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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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Bio Protocol

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14,761 просмотров • 1 год назад •via X (Twitter)

Наука и технологии
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Always, and especially today, the active work of enlighteners possessing extensive scientific and philosophical knowledge, whose calling is the enlightenment of rational, compassionate, and fruitful ethics, is very important. : The author emphasizes the critical role of enlighteners with deep scientific and philosophical knowledge, particularly today, in promoting rational, compassionate and fruitful ethics. Their active work is vital for guiding humanity toward enlightened values, enhancing collective wisdom and empathy. This perspective highlights the urgency of their calling to shape a humane, rational society. It stresses the need for knowledge driven leadership to navigate modern challenges, ensuring ethical progress. The call is for dedicated efforts to cultivate a thoughtful, compassionate ethos, securing a meaningful future for humanity.
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Always, and especially today, the active work of enlighteners possessing extensive scientific and philosophical knowledge, whose calling is the enlightenment of rational, compassionate, and fruitful ethics, is very important. : The author emphasizes the critical role of enlighteners with deep scientific and philosophical knowledge, particularly today, in promoting rational, compassionate and fruitful ethics. Their active work is vital for guiding humanity toward enlightened values, enhancing collective wisdom and empathy. This perspective highlights the urgency of their calling to shape a humane, rational society. It stresses the need for knowledge driven leadership to navigate modern challenges, ensuring ethical progress. The call is for dedicated efforts to cultivate a thoughtful, compassionate ethos, securing a meaningful future for humanity.

Zafar Mirzo | Quotes

175,283 просмотров • 1 год назад

The suppression of uncomfortable ideas may be common in religion or in politics, but it is not the path to knowledge, and there's no place for it in the endeavor of science.
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The suppression of uncomfortable ideas may be common in religion or in politics, but it is not the path to knowledge, and there's no place for it in the endeavor of science.

Prof. Carl Sagan

39,993 просмотров • 1 год назад

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

5,342,602 просмотров • 8 месяцев назад

Elon Musk "The suppression of uncomfortable ideas may be common in religion or in politics, but it is not the path to knowledge and there is no place for it in the endeavor of science." - Carl Sagan
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Elon Musk "The suppression of uncomfortable ideas may be common in religion or in politics, but it is not the path to knowledge and there is no place for it in the endeavor of science." - Carl Sagan

Oukham

68,783 просмотров • 2 лет назад

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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Sensitive content

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 просмотров • 11 месяцев назад

MAKING LOVE IS NOT THE SAME AS HAVING SEX. Sex can be a meeting of bodies. Making love is a meeting of souls that happens to use bodies as its language. One can be finished in minutes and leave both people emptier than they began. The other can last a lifetime in a single breath and rearrange the architecture of a person. In the mystical traditions, the body is not the obstacle to the sacred. It is the threshold. When two people approach each other without armor—without performance, without conquest, without the quiet transaction of validation—the ordinary act becomes a form of prayer. Attention turns into devotion. Breath synchronizes not by technique but by recognition. What passes between them is not merely pleasure; it is presence made tangible. Sex seeks an ending. Making love dissolves the one who was seeking. In that dissolution the boundary between giver and receiver softens, and for a little while the old story of separation loses its authority. This is why the mystics spoke of union in the language of lovers: not because the divine is romantic, but because only love is reckless enough to stop defending itself. The difference is not moral. It is ontological. One confirms the self. The other lets the self be rewritten by contact with what it cannot own.
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Sensitive content

MAKING LOVE IS NOT THE SAME AS HAVING SEX. Sex can be a meeting of bodies. Making love is a meeting of souls that happens to use bodies as its language. One can be finished in minutes and leave both people emptier than they began. The other can last a lifetime in a single breath and rearrange the architecture of a person. In the mystical traditions, the body is not the obstacle to the sacred. It is the threshold. When two people approach each other without armor—without performance, without conquest, without the quiet transaction of validation—the ordinary act becomes a form of prayer. Attention turns into devotion. Breath synchronizes not by technique but by recognition. What passes between them is not merely pleasure; it is presence made tangible. Sex seeks an ending. Making love dissolves the one who was seeking. In that dissolution the boundary between giver and receiver softens, and for a little while the old story of separation loses its authority. This is why the mystics spoke of union in the language of lovers: not because the divine is romantic, but because only love is reckless enough to stop defending itself. The difference is not moral. It is ontological. One confirms the self. The other lets the self be rewritten by contact with what it cannot own.

🧬Maxpein🧬

17,200 просмотров • 1 месяц назад

Science, on TRAC(k). Pandemic response depends on data held in many places at once: public health institutes, hospitals, ministries and research centers, each with its own systems and rules. AURORAI is a €6.9 million Horizon Europe project, running from September 2026 to 2030, with 12 organizations in nine European countries and a partner in Australia. It is building Europe's AI toolkit for pandemic preparedness, including for pathogens not yet identified. Its data backbone is the OriginTrail Decentralized Knowledge Graph (DKG). How that works without a central data lake: → Partners publish health and research data as Knowledge Assets, each with a Uniform Asset Locator (UAL) that proves who published it and that it hasn't been tampered with → The data stays where it is; Context Graphs control which regions see which metadata → That verified layer is the first input to AURORAI's decision support system, alongside pathogen models and population and mobility simulations When a model recommends an intervention, the inputs behind it can be traced to their source. In public health, that is what makes a recommendation defensible. Trust the source. More:
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Science, on TRAC(k). Pandemic response depends on data held in many places at once: public health institutes, hospitals, ministries and research centers, each with its own systems and rules. AURORAI is a €6.9 million Horizon Europe project, running from September 2026 to 2030, with 12 organizations in nine European countries and a partner in Australia. It is building Europe's AI toolkit for pandemic preparedness, including for pathogens not yet identified. Its data backbone is the OriginTrail Decentralized Knowledge Graph (DKG). How that works without a central data lake: → Partners publish health and research data as Knowledge Assets, each with a Uniform Asset Locator (UAL) that proves who published it and that it hasn't been tampered with → The data stays where it is; Context Graphs control which regions see which metadata → That verified layer is the first input to AURORAI's decision support system, alongside pathogen models and population and mobility simulations When a model recommends an intervention, the inputs behind it can be traced to their source. In public health, that is what makes a recommendation defensible. Trust the source. More:

OriginTrail

159,381 просмотров • 1 месяц назад

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

2,788,235 просмотров • 3 месяцев назад

The human brain is not the source of your thoughts; it's only the antenna receiving the message from your higher self. The conscious self is the observer in the material universe, receiving the knowledge of good and evil. The decision is already made before the person becomes consciously aware of making the decision. But the conscious self will have to bear all the emotions from the consequences of the decisions and the lessons required to learn the knowledge of good and evil.
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The human brain is not the source of your thoughts; it's only the antenna receiving the message from your higher self. The conscious self is the observer in the material universe, receiving the knowledge of good and evil. The decision is already made before the person becomes consciously aware of making the decision. But the conscious self will have to bear all the emotions from the consequences of the decisions and the lessons required to learn the knowledge of good and evil.

Open Minded Approach

24,369 просмотров • 5 месяцев назад

i don't f*cking understand why this isn't popular yet someone has created a memory system that uses 90% fewer tokens while still finding all the expected symbols it builds a local graph of source symbols and their relationships. architecture, decisions and handoffs live as Git-tracked Markdown. a note can point to the code behind it, so a code change can flag that knowledge for review the loop: scan the repo → map the code → write the why → anchor notes to symbols → retrieve a task-sized slice → review drift → commit to Git → continue in the next session in the author's small, one-repo test, graph retrieval returned 10.74× less context than grep top-3 while finding every expected symbol across six tasks the useful part is the connection between memory and evidence. the repo carries what the agent learned, and the code gives you a way to check whether that knowledge still holds save this, then build a second brain for your company⭣
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i don't f*cking understand why this isn't popular yet someone has created a memory system that uses 90% fewer tokens while still finding all the expected symbols it builds a local graph of source symbols and their relationships. architecture, decisions and handoffs live as Git-tracked Markdown. a note can point to the code behind it, so a code change can flag that knowledge for review the loop: scan the repo → map the code → write the why → anchor notes to symbols → retrieve a task-sized slice → review drift → commit to Git → continue in the next session in the author's small, one-repo test, graph retrieval returned 10.74× less context than grep top-3 while finding every expected symbol across six tasks the useful part is the connection between memory and evidence. the repo carries what the agent learned, and the code gives you a way to check whether that knowledge still holds save this, then build a second brain for your company⭣

beamnxw ./

33,354 просмотров • 1 месяц назад

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

561,483 просмотров • 1 год назад

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 просмотров • 6 месяцев назад

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 просмотров • 2 месяцев назад

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.
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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

2,209,234 просмотров • 8 месяцев назад

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
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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

15,199 просмотров • 3 месяцев назад

Make any knowledge base or LLM wiki follow the Open Knowledge Format with our new OKF plugin. OKF is an open spec created by Google that makes markdown-based knowledge bases portable and interoperable through a set of shared patterns and rules. With the new plugin, you can easily convert any existing knowledge base to OKF, and have agents write and maintain your wiki in the format. The plugin leverages a combination of linting, deterministic rules, and automations for creating special files like 𝚒𝚗𝚍𝚎𝚡.𝚖𝚍. It also adds a skill and leverages our MCP tools to give agents real-time hints and warnings as they write, so issues get caught immediately — plus 𝚊𝚞𝚍𝚒𝚝 and 𝚕𝚒𝚗𝚝 tools to check the entire knowledge base any time.
0:10

Make any knowledge base or LLM wiki follow the Open Knowledge Format with our new OKF plugin. OKF is an open spec created by Google that makes markdown-based knowledge bases portable and interoperable through a set of shared patterns and rules. With the new plugin, you can easily convert any existing knowledge base to OKF, and have agents write and maintain your wiki in the format. The plugin leverages a combination of linting, deterministic rules, and automations for creating special files like 𝚒𝚗𝚍𝚎𝚡.𝚖𝚍. It also adds a skill and leverages our MCP tools to give agents real-time hints and warnings as they write, so issues get caught immediately — plus 𝚊𝚞𝚍𝚒𝚝 and 𝚕𝚒𝚗𝚝 tools to check the entire knowledge base any time.

OpenKnowledge

139,843 просмотров • 2 месяцев назад

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
0:10

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 просмотров • 2 месяцев назад

In the Book of Enoch, the Watchers Baraqiel and Kokabiel were punished for teaching humanity the knowledge of astrology, astronomy, and reading celestial signs. Yet Enoch was taught by the angel Uriel how to track the celestial bodies and time. Enoch also shares knowledge of how to recognize the time of judgment. What is the difference between the two?
0:14

In the Book of Enoch, the Watchers Baraqiel and Kokabiel were punished for teaching humanity the knowledge of astrology, astronomy, and reading celestial signs. Yet Enoch was taught by the angel Uriel how to track the celestial bodies and time. Enoch also shares knowledge of how to recognize the time of judgment. What is the difference between the two?

Open Minded Approach

85,341 просмотров • 2 месяцев назад