, as well as natural sources such as wildfires, move around the planet between January and March 2020. With a resolution 100 times greater than standard weather maps, this model clearly shows the impact of human activity on the global climate. The animation particularly highlights the impact of emissions on global climate change, where large cities and industrial areas have a significant impact on the environment.
0:18

How CO2 Pollutes Our Atmosphere The video maps the movements of carbon dioxide in the Earth's atmosphere using data collected from ground stations and satellites. This visualization shows how CO2 emissions, mainly from cities and industrial sites, as well as natural sources such as wildfires, move around the planet between January and March 2020. With a resolution 100 times greater than standard weather maps, this model clearly shows the impact of human activity on the global climate. The animation particularly highlights the impact of emissions on global climate change, where large cities and industrial areas have a significant impact on the environment.

Black Hole

14,159 görüntüleme • 1 yıl önce