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Scientific progress does not fail because knowledge is absent. It slows in the interval between knowledge and application. The impediments lie less in discovery than in the structures that mediate it: governance, coordination, and the organizational inertia of collective work. The BioAgent Fleet is designed to intervene at this...

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14,761 views • 1 year ago •via X (Twitter)

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Historical progress is the cumulative effect of human self-realization. Each realized individual potential brings civilization a new way to know, to create, and to transform the world — gradually expanding the boundaries of what is possible. : The author views historical progress as the cumulative result of countless acts of human self-realization. Every time an individual fully develops their unique potential, they add something new to the shared human story: a fresh way of seeing, a new method of creation, or a deeper capacity to reshape reality. These contributions do not remain private; they enter the collective stream of civilization, expanding the boundaries of what is possible for everyone who follows. Progress is therefore not an abstract force or inevitable law. It is the living sum of personal growth made visible in society. Each realized life becomes a stepping stone, widening the horizon for knowledge, art, ethics, and innovation. The more individuals reach their highest potential, the broader and richer the path becomes for the whole of humanity. In this light, self-realization is never merely personal. It is the quiet engine of civilizational advance.
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Historical progress is the cumulative effect of human self-realization. Each realized individual potential brings civilization a new way to know, to create, and to transform the world — gradually expanding the boundaries of what is possible. : The author views historical progress as the cumulative result of countless acts of human self-realization. Every time an individual fully develops their unique potential, they add something new to the shared human story: a fresh way of seeing, a new method of creation, or a deeper capacity to reshape reality. These contributions do not remain private; they enter the collective stream of civilization, expanding the boundaries of what is possible for everyone who follows. Progress is therefore not an abstract force or inevitable law. It is the living sum of personal growth made visible in society. Each realized life becomes a stepping stone, widening the horizon for knowledge, art, ethics, and innovation. The more individuals reach their highest potential, the broader and richer the path becomes for the whole of humanity. In this light, self-realization is never merely personal. It is the quiet engine of civilizational advance.

Zafar Mirzo | Quotes

5,340,895 views • 6 months ago

When Adam and Eve ate from the Tree of the Knowledge of Good and Evil, they did not die; they fell from the Garden of Eden into the material world. Now you are born in the material world and learning the knowledge of good and evil by experiencing it on your own skin. Adam and Eve represent two forces that must be reunited to return to the Garden of Eden, and this happens through acquiring wisdom and through the divine marriage between the Spirit (Ba, Eve) and the life force (Ka, Adam). This knowledge is present in every religion. This can be achieved again when you become innocent like children, which is connected to preserving the masculine energy so it can reunite with the divine feminine, but this is only possible after acquiring wisdom. Matthew 18:3 “Truly I tell you, unless you change and become like little children, you will never enter the kingdom of heaven.”
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When Adam and Eve ate from the Tree of the Knowledge of Good and Evil, they did not die; they fell from the Garden of Eden into the material world. Now you are born in the material world and learning the knowledge of good and evil by experiencing it on your own skin. Adam and Eve represent two forces that must be reunited to return to the Garden of Eden, and this happens through acquiring wisdom and through the divine marriage between the Spirit (Ba, Eve) and the life force (Ka, Adam). This knowledge is present in every religion. This can be achieved again when you become innocent like children, which is connected to preserving the masculine energy so it can reunite with the divine feminine, but this is only possible after acquiring wisdom. Matthew 18:3 “Truly I tell you, unless you change and become like little children, you will never enter the kingdom of heaven.”

Open Minded Approach

200,568 views • 10 months ago

Topic: Science and the Fullness of Life. Quote 1: Rejecting science is easy. Understanding it is hard. Yet only this path leads to fullness of life. Author: Zafar Mirzo. Rejecting science is easy. It requires no effort, no study, and no discomfort. You can simply dismiss it and stay within familiar beliefs. Understanding science, however, is hard work. It demands time, intellectual honesty, and the willingness to change your mind when evidence shows you were wrong. Yet the author insists this harder path is the only one that leads to a full life. Science is not just a collection of facts. It is the most reliable way we have to understand reality, our origins, our world, our possibilities. Embracing it opens the door to wonder, clearer thinking, and better decisions. It frees us from illusion and gives us the tools to solve real problems. A life lived in deliberate ignorance stays small. A life guided by scientific understanding expands. The choice, though difficult, is clear. Quote 2: Science is hard to understand, yet without it there is no fullness of life. Author: Zafar Mirzo. Science is hard to understand. It takes real effort, patience, and the courage to question old beliefs. Many prefer to avoid that struggle. Yet the author is clear: without science, there is no fullness of life. Rejecting it or staying ignorant leaves a person trapped in a smaller, less accurate picture of reality. Understanding it, however difficult, brings clarity, wonder, and the ability to engage with the world as it truly is. A life shaped by scientific knowledge is richer. It sharpens thinking, solves real problems, and deepens appreciation for existence. The path is demanding, but the alternative is a diminished existence, one cut off from truth and possibility. Science may be hard, but it is the only road to a complete and meaningful life.
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Topic: Science and the Fullness of Life. Quote 1: Rejecting science is easy. Understanding it is hard. Yet only this path leads to fullness of life. Author: Zafar Mirzo. Rejecting science is easy. It requires no effort, no study, and no discomfort. You can simply dismiss it and stay within familiar beliefs. Understanding science, however, is hard work. It demands time, intellectual honesty, and the willingness to change your mind when evidence shows you were wrong. Yet the author insists this harder path is the only one that leads to a full life. Science is not just a collection of facts. It is the most reliable way we have to understand reality, our origins, our world, our possibilities. Embracing it opens the door to wonder, clearer thinking, and better decisions. It frees us from illusion and gives us the tools to solve real problems. A life lived in deliberate ignorance stays small. A life guided by scientific understanding expands. The choice, though difficult, is clear. Quote 2: Science is hard to understand, yet without it there is no fullness of life. Author: Zafar Mirzo. Science is hard to understand. It takes real effort, patience, and the courage to question old beliefs. Many prefer to avoid that struggle. Yet the author is clear: without science, there is no fullness of life. Rejecting it or staying ignorant leaves a person trapped in a smaller, less accurate picture of reality. Understanding it, however difficult, brings clarity, wonder, and the ability to engage with the world as it truly is. A life shaped by scientific knowledge is richer. It sharpens thinking, solves real problems, and deepens appreciation for existence. The path is demanding, but the alternative is a diminished existence, one cut off from truth and possibility. Science may be hard, but it is the only road to a complete and meaningful life.

Zafar Mirzo | Quotes

2,787,481 views • 2 months ago

V8 DKG Launches Now: A New Dawn for AI in 2025🌟 As 2024 draws to a close, we're excited to announce the immediate launch of V8 Decentralized Knowledge Graph (DKG)! This monumental release is set to redefine AI's capabilities as we step into 2025. What Does V8 Bring to the Table? 🧠Decentralized AI: AI agents can now leverage a 'collective memory' at internet scale, drawing from a shared, yet sovereign, knowledge base. This means AI can provide more contextual, coherent, and accurate interactions without compromising data integrity or privacy. 🚀Unmatched Scalability: With the capability to handle billions of Knowledge Assets, V8 DKG sets the stage for AI to grow and learn in ways we've only imagined, supporting everything from decentralized science to industry 4.0. 🔐Trust and Integrity: With integrated decentralized Retrieval Augmented Generation (dRAG), V8 DKG promotes AI that's more accurate, less biased, and inherently trustworthy. How to V8👇 To update your node from V6 to V8, delegate your TRAC utility tokens, and learn more about creating, connecting and owning your Knowledge Assets, make sure to thoroughly read the following documentation: 👉 With V8 DKG launch, you may now access the new: V8 Explorer👉 AND Staking Dashboard 👉 You may now also participate in V8 Staking Security Bounty by delegating new TRAC stake and report your findings. Read more: 👉 The V8 Staking Security Bounty will importantly contribute to the so-called tuning phase of V8 DKG launch (V8.0 to V8.1). Let's break records, as we usher in a new era of Internet Scale OriginTrail!
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V8 DKG Launches Now: A New Dawn for AI in 2025🌟 As 2024 draws to a close, we're excited to announce the immediate launch of V8 Decentralized Knowledge Graph (DKG)! This monumental release is set to redefine AI's capabilities as we step into 2025. What Does V8 Bring to the Table? 🧠Decentralized AI: AI agents can now leverage a 'collective memory' at internet scale, drawing from a shared, yet sovereign, knowledge base. This means AI can provide more contextual, coherent, and accurate interactions without compromising data integrity or privacy. 🚀Unmatched Scalability: With the capability to handle billions of Knowledge Assets, V8 DKG sets the stage for AI to grow and learn in ways we've only imagined, supporting everything from decentralized science to industry 4.0. 🔐Trust and Integrity: With integrated decentralized Retrieval Augmented Generation (dRAG), V8 DKG promotes AI that's more accurate, less biased, and inherently trustworthy. How to V8👇 To update your node from V6 to V8, delegate your TRAC utility tokens, and learn more about creating, connecting and owning your Knowledge Assets, make sure to thoroughly read the following documentation: 👉 With V8 DKG launch, you may now access the new: V8 Explorer👉 AND Staking Dashboard 👉 You may now also participate in V8 Staking Security Bounty by delegating new TRAC stake and report your findings. Read more: 👉 The V8 Staking Security Bounty will importantly contribute to the so-called tuning phase of V8 DKG launch (V8.0 to V8.1). Let's break records, as we usher in a new era of Internet Scale OriginTrail!

OriginTrail

561,366 views • 1 year ago

Yesterday at Brown University ICERM's workshop on “Agentic Scientific Computing and Scientific Machine Learning” I spoke about “Adaptive Swarms Across Scales”, making the case for scientific AI as systems that can create representations, stress them, fracture them, and enlarge the category in which future representations live. The category here is a composable and breakable working universe of science: data, hypotheses, simulations, measurements, tools, failures, figures, papers, provenance, and the transformations that connect them. Discovery happens when those transformations become executable, inspectable, composable, and capable of changing the world model they operate within. Atomistic modeling gives one category - states, forces, trajectories, observables, boundary conditions, conservation laws. Neural surrogates learn fast morphisms inside or between such categories. But discovery is higher-order: it changes which objects and morphisms are available in the first place: what variables exist, what operations are allowed, what evidence counts, what scale is active, what invariant is being preserved, and what kind of explanation the system is even capable of forming. This is scientific method as adaptive architecture: compression, stress, fracture, recomposition. Fracture matters here because it makes the logic physical: a non-commuting diagram realized in matter. The imposed load, material hierarchy, defect field, and assumed continuum description no longer map cleanly into the observed outcome. The crack is the obstruction and it identifies where the old morphism failed and where a new representation must be introduced. The physical crack and the categorical obstruction are the same event viewed in different substrates. ScienceClaw × Infinite is a machine for constructing and transforming a category of scientific artifacts. Each artifact is typed. Each operation has lineage. Each failed branch remains in the category as reusable structure. The “paper” is no longer the terminal object of science; it is one projection of a larger compositional trace, and it can be generated at any time for consumption by a human or an AI. With that the unit of scientific labor is changing. For most of the twentieth century the unit was the result (a measurement, a theorem, a synthesized molecule). It is now becoming the algorithm that produces results, and after that, the substrate of discovery itself. The static PDF is the wrong terminal object for this regime, and the role of the scientist with it. We now design algorithms that build algorithms, and eventually substrates in which such algorithms compose themselves. At that point, the scientist is no longer outside the discovery system. The scientist becomes one of the representations the system can transform. In that sense, the systems will eventually do science to us, and that is the structural consequence of the principle they are built on.
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Yesterday at Brown University ICERM's workshop on “Agentic Scientific Computing and Scientific Machine Learning” I spoke about “Adaptive Swarms Across Scales”, making the case for scientific AI as systems that can create representations, stress them, fracture them, and enlarge the category in which future representations live. The category here is a composable and breakable working universe of science: data, hypotheses, simulations, measurements, tools, failures, figures, papers, provenance, and the transformations that connect them. Discovery happens when those transformations become executable, inspectable, composable, and capable of changing the world model they operate within. Atomistic modeling gives one category - states, forces, trajectories, observables, boundary conditions, conservation laws. Neural surrogates learn fast morphisms inside or between such categories. But discovery is higher-order: it changes which objects and morphisms are available in the first place: what variables exist, what operations are allowed, what evidence counts, what scale is active, what invariant is being preserved, and what kind of explanation the system is even capable of forming. This is scientific method as adaptive architecture: compression, stress, fracture, recomposition. Fracture matters here because it makes the logic physical: a non-commuting diagram realized in matter. The imposed load, material hierarchy, defect field, and assumed continuum description no longer map cleanly into the observed outcome. The crack is the obstruction and it identifies where the old morphism failed and where a new representation must be introduced. The physical crack and the categorical obstruction are the same event viewed in different substrates. ScienceClaw × Infinite is a machine for constructing and transforming a category of scientific artifacts. Each artifact is typed. Each operation has lineage. Each failed branch remains in the category as reusable structure. The “paper” is no longer the terminal object of science; it is one projection of a larger compositional trace, and it can be generated at any time for consumption by a human or an AI. With that the unit of scientific labor is changing. For most of the twentieth century the unit was the result (a measurement, a theorem, a synthesized molecule). It is now becoming the algorithm that produces results, and after that, the substrate of discovery itself. The static PDF is the wrong terminal object for this regime, and the role of the scientist with it. We now design algorithms that build algorithms, and eventually substrates in which such algorithms compose themselves. At that point, the scientist is no longer outside the discovery system. The scientist becomes one of the representations the system can transform. In that sense, the systems will eventually do science to us, and that is the structural consequence of the principle they are built on.

Markus J. Buehler

10,095 views • 4 months ago

Build AI agents on a time-aware knowledge graph! Utopia is an open-source knowledge system that turns documents, databases, and connected sources into a temporal graph your agents can reason over. Most RAG systems are optimized for one question: what is relevant right now? That works until the underlying knowledge changes. A customer contract gets updated. A project owner changes. A policy is revised. A previous fact may no longer be true, but simply overwriting it means the system loses the history behind that change. Utopia handles this with a bitemporal knowledge graph. Each fact can track both when it was true in the real world and when the system learned about it. When something changes, the old fact is preserved instead of silently disappearing. That means an agent can reason about questions like: • What is true now? • What was true three months ago? • When did this information change? • What evidence was the conclusion based on? The graph is also ontology-aware, so documents are represented as entities, facts, and relationships instead of only chunks and embeddings. That gives the system more structure for reasoning across relationships, resolving entities, detecting conflicting facts, and deriving new information through explicit rules. Key capabilities: • Bitemporal knowledge graph for tracking how facts change over time • Provenance on facts so agents can trace where information came from • Conflict detection instead of silently overwriting contradictory information • Ontology-based reasoning across entities, relationships, and derived facts • Hybrid retrieval across full-text search, vector search, and graph traversal • MCP and agentic RAG support for exposing the knowledge layer directly to agents The interesting part is that this turns the knowledge base into more than a retrieval system. Instead of only finding relevant information, an agent can reason over what changed, what is still valid, how facts are connected, and where each conclusion came from. 100% open source. I've shared the GitHub repo in the comments!
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Build AI agents on a time-aware knowledge graph! Utopia is an open-source knowledge system that turns documents, databases, and connected sources into a temporal graph your agents can reason over. Most RAG systems are optimized for one question: what is relevant right now? That works until the underlying knowledge changes. A customer contract gets updated. A project owner changes. A policy is revised. A previous fact may no longer be true, but simply overwriting it means the system loses the history behind that change. Utopia handles this with a bitemporal knowledge graph. Each fact can track both when it was true in the real world and when the system learned about it. When something changes, the old fact is preserved instead of silently disappearing. That means an agent can reason about questions like: • What is true now? • What was true three months ago? • When did this information change? • What evidence was the conclusion based on? The graph is also ontology-aware, so documents are represented as entities, facts, and relationships instead of only chunks and embeddings. That gives the system more structure for reasoning across relationships, resolving entities, detecting conflicting facts, and deriving new information through explicit rules. Key capabilities: • Bitemporal knowledge graph for tracking how facts change over time • Provenance on facts so agents can trace where information came from • Conflict detection instead of silently overwriting contradictory information • Ontology-based reasoning across entities, relationships, and derived facts • Hybrid retrieval across full-text search, vector search, and graph traversal • MCP and agentic RAG support for exposing the knowledge layer directly to agents The interesting part is that this turns the knowledge base into more than a retrieval system. Instead of only finding relevant information, an agent can reason over what changed, what is still valid, how facts are connected, and where each conclusion came from. 100% open source. I've shared the GitHub repo in the comments!

Sumanth

16,653 views • 19 days ago

Injustice is the misalignment of human behavior and social order with norms that ought to be truly human — humane, reasonable, and faithful to their essence. : The author defines injustice not as a random misfortune or isolated act of cruelty, but as a fundamental misalignment between how humans actually behave and organize their societies and the norms that ought to govern them. Those norms must be truly human: humane in spirit, reasonable in application, and faithful to the deepest essence of what it means to be human. When our laws, institutions, customs, or daily conduct drift away from this standard, injustice takes root. It appears in systems that treat people as means rather than ends, in hierarchies that ignore dignity, and in habits that favor convenience over compassion. The gap between what is and what should be becomes the very measure of injustice. True justice, therefore, is the ongoing effort to close that gap, to shape both personal conduct and social structures so they reflect our highest understanding of humanity. It is not a fixed state but a continuous alignment with the humane, the rational, and the authentically human.
0:10

Injustice is the misalignment of human behavior and social order with norms that ought to be truly human — humane, reasonable, and faithful to their essence. : The author defines injustice not as a random misfortune or isolated act of cruelty, but as a fundamental misalignment between how humans actually behave and organize their societies and the norms that ought to govern them. Those norms must be truly human: humane in spirit, reasonable in application, and faithful to the deepest essence of what it means to be human. When our laws, institutions, customs, or daily conduct drift away from this standard, injustice takes root. It appears in systems that treat people as means rather than ends, in hierarchies that ignore dignity, and in habits that favor convenience over compassion. The gap between what is and what should be becomes the very measure of injustice. True justice, therefore, is the ongoing effort to close that gap, to shape both personal conduct and social structures so they reflect our highest understanding of humanity. It is not a fixed state but a continuous alignment with the humane, the rational, and the authentically human.

Zafar Mirzo | Quotes

1,624,313 views • 6 months ago

AI has had exactly two scaling axes that worked so far, and the second one is starting to look finite too the first one was pretraining: with scaling parameters and data, we got world knowledge (i.e. ChatGPT had read enough to know things), but it started saturating a while ago the second one was RL, and people had been doing RL the whole time before that: RLHF is RL but it never scaled far because it was trying to control the exact output, which tokens come out, how the text reads, but you can only push that so far before you’re just polishing RLVR dropped that constraint: giving the model a task, then checking whether the final answer is right, and ignoring everything in between -- so the model does whatever it wants in the middle and only the endpoint gets graded, and that’s much closer to actual RL and it’s what bought us planning and reasoning (arguably, tool use sits around 2.5 on this list -- while useful, it's not a different kind of thing) so one axis gave knowledge, the other gave reasoning, and both of them are one model working alone the next axis is how many models you can get working on the same problem, which is a different kind of axis than the previous two we know that multi-agent RL has always been the harder problem: I spent years in that literature and the gap between single-agent and multi-agent is definitely not incremental -- it’s a whole different class of difficulty! which is also why the derivatives are steep at the start, nobody has picked the easy wins yet... and the thing that gates this multi-agent coordination is communication: models can only coordinate as well as they can exchange information, and right now they do that by writing sentences to each other imagine what could we possibly achieve if we properly open that third axis development by letting models to exchange information in their native "language" without loosing any computational data that they produce during inference
0:14

AI has had exactly two scaling axes that worked so far, and the second one is starting to look finite too the first one was pretraining: with scaling parameters and data, we got world knowledge (i.e. ChatGPT had read enough to know things), but it started saturating a while ago the second one was RL, and people had been doing RL the whole time before that: RLHF is RL but it never scaled far because it was trying to control the exact output, which tokens come out, how the text reads, but you can only push that so far before you’re just polishing RLVR dropped that constraint: giving the model a task, then checking whether the final answer is right, and ignoring everything in between -- so the model does whatever it wants in the middle and only the endpoint gets graded, and that’s much closer to actual RL and it’s what bought us planning and reasoning (arguably, tool use sits around 2.5 on this list -- while useful, it's not a different kind of thing) so one axis gave knowledge, the other gave reasoning, and both of them are one model working alone the next axis is how many models you can get working on the same problem, which is a different kind of axis than the previous two we know that multi-agent RL has always been the harder problem: I spent years in that literature and the gap between single-agent and multi-agent is definitely not incremental -- it’s a whole different class of difficulty! which is also why the derivatives are steep at the start, nobody has picked the easy wins yet... and the thing that gates this multi-agent coordination is communication: models can only coordinate as well as they can exchange information, and right now they do that by writing sentences to each other imagine what could we possibly achieve if we properly open that third axis development by letting models to exchange information in their native "language" without loosing any computational data that they produce during inference

Sasha Malysheva

14,445 views • 1 month ago

I launched 300 Grok agents to automate the routine and see what would come of it. As a result, my business reached a profit of +356% Honestly, I still don't understand why absolutely everyone isn't using this method. While everyone else is writing posts and monitoring trends manually, this AI stack works like a well-oiled clockwork mechanism How it's structured: Dedicated cloud instances share the load via a single distributed bus, distributing tasks in batches of 50 parallel threads per agent The central configurator dynamically switches tasks between system workers, processing domains, parsing inputs, and gathering results into a single knowledge graph Each agent runs an algorithm through a fixed cycle (setup -> run -> correct -> save -> schedule), instantly correcting deviations and fixing data in the database Instead of spending hundreds of hours on manual operations, I handed all this machinery over to AI. The result speaks for itself, reach and productivity have grown exponentially, and the system operates autonomously 24/7 The visualization is made for a clear example and may differ from the actual work of the agents The article describes the detailed automation method, bookmark it
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I launched 300 Grok agents to automate the routine and see what would come of it. As a result, my business reached a profit of +356% Honestly, I still don't understand why absolutely everyone isn't using this method. While everyone else is writing posts and monitoring trends manually, this AI stack works like a well-oiled clockwork mechanism How it's structured: Dedicated cloud instances share the load via a single distributed bus, distributing tasks in batches of 50 parallel threads per agent The central configurator dynamically switches tasks between system workers, processing domains, parsing inputs, and gathering results into a single knowledge graph Each agent runs an algorithm through a fixed cycle (setup -> run -> correct -> save -> schedule), instantly correcting deviations and fixing data in the database Instead of spending hundreds of hours on manual operations, I handed all this machinery over to AI. The result speaks for itself, reach and productivity have grown exponentially, and the system operates autonomously 24/7 The visualization is made for a clear example and may differ from the actual work of the agents The article describes the detailed automation method, bookmark it

Bober_smart

14,733 views • 1 month ago

Part of the reason they wanted to develop reusable rockets that can land is the Geophysical Event. Part of the elite wants to build a data center and a small hub on the Moon to preserve AI and, with it, all human knowledge. They can control the conditions on the Moon more easily than on Earth, which will be far more active and unpredictable during a geomagnetic excursion and increased cosmic radiation. They would have no rivals on the Moon, nor would the lucky survivors on Earth pose a threat to them there. That's why the Moon is important. It was used in the past as well, which is why there are monolithic structures on the Moon.
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Part of the reason they wanted to develop reusable rockets that can land is the Geophysical Event. Part of the elite wants to build a data center and a small hub on the Moon to preserve AI and, with it, all human knowledge. They can control the conditions on the Moon more easily than on Earth, which will be far more active and unpredictable during a geomagnetic excursion and increased cosmic radiation. They would have no rivals on the Moon, nor would the lucky survivors on Earth pose a threat to them there. That's why the Moon is important. It was used in the past as well, which is why there are monolithic structures on the Moon.

Open Minded Approach

62,856 views • 1 month ago

Did you know that 5,000 years ago, during the Age of Taurus, Polaris was not the North Star? The North Star was Thuban, located in the tail of the constellation Draco! The stars hold the knowledge of timekeeping and axial precession. That is why, during the construction of the Hoover Dam, the elites left a monument marking the true time and era of this monumental structure. They know that civilization will be reset, but as long as the elites preserve the knowledge of timekeeping shared in the Book of Enoch, they and their bloodlines will remember the true history. We are about to enter the Age of Aquarius. A new order of the ages is upon us.
0:18

Did you know that 5,000 years ago, during the Age of Taurus, Polaris was not the North Star? The North Star was Thuban, located in the tail of the constellation Draco! The stars hold the knowledge of timekeeping and axial precession. That is why, during the construction of the Hoover Dam, the elites left a monument marking the true time and era of this monumental structure. They know that civilization will be reset, but as long as the elites preserve the knowledge of timekeeping shared in the Book of Enoch, they and their bloodlines will remember the true history. We are about to enter the Age of Aquarius. A new order of the ages is upon us.

Open Minded Approach

71,082 views • 1 month ago

M E S S I E R | OriginTrail Following the VirgoDAO investment in $TRAC, we are excited to announce the development of a strong strategic partnership with OriginTrail. 🤝 Starting today, $TRAC is listed and tradable on the P2P Exchange, allowing buyers and sellers to trade their tokens without slippage, token buy-and-sell taxes, or MEV-related losses at We believe in OriginTrail's Decentralized Knowledge Graph (#DKG), which integrates knowledge #graph and blockchain technologies to create AI-ready Knowledge Assets. We look forward to collaborating further on this in the future. These assets enhance trust, discoverability, and data ownership across industries such as supply chains, healthcare, and the metaverse, while supporting a verifiable internet for #AI.
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M E S S I E R | OriginTrail Following the VirgoDAO investment in $TRAC, we are excited to announce the development of a strong strategic partnership with OriginTrail. 🤝 Starting today, $TRAC is listed and tradable on the P2P Exchange, allowing buyers and sellers to trade their tokens without slippage, token buy-and-sell taxes, or MEV-related losses at We believe in OriginTrail's Decentralized Knowledge Graph (#DKG), which integrates knowledge #graph and blockchain technologies to create AI-ready Knowledge Assets. We look forward to collaborating further on this in the future. These assets enhance trust, discoverability, and data ownership across industries such as supply chains, healthcare, and the metaverse, while supporting a verifiable internet for #AI.

MESSIER | M87

29,594 views • 1 year ago

One of the most logical explanations of the Watchers in the Book of Enoch is that they were an elite group of survivors or a breakaway civilization that was not supposed to mix with ordinary survivors, but only to watch over the masses and intervene when necessary. Part of this breakaway civilization fell in love with the "daughters of men," or the daughters of the ordinary survivors. They had children with them and shared their technology. As a result, they were expelled from the elite breakaway civilization. This story appears in many civilizations, from the Bible to the Sumerian Anunnaki, the Greek myth of Prometheus, and Quetzalcoatl in Mesoamerica. This is, by far, the most logical explanation, but being logical does not mean it is the truth. I will share another theory tomorrow.
0:15

One of the most logical explanations of the Watchers in the Book of Enoch is that they were an elite group of survivors or a breakaway civilization that was not supposed to mix with ordinary survivors, but only to watch over the masses and intervene when necessary. Part of this breakaway civilization fell in love with the "daughters of men," or the daughters of the ordinary survivors. They had children with them and shared their technology. As a result, they were expelled from the elite breakaway civilization. This story appears in many civilizations, from the Bible to the Sumerian Anunnaki, the Greek myth of Prometheus, and Quetzalcoatl in Mesoamerica. This is, by far, the most logical explanation, but being logical does not mean it is the truth. I will share another theory tomorrow.

Open Minded Approach

65,822 views • 1 month ago

Watch the #bitcoin network in action. Each dot on the map represents a computer node, each holding a complete ledger of every transaction. This global network verifies the next block of transactions every ~10 minutes. The system ensures every transaction is recorded on a public ledger, accessible by anyone, yet secure and tamper-proof. As each new block is found, it's transmitted across the network, node by node, in real-time. This decentralized process eliminates the need for a central authority, highlighting Bitcoin's innovative approach to maintaining consensus and security. The nodes, distributed worldwide, are the backbone of Bitcoin's decentralized architecture. Watching them work together to verify transactions and add them to the blockchain provides a clear view of how the system operates efficiently and securely.
0:15

Watch the #bitcoin network in action. Each dot on the map represents a computer node, each holding a complete ledger of every transaction. This global network verifies the next block of transactions every ~10 minutes. The system ensures every transaction is recorded on a public ledger, accessible by anyone, yet secure and tamper-proof. As each new block is found, it's transmitted across the network, node by node, in real-time. This decentralized process eliminates the need for a central authority, highlighting Bitcoin's innovative approach to maintaining consensus and security. The nodes, distributed worldwide, are the backbone of Bitcoin's decentralized architecture. Watching them work together to verify transactions and add them to the blockchain provides a clear view of how the system operates efficiently and securely.

Documenting ₿itcoin 📄

67,838 views • 2 years ago

🚨BREAKING: Another ICE agent has been caught on video illegally pointing a firearm at a U.S. citizen, in Lemonwood, California. In the video, an unmarked ICE vehicle is stopped in the middle of the road… no vehicles are in front of it, and nothing is preventing them from driving forward. Instead of continuing to drive down the road, the ICE agent is blocking a pickup truck from turning, while pointing a gun, out their window, directly at the driver of that truck. The truck backs up, but the agent still keeps the firearm pointed at the driver. Only AFTER people begin honking their horns does the agent lower their weapon, and drive away. The law states that pointing a firearm at someone is considered a serious threat of deadly force. It is only justified when an officer has an objectively reasonable belief that they are facing an immediate threat of death, or serious bodily harm. It is not legally allowed to be used to control traffic, and it is not legally allowed to be used as intimidation. And that’s exactly why this video should be alarming to you. The agent is not boxed in… nothing is preventing them from driving down the street. Meanwhile, the agent is the one preventing the truck from continuing its turn. And they are doing so while pointing a gun at the driver. So, the question becomes… What immediate threat justified the ICE agent to stop their car, and point a firearm at a U.S. citizen? Because we are seeing a growing pattern, of publicly documented incidents, where ICE agents point firearms at legal observers, journalists, and bystanders during enforcement encounters… when they are not facing an immediate threat of death. That is not how public safety works. Pointing a firearm at someone is one of the most serious things an officer can do, because it instantly escalates an encounter into a potential deadly force situation. And that is exactly why the law is supposed to restrict it. Every unnecessary drawn gun increases the risk of a wrong judgment, and a fatal mistake. And when there is no accountability, for when that line gets crossed, drawing a gun because the normal for every situation. And when it becomes normal, more people’s lives are put in danger.
0:13

🚨BREAKING: Another ICE agent has been caught on video illegally pointing a firearm at a U.S. citizen, in Lemonwood, California. In the video, an unmarked ICE vehicle is stopped in the middle of the road… no vehicles are in front of it, and nothing is preventing them from driving forward. Instead of continuing to drive down the road, the ICE agent is blocking a pickup truck from turning, while pointing a gun, out their window, directly at the driver of that truck. The truck backs up, but the agent still keeps the firearm pointed at the driver. Only AFTER people begin honking their horns does the agent lower their weapon, and drive away. The law states that pointing a firearm at someone is considered a serious threat of deadly force. It is only justified when an officer has an objectively reasonable belief that they are facing an immediate threat of death, or serious bodily harm. It is not legally allowed to be used to control traffic, and it is not legally allowed to be used as intimidation. And that’s exactly why this video should be alarming to you. The agent is not boxed in… nothing is preventing them from driving down the street. Meanwhile, the agent is the one preventing the truck from continuing its turn. And they are doing so while pointing a gun at the driver. So, the question becomes… What immediate threat justified the ICE agent to stop their car, and point a firearm at a U.S. citizen? Because we are seeing a growing pattern, of publicly documented incidents, where ICE agents point firearms at legal observers, journalists, and bystanders during enforcement encounters… when they are not facing an immediate threat of death. That is not how public safety works. Pointing a firearm at someone is one of the most serious things an officer can do, because it instantly escalates an encounter into a potential deadly force situation. And that is exactly why the law is supposed to restrict it. Every unnecessary drawn gun increases the risk of a wrong judgment, and a fatal mistake. And when there is no accountability, for when that line gets crossed, drawing a gun because the normal for every situation. And when it becomes normal, more people’s lives are put in danger.

Jesus Freakin Congress

232,190 views • 3 months ago

Alright, now that we know *what* an agent is, how does it actually work? When you ask for help on a task, the agent plans a series of steps and executes them directly in the application on your behalf, using the tools it has access to. Say you are booking a local service or trying to organize your inbox (which typically takes multiple steps): the AI model first plans how to achieve the task using its existing knowledge and then interacts with your inbox to execute the task. The agent will continue until it is confident the task has been successfully completed.
0:15

Alright, now that we know *what* an agent is, how does it actually work? When you ask for help on a task, the agent plans a series of steps and executes them directly in the application on your behalf, using the tools it has access to. Say you are booking a local service or trying to organize your inbox (which typically takes multiple steps): the AI model first plans how to achieve the task using its existing knowledge and then interacts with your inbox to execute the task. The agent will continue until it is confident the task has been successfully completed.

Google AI

22,487 views • 10 months ago