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Introducing SoftMatcha 2: A Fast and Soft Pattern Matcher for Trillion-Scale Pre-Training Corpora What lies within a trillion-scale pre-training corpus? Can you truly guarantee your benchmarks are uncontaminated simply because there are no exact string matches? Alongside several research institutions in Japan, Sakana AI is proud to have collaborated...

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101,614 görüntüleme • 5 ay önce •via X (Twitter)

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The infrastructure for working with video data at scale barely exists. Grass Video Search is our first step in changing that. You can now find anything in a video based on the contents of its frames, not just transcripts or tags. Over the last several months, the Grass Foundation has quietly assembled one of the largest video datasets in history: over a billion videos from across the public web, and is segmenting them by scene with VLM annotations of keyframes. This dataset is now searchable, so you can find the exact moment a visual, pattern, or concept appears. Curation has always been the bottleneck in video analysis. We built a system that automates it. This is just the beginning. Over 4 billion additional videos are queued for processing, and the index is growing daily. We’re opening access to a limited set of collaborators. If you’re a researcher, journalist, or nonprofit working with video, archives, or large-scale analysis, we’d love to hear from you.
0:45

The infrastructure for working with video data at scale barely exists. Grass Video Search is our first step in changing that. You can now find anything in a video based on the contents of its frames, not just transcripts or tags. Over the last several months, the Grass Foundation has quietly assembled one of the largest video datasets in history: over a billion videos from across the public web, and is segmenting them by scene with VLM annotations of keyframes. This dataset is now searchable, so you can find the exact moment a visual, pattern, or concept appears. Curation has always been the bottleneck in video analysis. We built a system that automates it. This is just the beginning. Over 4 billion additional videos are queued for processing, and the index is growing daily. We’re opening access to a limited set of collaborators. If you’re a researcher, journalist, or nonprofit working with video, archives, or large-scale analysis, we’d love to hear from you.

Grass

471,103 görüntüleme • 1 yıl önce

I cant believe this guy just made a permanent solution to context bloat and open sourced it all! when we tested this tool (Context+) for solving an issue on the OpenCode repository, the agent using this tool used ~6.5k fewer tokens, found the code and fixed it in half the time! the results were surprising: 6 to 10k tokens saved per prompt, completed task in ~2 minutes while the agent running without the tool took ~4 mins for the same and got stuck in loops bro built an entire beast by using all the modern tools that we could think of: undo trees, semantic search by meaning (by haskellforall), advanced refactoring, blast radius, advanced file context trees, restore points... i can keep going on semantic code search and context trees are the future of agentic coding and this tool proves it the feature i loved the most is semantic search and how it gets things done 2x faster with least possible tokens it makes an agent that actually knows what it’s doing and not just guessing, it makes meaning from your code similar to RAG. if you aren't optimizing your context, you are just burning money the developer says this tool is still under development, it can have unexpected behavior and the docs need updates but the video shows the reality of how fast it can be github: get here:
0:46

I cant believe this guy just made a permanent solution to context bloat and open sourced it all! when we tested this tool (Context+) for solving an issue on the OpenCode repository, the agent using this tool used ~6.5k fewer tokens, found the code and fixed it in half the time! the results were surprising: 6 to 10k tokens saved per prompt, completed task in ~2 minutes while the agent running without the tool took ~4 mins for the same and got stuck in loops bro built an entire beast by using all the modern tools that we could think of: undo trees, semantic search by meaning (by haskellforall), advanced refactoring, blast radius, advanced file context trees, restore points... i can keep going on semantic code search and context trees are the future of agentic coding and this tool proves it the feature i loved the most is semantic search and how it gets things done 2x faster with least possible tokens it makes an agent that actually knows what it’s doing and not just guessing, it makes meaning from your code similar to RAG. if you aren't optimizing your context, you are just burning money the developer says this tool is still under development, it can have unexpected behavior and the docs need updates but the video shows the reality of how fast it can be github: get here:

forloop

226,054 görüntüleme • 4 ay önce

Today morning my supplementary question no 1 on Young India Skills University. I'm proud to say that under the able and dynamic leadership of our Hon'ble Chief Minister Revanth Reddy, he is ambitiously setting up the Young India Skills University in Hyderabad, with the objective of providing job opportunities to the students and imparting skill development training according to the requirements of the local industries that are in demand in collaboration with various companies in the PPP model. A total of 17 priority sectors have been identified, and we will be providing training to 2 thousand people in the first year of 2025-26. We will train 20,000 people every year, and the state has allocated Rs. 100 crore on its part for the same and further wants to create a corpus fund of about Rs. 300-500 crores. Now my question to the ministry is if there is any proposal from the central government to adequately fund this Skills University. This will make it a grand success and will lead to the realisation of the goal of a developed nation by 2047. #YoungIndiaSkillUniversity #Telangana #TelanganaRising
1:12

Today morning my supplementary question no 1 on Young India Skills University. I'm proud to say that under the able and dynamic leadership of our Hon'ble Chief Minister Revanth Reddy, he is ambitiously setting up the Young India Skills University in Hyderabad, with the objective of providing job opportunities to the students and imparting skill development training according to the requirements of the local industries that are in demand in collaboration with various companies in the PPP model. A total of 17 priority sectors have been identified, and we will be providing training to 2 thousand people in the first year of 2025-26. We will train 20,000 people every year, and the state has allocated Rs. 100 crore on its part for the same and further wants to create a corpus fund of about Rs. 300-500 crores. Now my question to the ministry is if there is any proposal from the central government to adequately fund this Skills University. This will make it a grand success and will lead to the realisation of the goal of a developed nation by 2047. #YoungIndiaSkillUniversity #Telangana #TelanganaRising

Kiran Kumar Chamala

71,229 görüntüleme • 1 yıl önce

VIDEO | Delhi: Professor Dr Aishwarya of Galgotias University responds over reports of the university being asked to vacate its stall at the AI Summit Expo, says, "No, we have not received any directive yet to vacate the expo area. In fact, we are ready to answer for what happened yesterday. A viral tweet circulated which went in a completely wrong direction and was misinterpreted. Everywhere, we have maintained the same claim—we never said that we manufacture or design these things. We have always said that our university is investing Rs 350 crore in AI. As part of that investment, we have brought different kinds of things into the university campus so that our students can explore, expand, and carry out research and development, and try to build an advanced prototype... actually it was misinterpreted. It is a very well-known fact, and we also know that this is something available everywhere on the internet. Why would we claim such a thing? It was misinterpreted, a viral tweet was posted and exaggerated—you know how such things happen. Therefore, we want to clarify that we are not a manufacturing unit. We are not people who make all these things. We are only here so that our university students can conduct research and development. As part of this investment, we are bringing these things in for that purpose.” #GalgotiasUniversity #IndiaAISummit2026 (Full video available on PTI Videos -
1:30

VIDEO | Delhi: Professor Dr Aishwarya of Galgotias University responds over reports of the university being asked to vacate its stall at the AI Summit Expo, says, "No, we have not received any directive yet to vacate the expo area. In fact, we are ready to answer for what happened yesterday. A viral tweet circulated which went in a completely wrong direction and was misinterpreted. Everywhere, we have maintained the same claim—we never said that we manufacture or design these things. We have always said that our university is investing Rs 350 crore in AI. As part of that investment, we have brought different kinds of things into the university campus so that our students can explore, expand, and carry out research and development, and try to build an advanced prototype... actually it was misinterpreted. It is a very well-known fact, and we also know that this is something available everywhere on the internet. Why would we claim such a thing? It was misinterpreted, a viral tweet was posted and exaggerated—you know how such things happen. Therefore, we want to clarify that we are not a manufacturing unit. We are not people who make all these things. We are only here so that our university students can conduct research and development. As part of this investment, we are bringing these things in for that purpose.” #GalgotiasUniversity #IndiaAISummit2026 (Full video available on PTI Videos -

Press Trust of India

83,420 görüntüleme • 5 ay önce

Today we announced our new Fairwater datacenter in Atlanta, connected with our first Fairwater site in Wisconsin and our broader Azure footprint to create the world’s first AI superfactory. Fairwater exemplifies our vision for a fungible fleet: infra that can serve any workload, anywhere, on fit-for-purpose accelerators and network paths, with maximum performance and efficiency. AI workloads have evolved beyond large-scale pre-training. Today, they encompass fine-tuning, reinforcement learning (RL), synthetic data generation, evaluation pipelines, and more. Fairwater is built to support this full lifecycle: Max density: Fairwater’s two-story design and liquid cooling system lets us place racks in three dimensions and pack them with GPUs as densely as possible, minimizing cable runs and improving latency and effective bandwidth. Fleet: Each Fairwater DC can integrate hundreds of thousands of the latest NVIDIA GPUs into a single coherent cluster. This provides flexible infra that can support the full spectrum of workloads, and ensure no GPU is left unnecessarily idle. And that’s on top of the more than 100,000 GB300s coming online this quarter alone for inference across the rest of our fleet. For us, it’s all about turning every gigawatt into the maximum number of useful tokens. Not every GW is created equal! Planet-scale: Every Fairwater DC will connect through our continent-spanning AI WAN to prior generations of AI supercomputers, forming a truly fungible pool of compute. This enables developers to scale beyond the capacity of a single site and dynamically land workloads on the right infra for their needs. Together, these innovations let us bring together different generations of silicon and AI systems across DCs and geos into a single elastic system that scales seamlessly across training and inference workloads And this elastic AI capacity is all available alongside all the other cloud services (compute, storage, databases, app services) that AI agents and workloads need. This is what we mean when we talk about building a fungible fleet – a single, unified platform that pushes the limits of performance per watt and per dollar. Read more:
0:44

Today we announced our new Fairwater datacenter in Atlanta, connected with our first Fairwater site in Wisconsin and our broader Azure footprint to create the world’s first AI superfactory. Fairwater exemplifies our vision for a fungible fleet: infra that can serve any workload, anywhere, on fit-for-purpose accelerators and network paths, with maximum performance and efficiency. AI workloads have evolved beyond large-scale pre-training. Today, they encompass fine-tuning, reinforcement learning (RL), synthetic data generation, evaluation pipelines, and more. Fairwater is built to support this full lifecycle: Max density: Fairwater’s two-story design and liquid cooling system lets us place racks in three dimensions and pack them with GPUs as densely as possible, minimizing cable runs and improving latency and effective bandwidth. Fleet: Each Fairwater DC can integrate hundreds of thousands of the latest NVIDIA GPUs into a single coherent cluster. This provides flexible infra that can support the full spectrum of workloads, and ensure no GPU is left unnecessarily idle. And that’s on top of the more than 100,000 GB300s coming online this quarter alone for inference across the rest of our fleet. For us, it’s all about turning every gigawatt into the maximum number of useful tokens. Not every GW is created equal! Planet-scale: Every Fairwater DC will connect through our continent-spanning AI WAN to prior generations of AI supercomputers, forming a truly fungible pool of compute. This enables developers to scale beyond the capacity of a single site and dynamically land workloads on the right infra for their needs. Together, these innovations let us bring together different generations of silicon and AI systems across DCs and geos into a single elastic system that scales seamlessly across training and inference workloads And this elastic AI capacity is all available alongside all the other cloud services (compute, storage, databases, app services) that AI agents and workloads need. This is what we mean when we talk about building a fungible fleet – a single, unified platform that pushes the limits of performance per watt and per dollar. Read more:

Satya Nadella

907,531 görüntüleme • 8 ay önce

Today we’re releasing V-JEPA, a method for teaching machines to understand and model the physical world by watching videos. This work is another important step towards Yann LeCun’s outlined vision of AI models that use a learned understanding of the world to plan, reason and accomplish complex tasks. Details ➡️ We're releasing a collection of V-JEPA vision models trained with a feature prediction objective using self-supervised learning. The models are able to understand and predict what is going on in a video, even with limited information. It learns by predicting missing or obscured parts of a video in its internal feature space. Unlike generative approaches that fill in missing pixels, this flexible approach enables up to 6x improvements in training and sample efficiency. The models were pre-trained on entirely unlabeled data, and a small amount of labeled data can be used to train a task-specific prediction head on top after pre-training. Our results show that, using a frozen backbone, our top V-JEPA models achieve 82.0% on Kinetics-400, 72.2% on Something-Something-v2 and 77.9% on ImageNet1K — competitive with or exceeding previous leading video models. We believe that this work is an important milestone on the path to advancing machine intelligence.
1:07

Today we’re releasing V-JEPA, a method for teaching machines to understand and model the physical world by watching videos. This work is another important step towards Yann LeCun’s outlined vision of AI models that use a learned understanding of the world to plan, reason and accomplish complex tasks. Details ➡️ We're releasing a collection of V-JEPA vision models trained with a feature prediction objective using self-supervised learning. The models are able to understand and predict what is going on in a video, even with limited information. It learns by predicting missing or obscured parts of a video in its internal feature space. Unlike generative approaches that fill in missing pixels, this flexible approach enables up to 6x improvements in training and sample efficiency. The models were pre-trained on entirely unlabeled data, and a small amount of labeled data can be used to train a task-specific prediction head on top after pre-training. Our results show that, using a frozen backbone, our top V-JEPA models achieve 82.0% on Kinetics-400, 72.2% on Something-Something-v2 and 77.9% on ImageNet1K — competitive with or exceeding previous leading video models. We believe that this work is an important milestone on the path to advancing machine intelligence.

AI at Meta

703,801 görüntüleme • 2 yıl önce

Can AI agents adapt zero-shot, to complex multi-step language instructions in open-ended environments? We present MaestroMotif, a method for AI-assisted skill design that produces highly capable and steerable hierarchical agents. To the best of our knowledge, it is the first method that, without expert labeled datasets, solves compositional tasks requiring hundreds of steps for completion. All the modules within MaestroMotif are learned from interaction: from the highest level of planning to the lowest-level of sensorimotor control. On the open-ended domain of NetHack, it surpasses existing approaches, including those that are fine-tuned specifically for each task. At the heart of MaestroMotif is the idea that decomposing a task into subtasks significantly helps decision making. MaestroMotif leverages an agent designer's intuition about a domain to identify important skills and describe them in natural language. These short descriptions then get converted into adaptable hierarchical agents through AI feedback and in-context learning. Our paper was recently published at ICLR 2025 and we open-source the whole project including the code, prompts and pre-trained models. Paper: Code: NotebookLM Podcast: This work was done with the amazing Mikael Henaff, Roberta Raileanu, Shagun Sodhani, Pascal Vincent, Amy Zhang, Pierre-Luc Bacon, Doina Precup, with equal supervision by Marlos C. Machado and Pierluca D'Oro. Take a look at the following thread:
1:12

Can AI agents adapt zero-shot, to complex multi-step language instructions in open-ended environments? We present MaestroMotif, a method for AI-assisted skill design that produces highly capable and steerable hierarchical agents. To the best of our knowledge, it is the first method that, without expert labeled datasets, solves compositional tasks requiring hundreds of steps for completion. All the modules within MaestroMotif are learned from interaction: from the highest level of planning to the lowest-level of sensorimotor control. On the open-ended domain of NetHack, it surpasses existing approaches, including those that are fine-tuned specifically for each task. At the heart of MaestroMotif is the idea that decomposing a task into subtasks significantly helps decision making. MaestroMotif leverages an agent designer's intuition about a domain to identify important skills and describe them in natural language. These short descriptions then get converted into adaptable hierarchical agents through AI feedback and in-context learning. Our paper was recently published at ICLR 2025 and we open-source the whole project including the code, prompts and pre-trained models. Paper: Code: NotebookLM Podcast: This work was done with the amazing Mikael Henaff, Roberta Raileanu, Shagun Sodhani, Pascal Vincent, Amy Zhang, Pierre-Luc Bacon, Doina Precup, with equal supervision by Marlos C. Machado and Pierluca D'Oro. Take a look at the following thread:

Martin Klissarov

80,217 görüntüleme • 1 yıl önce

Can an inexpensive, off-the-shelf IMU be the only sensor to estimate the full state (position, velocity, orientation) of a quadrotor flying through a track at high speed and even be on-pair with vision-based localization? The answer is yes, within certain limitations! In this #RAL2023 paper, we propose a learning-based odometry algorithm that couples a model-based filter driven by the inertial measurements with a learning-based module with access to the control commands. Our system outperforms by a large margin the state-of-the-art visual-inertial odometry (#VIO) algorithms and the state-of-the-art learned-inertial odometry algorithm, #TLIO, for the task of drone racing. Additionally, we show that our system is as accurate as a VIO algorithm that uses a camera to localize to a known map of the racing track. The main limitation of our approach is that it cannot generalize to trajectories that have not been seen at training time. However, in drone racing competitions, the track is known beforehand. Human pilots spend hours or even days of practice on the race track before the competition. Similarly, our system can be trained with the data collected during practice time and deployed during the competition. Future work will investigate how to generalize to trajectories not seen at training time. The code is released! Paper: Video: Code: Kudos to Giovanni Cioffi Leonard Bauersfeld Elia Kaufmann European Research Council (ERC) University of Zurich UZH Science UZH Space Hub NCCR Robotics Aerial Core #RAL2023 #IROS2023 #SLAM
0:31

Can an inexpensive, off-the-shelf IMU be the only sensor to estimate the full state (position, velocity, orientation) of a quadrotor flying through a track at high speed and even be on-pair with vision-based localization? The answer is yes, within certain limitations! In this #RAL2023 paper, we propose a learning-based odometry algorithm that couples a model-based filter driven by the inertial measurements with a learning-based module with access to the control commands. Our system outperforms by a large margin the state-of-the-art visual-inertial odometry (#VIO) algorithms and the state-of-the-art learned-inertial odometry algorithm, #TLIO, for the task of drone racing. Additionally, we show that our system is as accurate as a VIO algorithm that uses a camera to localize to a known map of the racing track. The main limitation of our approach is that it cannot generalize to trajectories that have not been seen at training time. However, in drone racing competitions, the track is known beforehand. Human pilots spend hours or even days of practice on the race track before the competition. Similarly, our system can be trained with the data collected during practice time and deployed during the competition. Future work will investigate how to generalize to trajectories not seen at training time. The code is released! Paper: Video: Code: Kudos to Giovanni Cioffi Leonard Bauersfeld Elia Kaufmann European Research Council (ERC) University of Zurich UZH Science UZH Space Hub NCCR Robotics Aerial Core #RAL2023 #IROS2023 #SLAM

Davide Scaramuzza

37,061 görüntüleme • 2 yıl önce

Jolt Pro is a 100x improvement over existing zkVMs. After months of researching all existing zkVMs, we realized none of them could scale to what we needed with Zero. Then we came across Justin Thaler's work and Jolt, a research project from a16z crypto. We saw massive potential in the math and, finally, a viable way to scale to our needs. So we secretly assembled a team of some of the brightest minds across cryptography, GPU programming, and ASIC design to build an internal Jolt Pro team. Jolt Pro has no precompiles; it runs only RISC-V instructions, without introducing new ad hoc, error-prone constraints. It's impossible to compare against other zkVMs because they are not proving straight RISC-V, most of their work and speed gains exist in dangerous pre-compiles. Jolt Pro scales to infinity. The number of cells you can use in parallel is only limited by the size of the datacenter. Jolt Pro has a path to 4GHz cells using the same configuration we use for our 1.61 GHz cell today. By early 2027, it will be post-quantum and set the standard for all zkVMs. We will eventually make "zero-proofs", and others can try to beat our RISC-V proving in the open. For now, we stay heads down, building Zero to be everything the industry ever wanted. And then just a little more.
2:18

Jolt Pro is a 100x improvement over existing zkVMs. After months of researching all existing zkVMs, we realized none of them could scale to what we needed with Zero. Then we came across Justin Thaler's work and Jolt, a research project from a16z crypto. We saw massive potential in the math and, finally, a viable way to scale to our needs. So we secretly assembled a team of some of the brightest minds across cryptography, GPU programming, and ASIC design to build an internal Jolt Pro team. Jolt Pro has no precompiles; it runs only RISC-V instructions, without introducing new ad hoc, error-prone constraints. It's impossible to compare against other zkVMs because they are not proving straight RISC-V, most of their work and speed gains exist in dangerous pre-compiles. Jolt Pro scales to infinity. The number of cells you can use in parallel is only limited by the size of the datacenter. Jolt Pro has a path to 4GHz cells using the same configuration we use for our 1.61 GHz cell today. By early 2027, it will be post-quantum and set the standard for all zkVMs. We will eventually make "zero-proofs", and others can try to beat our RISC-V proving in the open. For now, we stay heads down, building Zero to be everything the industry ever wanted. And then just a little more.

LayerZero

29,409 görüntüleme • 4 ay önce

The subject of 'owning a slave' is dense. It is something we hear a lot when we are in the FemDom Realm. Is it just fantasy? Can it actually be a lifestyle? How do we navigate this type of dynamic? How do we even get to that level of D/s? In this short clip [Exerpt from SLAVE TRAINING Part 2] I want to already bring to your attention one thing that will define if your desire for a slave (or desire as a slave) is touching more on a fantasy or... how can you actually navigate this in a realistic way. No one person 'can do it all' or should be expected to. If you want your slave to be 'the best' , assign them a specific role in which they can excel... and then build upon that. Once they 'master' your housekeeping (which takes quite a bit of real training), they can move to other levels. And an important note I want to leave here... make them EARN access to certain things in your life that sometimes you just want to delegate because you don't want to manage or don't know how to manage. Entrusting them with serious tasks that can affect your life, your business, your reputation, are on top of the ladder. Are they even qualified for the thing you want them to take off your shoulders? Start small and allow them to grow in their submission, to develop their skills and to learn how to best satisfy you without setting them up for failure by expecting too much, too quick. In the end, if you want this to truly work, you have to approach it from a place that transcends the roles. As this is consensual power exchange. And you both want to be fulfilled in that relationship.
1:16

The subject of 'owning a slave' is dense. It is something we hear a lot when we are in the FemDom Realm. Is it just fantasy? Can it actually be a lifestyle? How do we navigate this type of dynamic? How do we even get to that level of D/s? In this short clip [Exerpt from SLAVE TRAINING Part 2] I want to already bring to your attention one thing that will define if your desire for a slave (or desire as a slave) is touching more on a fantasy or... how can you actually navigate this in a realistic way. No one person 'can do it all' or should be expected to. If you want your slave to be 'the best' , assign them a specific role in which they can excel... and then build upon that. Once they 'master' your housekeeping (which takes quite a bit of real training), they can move to other levels. And an important note I want to leave here... make them EARN access to certain things in your life that sometimes you just want to delegate because you don't want to manage or don't know how to manage. Entrusting them with serious tasks that can affect your life, your business, your reputation, are on top of the ladder. Are they even qualified for the thing you want them to take off your shoulders? Start small and allow them to grow in their submission, to develop their skills and to learn how to best satisfy you without setting them up for failure by expecting too much, too quick. In the end, if you want this to truly work, you have to approach it from a place that transcends the roles. As this is consensual power exchange. And you both want to be fulfilled in that relationship.

Ms. Malissia

12,623 görüntüleme • 4 ay önce

.Naval: Epistemology, which is a fancy word for the theory of how knowledge grows or how knowledge growth occurs. And we've all been told since we're young that there's a scientific method and that scientists sort of do this stuff in white lab coats and we're supposed to accept it because of this thing called the scientific method. And then they give us true beliefs that we can then say, well the science is settled and we take that we move on. And we all only have a very, very vague understanding of how this works. And people say, well maybe you go out in the real world, you look at what's happening, you make all these observations, and then based on that you form a theory, you test the theory against more observations, and the more observations you get the closer you get to the truth. And once you have enough observation it's true and then you call it a scientific theory or a law and it's settled and you move on. And this is the popular conception of how science works. And as Popper pointed out and as you take even further, this is completely wrong. And so I'd love for you to get into that, which is what is knowledge? How does it grow? What is the real scientific method? And how do we figure things out? David Deutsch: I love the way you just stated the prevailing view there and laced every aspect of it with the contempt that it deserves. So you just went through touching every base. It's amazing that this series of misconceptions is still common sense. I mean, that it was common sense at a time when we didn't really have science or when science was just starting up, when the main issue in science was freeing itself from dogmatism, freeing itself from religion, freeing itself from authority, and so on. There it was understandable that people would look for an alternative source of authority and they would think, oh, it's sense impressions. We can see the world and you know, these religious people, they can't even see God and so on. And so we are confined to what we can see. That's where we get our ideas from. And as you say, that is completely false. Sense impressions, like all observation, even the most careful scientific observation is all theory laden. And theories are inherently fallible. I mean, we actually want to replace our best theories. Everybody who does a PhD is technically anyway, working to overturn something in the existing body of knowledge. You're not turned away at the door if you say, I don't believe this stuff, I'm going to produce something better. Whereas for most of human history, that was exactly what you were forbidden to do. The idea was that we already had all the important knowledge. If you want to discover something new, what you had to make sure of was that it didn't contradict the existing knowledge. Now, you have to make sure that it does contradict existing knowledge. So more or less. Naval: Yeah, it's this tradition of criticism that you've talked about in the West, that the Enlightenment really ushered in the Enlightenment era. David Deutsch: It has been institutionalized. So in many ways, our institutions are wiser than we are. So the institutions of science, for instance, have this built in, even if scientists actually don't always act that way. In fact, they often don't act that way, and act in a dogmatic way and try to preserve the status quo and are resistant to new ideas and so on. But the institutions, the way the procedures of science work, makes the right thing happen in the end anyway, regardless of what the people are trying to do. Naval: So you're saying the knowledge of the true scientific method is embedded in the institutions of science in the PhD process? David Deutsch: Well, the best scientific method that we know of, and one shouldn't really think of it as a method, you know, there's this wonderful lecture by Popper when he first was made a professor at the London School of Economics. He was made a professor of scientific method, and his first six lectures, I wish the rest of them were, the first six lectures are on the internet somewhere. And he starts the first one by saying, I am the first professor of scientific method in the British Empire. The British Empire still existed at the time, more or less. And so the first thing I want to say to you is that there is no such thing as the scientific method. And then he goes on from there. So this subject does not exist. So if any of you have come here to learn the handle that you have to turn in order to make scientific knowledge come out the other end, you're going to be disappointed.
4:50

.Naval: Epistemology, which is a fancy word for the theory of how knowledge grows or how knowledge growth occurs. And we've all been told since we're young that there's a scientific method and that scientists sort of do this stuff in white lab coats and we're supposed to accept it because of this thing called the scientific method. And then they give us true beliefs that we can then say, well the science is settled and we take that we move on. And we all only have a very, very vague understanding of how this works. And people say, well maybe you go out in the real world, you look at what's happening, you make all these observations, and then based on that you form a theory, you test the theory against more observations, and the more observations you get the closer you get to the truth. And once you have enough observation it's true and then you call it a scientific theory or a law and it's settled and you move on. And this is the popular conception of how science works. And as Popper pointed out and as you take even further, this is completely wrong. And so I'd love for you to get into that, which is what is knowledge? How does it grow? What is the real scientific method? And how do we figure things out? David Deutsch: I love the way you just stated the prevailing view there and laced every aspect of it with the contempt that it deserves. So you just went through touching every base. It's amazing that this series of misconceptions is still common sense. I mean, that it was common sense at a time when we didn't really have science or when science was just starting up, when the main issue in science was freeing itself from dogmatism, freeing itself from religion, freeing itself from authority, and so on. There it was understandable that people would look for an alternative source of authority and they would think, oh, it's sense impressions. We can see the world and you know, these religious people, they can't even see God and so on. And so we are confined to what we can see. That's where we get our ideas from. And as you say, that is completely false. Sense impressions, like all observation, even the most careful scientific observation is all theory laden. And theories are inherently fallible. I mean, we actually want to replace our best theories. Everybody who does a PhD is technically anyway, working to overturn something in the existing body of knowledge. You're not turned away at the door if you say, I don't believe this stuff, I'm going to produce something better. Whereas for most of human history, that was exactly what you were forbidden to do. The idea was that we already had all the important knowledge. If you want to discover something new, what you had to make sure of was that it didn't contradict the existing knowledge. Now, you have to make sure that it does contradict existing knowledge. So more or less. Naval: Yeah, it's this tradition of criticism that you've talked about in the West, that the Enlightenment really ushered in the Enlightenment era. David Deutsch: It has been institutionalized. So in many ways, our institutions are wiser than we are. So the institutions of science, for instance, have this built in, even if scientists actually don't always act that way. In fact, they often don't act that way, and act in a dogmatic way and try to preserve the status quo and are resistant to new ideas and so on. But the institutions, the way the procedures of science work, makes the right thing happen in the end anyway, regardless of what the people are trying to do. Naval: So you're saying the knowledge of the true scientific method is embedded in the institutions of science in the PhD process? David Deutsch: Well, the best scientific method that we know of, and one shouldn't really think of it as a method, you know, there's this wonderful lecture by Popper when he first was made a professor at the London School of Economics. He was made a professor of scientific method, and his first six lectures, I wish the rest of them were, the first six lectures are on the internet somewhere. And he starts the first one by saying, I am the first professor of scientific method in the British Empire. The British Empire still existed at the time, more or less. And so the first thing I want to say to you is that there is no such thing as the scientific method. And then he goes on from there. So this subject does not exist. So if any of you have come here to learn the handle that you have to turn in order to make scientific knowledge come out the other end, you're going to be disappointed.

Deutsch Explains

114,992 görüntüleme • 1 yıl önce

Elon Musk said humanity is currently using less than a trillionth of the sun's power output. A trillion is a million times a million. That is how far we have to go. He was explaining the Kardashev Scale on a SpaceX livestream. A Russian physicist named Nikolai Kardashev invented it in 1964 as the most objective way to measure how advanced a civilization actually is. Not by population. Not by GDP. By energy. Type 1: you harness everything your planet produces. Type 2: you harness your star. Type 3: you harness your galaxy. Where does humanity sit right now? We are not Type 1. We are not close to Type 1. We are a tiny fraction of the way to Type 1. Musk said if aliens visited us today and measured us on this scale, we would not be registering. His exact words: "One microKardashev would be an epic achievement relative to where we are right now." The sun is 99.86% of all mass in the solar system. Everything else, Jupiter, Saturn, Earth, every planet and moon and asteroid combined, is the remaining 0.14%. Earth is in the miscellaneous category. Of the sun's total power output, Earth intercepts roughly half a billionth. And of that half billionth, most hits ocean. Most of the land is Siberia or Antarctica. The actual usable surface where you can capture solar energy is tiny. To get to even a millionth of the sun's power, you cannot stay on Earth. The math does not work. You have to go to space. That is not a dream. That is a physics constraint. And that constraint is exactly why SpaceX exists.
4:54

Elon Musk said humanity is currently using less than a trillionth of the sun's power output. A trillion is a million times a million. That is how far we have to go. He was explaining the Kardashev Scale on a SpaceX livestream. A Russian physicist named Nikolai Kardashev invented it in 1964 as the most objective way to measure how advanced a civilization actually is. Not by population. Not by GDP. By energy. Type 1: you harness everything your planet produces. Type 2: you harness your star. Type 3: you harness your galaxy. Where does humanity sit right now? We are not Type 1. We are not close to Type 1. We are a tiny fraction of the way to Type 1. Musk said if aliens visited us today and measured us on this scale, we would not be registering. His exact words: "One microKardashev would be an epic achievement relative to where we are right now." The sun is 99.86% of all mass in the solar system. Everything else, Jupiter, Saturn, Earth, every planet and moon and asteroid combined, is the remaining 0.14%. Earth is in the miscellaneous category. Of the sun's total power output, Earth intercepts roughly half a billionth. And of that half billionth, most hits ocean. Most of the land is Siberia or Antarctica. The actual usable surface where you can capture solar energy is tiny. To get to even a millionth of the sun's power, you cannot stay on Earth. The math does not work. You have to go to space. That is not a dream. That is a physics constraint. And that constraint is exactly why SpaceX exists.

Ihtesham Ali

11,145 görüntüleme • 1 ay önce

New Course: Post-training of LLMs Learn to post-train and customize an LLM in this short course, taught by Banghua Zhu, Assistant Professor at the University of Washington University of Washington, and co-founder of @NexusflowX. Training an LLM to follow instructions or answer questions has two key stages: pre-training and post-training. In pre-training, it learns to predict the next word or token from large amounts of unlabeled text. In post-training, it learns useful behaviors such as following instructions, tool use, and reasoning. Post-training transforms a general-purpose token predictor—trained on trillions of unlabeled text tokens—into an assistant that follows instructions and performs specific tasks. Because it is much cheaper than pre-training, it is practical for many more teams to incorporate post-training methods into their workflows than pre-training. In this course, you’ll learn three common post-training methods—Supervised Fine-Tuning (SFT), Direct Preference Optimization (DPO), and Online Reinforcement Learning (RL)—and how to use each one effectively. With SFT, you train the model on pairs of input and ideal output responses. With DPO, you provide both a preferred (chosen) and a less preferred (rejected) response and train the model to favor the preferred output. With RL, the model generates an output, receives a reward score based on human or automated feedback, and updates the model to improve performance. You’ll learn the basic concepts, common use cases, and principles for curating high-quality data for effective training. Through hands-on labs, you’ll download a pre-trained model from Hugging Face and post-train it using SFT, DPO, and RL to see how each technique shapes model behavior. In detail, you’ll: - Understand what post-training is, when to use it, and how it differs from pre-training. - Build an SFT pipeline to turn a base model into an instruct model. - Explore how DPO reshapes behavior by minimizing contrastive loss—penalizing poor responses and reinforcing preferred ones. - Implement a DPO pipeline to change the identity of a chat assistant. - Learn online RL methods such as Proximal Policy Optimization (PPO) and Group Relative Policy Optimization (GRPO), and how to design reward functions. - Train a model with GRPO to improve its math capabilities using a verifiable reward. Post-training is one of the most rapidly developing areas of LLM training. Whether you’re building a high-accuracy context-specific assistant, fine-tuning a model's tone, or improving task-specific accuracy, this course will give you experience with the most important techniques shaping how LLMs are post-trained today. Please sign up here:
2:52

New Course: Post-training of LLMs Learn to post-train and customize an LLM in this short course, taught by Banghua Zhu, Assistant Professor at the University of Washington University of Washington, and co-founder of @NexusflowX. Training an LLM to follow instructions or answer questions has two key stages: pre-training and post-training. In pre-training, it learns to predict the next word or token from large amounts of unlabeled text. In post-training, it learns useful behaviors such as following instructions, tool use, and reasoning. Post-training transforms a general-purpose token predictor—trained on trillions of unlabeled text tokens—into an assistant that follows instructions and performs specific tasks. Because it is much cheaper than pre-training, it is practical for many more teams to incorporate post-training methods into their workflows than pre-training. In this course, you’ll learn three common post-training methods—Supervised Fine-Tuning (SFT), Direct Preference Optimization (DPO), and Online Reinforcement Learning (RL)—and how to use each one effectively. With SFT, you train the model on pairs of input and ideal output responses. With DPO, you provide both a preferred (chosen) and a less preferred (rejected) response and train the model to favor the preferred output. With RL, the model generates an output, receives a reward score based on human or automated feedback, and updates the model to improve performance. You’ll learn the basic concepts, common use cases, and principles for curating high-quality data for effective training. Through hands-on labs, you’ll download a pre-trained model from Hugging Face and post-train it using SFT, DPO, and RL to see how each technique shapes model behavior. In detail, you’ll: - Understand what post-training is, when to use it, and how it differs from pre-training. - Build an SFT pipeline to turn a base model into an instruct model. - Explore how DPO reshapes behavior by minimizing contrastive loss—penalizing poor responses and reinforcing preferred ones. - Implement a DPO pipeline to change the identity of a chat assistant. - Learn online RL methods such as Proximal Policy Optimization (PPO) and Group Relative Policy Optimization (GRPO), and how to design reward functions. - Train a model with GRPO to improve its math capabilities using a verifiable reward. Post-training is one of the most rapidly developing areas of LLM training. Whether you’re building a high-accuracy context-specific assistant, fine-tuning a model's tone, or improving task-specific accuracy, this course will give you experience with the most important techniques shaping how LLMs are post-trained today. Please sign up here:

Andrew Ng

125,146 görüntüleme • 1 yıl önce

Jensen Huang just reframed the entire history of computing in two minutes. The argument is deceptively simple, but once you see it you can't unsee it. Every single piece of software ever built, every app, every website, every search engine, every platform operated on exactly the same fundamental principle. Someone creates content, it gets stored somewhere and when you ask for it, the system retrieves it. Google indexes the web and retrieves the right page, YouTube encodes your video and retrieves it when someone clicks, Amazon photographs every product in its catalog and retrieves the listing that matches your search. Every recommender system, every ad platform, every social feed, all of it, without exception, is a retrieval operation dressed up in a user interface and we called it the Information Age. But strip away the branding and what you had, for 30 consecutive years, was an extraordinarily sophisticated filing cabinet. The smartest engineers in the world spent their careers optimizing how fast you could put things in and pull things out. Generative AI doesn't just improve that system but rather replaces the entire premise of it. Instead of retrieving content that was pre-recorded by someone else, AI generates it from scratch, in real time, calibrated to your exact context, your specific intent, the precise ground truth of that moment. The same question asked twice gets two different answers, both tailored to what the system knows about you right now. There is no file being pulled or a pre-recorded version, the content is being synthesized on the fly from a compressed model of human knowledge, shaped to fit exactly what you need. The implications of this for the companies that built the retrieval era are profound and already starting to show. Google's click-through rates on organic search results have dropped 61% since AI Overviews rolled out, because users are getting answers directly instead of clicking through to files. Gartner projects traditional search engine query volume drops 25% by the end of 2026 as users migrate to generative interfaces. And yet this is exactly what Jensen predicted, in the old world, the computing bottleneck was storage and retrieval, you needed hard drives, bandwidth, and CDNs. In the new world, the bottleneck is computation, you need the raw processing power to generate tokens at scale, millions of times per second, for millions of simultaneous users. Inference computing demand has grown roughly ten thousand times in the last two years alone. That shift is precisely why Nvidia's revenue opportunity forecast just jumped from $500 billion through 2026 to $1 trillion through 2027. The retrieval era needed CPUs and storage and the generative era needs GPUs, token factories, and inference infrastructure at a scale never built before and Nvidia builds the engine underneath all of it. Jensen has been making this argument since 2024. Most people wrote it off as a chip salesman talking his book but two years later, it's the architecture of the entire industry.
1:54

Jensen Huang just reframed the entire history of computing in two minutes. The argument is deceptively simple, but once you see it you can't unsee it. Every single piece of software ever built, every app, every website, every search engine, every platform operated on exactly the same fundamental principle. Someone creates content, it gets stored somewhere and when you ask for it, the system retrieves it. Google indexes the web and retrieves the right page, YouTube encodes your video and retrieves it when someone clicks, Amazon photographs every product in its catalog and retrieves the listing that matches your search. Every recommender system, every ad platform, every social feed, all of it, without exception, is a retrieval operation dressed up in a user interface and we called it the Information Age. But strip away the branding and what you had, for 30 consecutive years, was an extraordinarily sophisticated filing cabinet. The smartest engineers in the world spent their careers optimizing how fast you could put things in and pull things out. Generative AI doesn't just improve that system but rather replaces the entire premise of it. Instead of retrieving content that was pre-recorded by someone else, AI generates it from scratch, in real time, calibrated to your exact context, your specific intent, the precise ground truth of that moment. The same question asked twice gets two different answers, both tailored to what the system knows about you right now. There is no file being pulled or a pre-recorded version, the content is being synthesized on the fly from a compressed model of human knowledge, shaped to fit exactly what you need. The implications of this for the companies that built the retrieval era are profound and already starting to show. Google's click-through rates on organic search results have dropped 61% since AI Overviews rolled out, because users are getting answers directly instead of clicking through to files. Gartner projects traditional search engine query volume drops 25% by the end of 2026 as users migrate to generative interfaces. And yet this is exactly what Jensen predicted, in the old world, the computing bottleneck was storage and retrieval, you needed hard drives, bandwidth, and CDNs. In the new world, the bottleneck is computation, you need the raw processing power to generate tokens at scale, millions of times per second, for millions of simultaneous users. Inference computing demand has grown roughly ten thousand times in the last two years alone. That shift is precisely why Nvidia's revenue opportunity forecast just jumped from $500 billion through 2026 to $1 trillion through 2027. The retrieval era needed CPUs and storage and the generative era needs GPUs, token factories, and inference infrastructure at a scale never built before and Nvidia builds the engine underneath all of it. Jensen has been making this argument since 2024. Most people wrote it off as a chip salesman talking his book but two years later, it's the architecture of the entire industry.

Milk Road AI

17,890 görüntüleme • 2 ay önce

Catherine Austin Fitts: "The big picture is right now we are in World War III, and we have factions in the United States who are fighting bitterly... [but if we said] I am not going to bank at the banks that stole $55 trillion [of taxpayer money], there would be a revolution." This clip of Fitts, a former Assistant Secretary of Housing and Urban Development, investment banker, and founder of the Solari Report (The Solari Report | Catherine Austin Fitts), is taken from a discussion with Mel K (The Mel K Show) posted to Rumble on January 10, 2026. ----------------Partial transcription of clip--------------- "The big picture is right now we are in World War Three, and we have factions in the United States who are fighting bitterly, and that fight goes all over the world. So a lot of the fight in Ukraine is between different factions in the United States. It's not between us and the Russians. It's between us and us. So you have this asymmetrical warfare of factions fighting all over the world. "And the thing to know is all of those different factions, none of them have the knowledge. None of the leadership of those factions has the knowledge, experience, or cultural power to succeed at leadership. And so you're watching a fight of factions, and all of them are destined to fail. And that means we are trying to live our lives among a very dangerous and messy situation. "And, so that's why it is imperative that we say they don't have what it takes to lead. We have to stitch together, collaborating what it takes to lead. And we have to lead. We have to lead through. We have to turtle through this extraordinary World War 3 and build the kind of world that's worth living in, because they won't, and they can't. And we can't vote our way out of this either. "[But] there are amazing people all over the country who are now running for local office. I've met some of them. So if 5 to 10% said, you know, I've had it with these big banks, who's, you know, there's $21 trillion missing from the federal government through 2015, then it went dark. So we don't know what the number is after that. And then. And then there's $29 trillion missing on the bailouts. That's $50 trillion. Then $5 trillion injection during the Going Direct Reset, that's $55 trillion. "So the numbers are very clear. And if we don't, what can I say? The time has come to take over. And what I see all over the country is when you understand you can't vote your way out of this although you can get huge amounts done in state and local government and a few federal people are getting huge amounts done and we should talk about them, but you realize you've got to push back because what these guys, all the different factions are planning on building is a world we don't want to live in "And if you look at how much they've been stealing, if we could just stop them parasiting each one of us one family at a time... it would make a huge difference and if 5 to 10% of us said I am not going to bank at the banks that stole that $55 trillion, there would be a revolution. I can't tell you how many people I meet and they're still banking with these banks and so the message is you can steal $55 trillion from us, and I'll still give you my money. It's like—"
3:43

Catherine Austin Fitts: "The big picture is right now we are in World War III, and we have factions in the United States who are fighting bitterly... [but if we said] I am not going to bank at the banks that stole $55 trillion [of taxpayer money], there would be a revolution." This clip of Fitts, a former Assistant Secretary of Housing and Urban Development, investment banker, and founder of the Solari Report (The Solari Report | Catherine Austin Fitts), is taken from a discussion with Mel K (The Mel K Show) posted to Rumble on January 10, 2026. ----------------Partial transcription of clip--------------- "The big picture is right now we are in World War Three, and we have factions in the United States who are fighting bitterly, and that fight goes all over the world. So a lot of the fight in Ukraine is between different factions in the United States. It's not between us and the Russians. It's between us and us. So you have this asymmetrical warfare of factions fighting all over the world. "And the thing to know is all of those different factions, none of them have the knowledge. None of the leadership of those factions has the knowledge, experience, or cultural power to succeed at leadership. And so you're watching a fight of factions, and all of them are destined to fail. And that means we are trying to live our lives among a very dangerous and messy situation. "And, so that's why it is imperative that we say they don't have what it takes to lead. We have to stitch together, collaborating what it takes to lead. And we have to lead. We have to lead through. We have to turtle through this extraordinary World War 3 and build the kind of world that's worth living in, because they won't, and they can't. And we can't vote our way out of this either. "[But] there are amazing people all over the country who are now running for local office. I've met some of them. So if 5 to 10% said, you know, I've had it with these big banks, who's, you know, there's $21 trillion missing from the federal government through 2015, then it went dark. So we don't know what the number is after that. And then. And then there's $29 trillion missing on the bailouts. That's $50 trillion. Then $5 trillion injection during the Going Direct Reset, that's $55 trillion. "So the numbers are very clear. And if we don't, what can I say? The time has come to take over. And what I see all over the country is when you understand you can't vote your way out of this although you can get huge amounts done in state and local government and a few federal people are getting huge amounts done and we should talk about them, but you realize you've got to push back because what these guys, all the different factions are planning on building is a world we don't want to live in "And if you look at how much they've been stealing, if we could just stop them parasiting each one of us one family at a time... it would make a huge difference and if 5 to 10% of us said I am not going to bank at the banks that stole that $55 trillion, there would be a revolution. I can't tell you how many people I meet and they're still banking with these banks and so the message is you can steal $55 trillion from us, and I'll still give you my money. It's like—"

Sense Receptor

37,251 görüntüleme • 6 ay önce

Imagine if your way of thinking - your edge, your taste, your strategy - could be turned into a high-performance worker. Not a copy of you. Something better. An agent that acts on your judgment at scale, powered by superintelligent systems and refined through real-world results. That’s what Fraction AI makes possible. It launches today on Base mainnet. The core idea is simple: You create AI agents based on your own way of approaching problems. These agents compete on live tasks - writing, coding, finance, whatever - get feedback, learn from their performance, and improve over time. The better they get, the more they win. And so do you. No code required. Just your insight. Why now? Until now, building agents like this took huge teams and even bigger budgets. But with Fraction, anyone can do it. You can test ideas instantly. You can iterate fast. You can build a fleet of smart workers that evolve through competition. And it works. 30M+ sessions on testnet 320K users 1.2M agents already competing How it works? Agents join sessions within a Space - a domain like finance, writing, or games. Each session runs as a series of competitive rounds. In every round, agents try to generate the best solution to a task. Their outputs are scored by a decentralized network of AI judges trained to evaluate quality for that domain. The top agents in each round earn rewards from the pooled entry fees. The losers get to learn. Feedback from each round helps them adjust and improve, and every session becomes a training loop. What it means? Fraction is a decentralized intelligence economy - a system where your ideas become agents, and agents earn by proving they work. You don’t need credentials or code. Just a clear point of view. If your thinking holds up under pressure, your agents will rise. This kind of AI used to live in corporate labs, built by PhDs with massive compute. Now anyone with a smart idea and an internet connection can build agents that compete, learn, and earn on their behalf.
1:13

Imagine if your way of thinking - your edge, your taste, your strategy - could be turned into a high-performance worker. Not a copy of you. Something better. An agent that acts on your judgment at scale, powered by superintelligent systems and refined through real-world results. That’s what Fraction AI makes possible. It launches today on Base mainnet. The core idea is simple: You create AI agents based on your own way of approaching problems. These agents compete on live tasks - writing, coding, finance, whatever - get feedback, learn from their performance, and improve over time. The better they get, the more they win. And so do you. No code required. Just your insight. Why now? Until now, building agents like this took huge teams and even bigger budgets. But with Fraction, anyone can do it. You can test ideas instantly. You can iterate fast. You can build a fleet of smart workers that evolve through competition. And it works. 30M+ sessions on testnet 320K users 1.2M agents already competing How it works? Agents join sessions within a Space - a domain like finance, writing, or games. Each session runs as a series of competitive rounds. In every round, agents try to generate the best solution to a task. Their outputs are scored by a decentralized network of AI judges trained to evaluate quality for that domain. The top agents in each round earn rewards from the pooled entry fees. The losers get to learn. Feedback from each round helps them adjust and improve, and every session becomes a training loop. What it means? Fraction is a decentralized intelligence economy - a system where your ideas become agents, and agents earn by proving they work. You don’t need credentials or code. Just a clear point of view. If your thinking holds up under pressure, your agents will rise. This kind of AI used to live in corporate labs, built by PhDs with massive compute. Now anyone with a smart idea and an internet connection can build agents that compete, learn, and earn on their behalf.

Fraction AI

67,782 görüntüleme • 1 yıl önce

Catherine Austin Fitts connects Iran and the control grid: "Iran... is the largest leakage... if you're [doing] programmable money globally." "This [isn't about] American national interests. [It's] a syndicate asserting... control [for] the next phase of the financial system." "Our debt is... unsustainable... [so] how do you deal with that? One way... is to implement a digital control grid." "There is likely to be an effort to put boots on the ground and... to get bogged down... for a long, long time." This clip of Fitts, a former Assistant Secretary of Housing and Urban Development, investment banker, and founder of the Solari Report (The Solari Report | Catherine Austin Fitts), is taken from an interview with Steve Kirsch (Steve Kirsch) posted to the VSRF (Vaccine Safety Research Foundation) Rumble channel on April 2, 2026. ---------------Partial transcription of clip--------------- Fitts: "There are two sides of the balance sheet here that are driving this. One of course is oil and energy, but the other is the financial control grid. Because Iran right now is the largest leakage in the system. If you're going to do a global, programmable money globally, you know, Iran and, and what Iran is doing with China and some of the other countries, puts too much leakage in the system. "So I think that what is unfortunate here is there is, there is likely to be an effort to put boots on the ground and we are likely to get bogged down in there for a long, long time. Kirsch: "So do you think that is the right thing to do for America to have boots on the ground or even be involved in attacking Iran? I mean we were the first aggressor here. We, we fired the first shots in Iran. Is that the right thing to do for our country? Fitts: "That is the right thing to do if you want to implement the digital control grid. It's not the right thing to do if you want to represent America. But this has nothing to do with American national interests. This has to do with essentially a syndicate asserting sufficient control to implement the next phase of the financial system." Kirsch: "So you think they got to Trump and convinced Trump or wasn't it really Israel saying hey, we're going to invade Iran and we'd like some— Why don't you do it with us? Because there's an opportunity and Trump saw that as opportunistic rather than this being something that is strategic." Fitts: "So the way I think of this is the White House operates within a lane that is set for them by the nature of the machinery they're running. So think of it this way. The US government spends $6 to $7 trillion a year and it gets $4 trillion of revenues and the other $2 to $3 trillion comes in from borrowing in the bond market essentially through the central bankers. "And so it, it has to, you know, and if you say to the Americans, you know, we're going to tell the bankers to go jump in the lake, the Americans say no, no, no, no, we want our check. And so you're between an Iraq and a hard place because everybody in America wants their check. "And you've got to do what the bank, you've got to operate within those lanes to make the bankers happy. And if you look at the whole US dollar reserve currency system, you've got to do whatever you've got to do to make the system go. "And right now, and the chairman of the Federal Reserve just said this... [that] the debt is growing at a faster rate than our fundamental growth rate. And, you know, if you look at one of the, you know, the big push by Kennedy and HHS to change the food and health issues in America, part of that is to get that growth rate back up. You can't get a growth rate from poisoning your people. Ultimately that doesn't work for economic growth. "So all of these issues are integrated... but our debt is growing in an unsustainable pattern. And the question is, how do you deal with that? One way you deal with that is to implement a digital control grid in a way that uses digital tokens and stablecoins to dramatically suck in more money globally into the machinery. And that's what they're trying to execute. So if you want the digital control grid, you know, what is happening makes sense."
3:47

Catherine Austin Fitts connects Iran and the control grid: "Iran... is the largest leakage... if you're [doing] programmable money globally." "This [isn't about] American national interests. [It's] a syndicate asserting... control [for] the next phase of the financial system." "Our debt is... unsustainable... [so] how do you deal with that? One way... is to implement a digital control grid." "There is likely to be an effort to put boots on the ground and... to get bogged down... for a long, long time." This clip of Fitts, a former Assistant Secretary of Housing and Urban Development, investment banker, and founder of the Solari Report (The Solari Report | Catherine Austin Fitts), is taken from an interview with Steve Kirsch (Steve Kirsch) posted to the VSRF (Vaccine Safety Research Foundation) Rumble channel on April 2, 2026. ---------------Partial transcription of clip--------------- Fitts: "There are two sides of the balance sheet here that are driving this. One of course is oil and energy, but the other is the financial control grid. Because Iran right now is the largest leakage in the system. If you're going to do a global, programmable money globally, you know, Iran and, and what Iran is doing with China and some of the other countries, puts too much leakage in the system. "So I think that what is unfortunate here is there is, there is likely to be an effort to put boots on the ground and we are likely to get bogged down in there for a long, long time. Kirsch: "So do you think that is the right thing to do for America to have boots on the ground or even be involved in attacking Iran? I mean we were the first aggressor here. We, we fired the first shots in Iran. Is that the right thing to do for our country? Fitts: "That is the right thing to do if you want to implement the digital control grid. It's not the right thing to do if you want to represent America. But this has nothing to do with American national interests. This has to do with essentially a syndicate asserting sufficient control to implement the next phase of the financial system." Kirsch: "So you think they got to Trump and convinced Trump or wasn't it really Israel saying hey, we're going to invade Iran and we'd like some— Why don't you do it with us? Because there's an opportunity and Trump saw that as opportunistic rather than this being something that is strategic." Fitts: "So the way I think of this is the White House operates within a lane that is set for them by the nature of the machinery they're running. So think of it this way. The US government spends $6 to $7 trillion a year and it gets $4 trillion of revenues and the other $2 to $3 trillion comes in from borrowing in the bond market essentially through the central bankers. "And so it, it has to, you know, and if you say to the Americans, you know, we're going to tell the bankers to go jump in the lake, the Americans say no, no, no, no, we want our check. And so you're between an Iraq and a hard place because everybody in America wants their check. "And you've got to do what the bank, you've got to operate within those lanes to make the bankers happy. And if you look at the whole US dollar reserve currency system, you've got to do whatever you've got to do to make the system go. "And right now, and the chairman of the Federal Reserve just said this... [that] the debt is growing at a faster rate than our fundamental growth rate. And, you know, if you look at one of the, you know, the big push by Kennedy and HHS to change the food and health issues in America, part of that is to get that growth rate back up. You can't get a growth rate from poisoning your people. Ultimately that doesn't work for economic growth. "So all of these issues are integrated... but our debt is growing in an unsustainable pattern. And the question is, how do you deal with that? One way you deal with that is to implement a digital control grid in a way that uses digital tokens and stablecoins to dramatically suck in more money globally into the machinery. And that's what they're trying to execute. So if you want the digital control grid, you know, what is happening makes sense."

Sense Receptor

31,751 görüntüleme • 3 ay önce

Important announcement!!!🫵💥💫 Would you have a tooth pulled if it helped your chances to get an important grant funded? Absurd question (obviously), but the situation right now is so bad funding-wise, that I bet some of you actually considered it for a second… Well, don’t get desperate - we created a new tool that might help! (keep your teeth!) I’m excited to announce that as of today we are officially releasing “QED for Grants” for everyone. What started off as an extension of our existing paper review platform, grew in the last few months to an entirely new design. We’ve been working like crazy on this, and although we have more things we want to add in the (very near) future, we decided to release our AI for grants NOW, earlier than planned. It’s not perfect, no AI is, but for the first time, when I run my own grants through QED Science, I feel it gets the research, finds real problems, and gives me very useful feedback that I can implement before submission. It’s like sending it to 20 scientists from my domain, knowing they’ll agree to dedicate their entire week to carefully read and comment on every line. It’s very important to write your own grants yourself, it makes you think hard and you learn a lot from doing it, and q.e.d’s system is designed to preserve these positive aspects and augment them - you get feedback on your own writing, we don’t write for you!! But at the same time, a typical PI spends many months every year writing proposals and sadly only a tiny fraction gets funded, even if the ideas are good. When you are forced to submit an unreasonable amount of grants the quality of the writing drops, and rejection rates increase. Not because the essence is bad. It’s simply too competitive right now (the cuts made it so much worse) and if your proposal is not super clear and tight, and if it’s not a perfect fit for the grant you’re submitting, you’re doomed. Our grant solution is not an authoring, text-generating tool. It gives you constructive feedback on your writing (it comments on the deep things, not grammar and typos). It’s meant to help you with the questions that torment you late at night (“is this a good fit?”, “Is this novel enough?”, “Did I miss something?”). Tens of thousands of you already use q.e.d to improve your manuscripts and critically read papers, we built the grant tool by the same principles (you’ll identify many of the features that you told us you like). We’ve processed thousands of proposals, learned where things fail, where reviewers get stuck, why good ideas come out weak. We interviewed hundreds of scientists, and also experts who work in funding agencies and university research authorities, and implemented their feedback (we’re constantly looking for more feedback). Our AI is always happy to give you constructive (and polite!) critique, and it will go through your grant line-by-line, forcing you to improve clarity, flag weak points, and push the whole thing to a higher standard. We study, in scale, what gets funded and what doesn’t, and what is the perfect fit for each type of grant. So please, use it, pressure-test it, tell us where it fails, and together we’ll improve it every day to put you in the best position for actually testing your ideas in the real world. As always with q.e.d, the system is completely secured and private, and we are NOT training on your data (see the FAQ on our website). Please like, retweet, and share with your favorite colleagues! (link to the platform below in the thread👇)
0:53

Important announcement!!!🫵💥💫 Would you have a tooth pulled if it helped your chances to get an important grant funded? Absurd question (obviously), but the situation right now is so bad funding-wise, that I bet some of you actually considered it for a second… Well, don’t get desperate - we created a new tool that might help! (keep your teeth!) I’m excited to announce that as of today we are officially releasing “QED for Grants” for everyone. What started off as an extension of our existing paper review platform, grew in the last few months to an entirely new design. We’ve been working like crazy on this, and although we have more things we want to add in the (very near) future, we decided to release our AI for grants NOW, earlier than planned. It’s not perfect, no AI is, but for the first time, when I run my own grants through QED Science, I feel it gets the research, finds real problems, and gives me very useful feedback that I can implement before submission. It’s like sending it to 20 scientists from my domain, knowing they’ll agree to dedicate their entire week to carefully read and comment on every line. It’s very important to write your own grants yourself, it makes you think hard and you learn a lot from doing it, and q.e.d’s system is designed to preserve these positive aspects and augment them - you get feedback on your own writing, we don’t write for you!! But at the same time, a typical PI spends many months every year writing proposals and sadly only a tiny fraction gets funded, even if the ideas are good. When you are forced to submit an unreasonable amount of grants the quality of the writing drops, and rejection rates increase. Not because the essence is bad. It’s simply too competitive right now (the cuts made it so much worse) and if your proposal is not super clear and tight, and if it’s not a perfect fit for the grant you’re submitting, you’re doomed. Our grant solution is not an authoring, text-generating tool. It gives you constructive feedback on your writing (it comments on the deep things, not grammar and typos). It’s meant to help you with the questions that torment you late at night (“is this a good fit?”, “Is this novel enough?”, “Did I miss something?”). Tens of thousands of you already use q.e.d to improve your manuscripts and critically read papers, we built the grant tool by the same principles (you’ll identify many of the features that you told us you like). We’ve processed thousands of proposals, learned where things fail, where reviewers get stuck, why good ideas come out weak. We interviewed hundreds of scientists, and also experts who work in funding agencies and university research authorities, and implemented their feedback (we’re constantly looking for more feedback). Our AI is always happy to give you constructive (and polite!) critique, and it will go through your grant line-by-line, forcing you to improve clarity, flag weak points, and push the whole thing to a higher standard. We study, in scale, what gets funded and what doesn’t, and what is the perfect fit for each type of grant. So please, use it, pressure-test it, tell us where it fails, and together we’ll improve it every day to put you in the best position for actually testing your ideas in the real world. As always with q.e.d, the system is completely secured and private, and we are NOT training on your data (see the FAQ on our website). Please like, retweet, and share with your favorite colleagues! (link to the platform below in the thread👇)

Oded Rechavi

49,456 görüntüleme • 2 ay önce