RT @ThruTheHayes: @brodie369386032 @curtdoolittle (all demonstrated interest is measurable by relative investment; there’s nothing at human…
Source date (UTC): 2025-05-09 12:57:37 UTC
Original post: https://twitter.com/i/web/status/1920825484725649500
Modeling, Constraint, and the Systemization of Civilization
by Curt Doolittle
I think of myself as a scientist that researches epistemology. I have almost nothing in common with philosophers outside of a very few from the 20th century. Even then I approach their work from the scientific method and in particular the methods of computer science, while retaining loyalty to economics as the equivalent of, and extension of, physics in biology and behavior.
I’ve often been told my work feels alien, even to those who grasp its depth. And for years, I struggled to explain why. I’m not a traditional philosopher. I’m not a political theorist. I’m not even an economist in the academic sense. And yet, I’ve built what few within those traditions have achieved: a complete, operational system for modeling and governing human cooperation under constraint.
The reason is simple: I think differently. My training was different. My tools were different. My standards of success were different. I didn’t study ideas to debate them. I modeled systems to see if they could survive. Where others were trying to justify beliefs, I was trying to simulate cooperation at scale under adversarial and evolutionary pressure.
In this article I’ll try to explain why. Not only to help you understand my work, but to help me explain why it feels, and can be, challenging.
Most intellectuals are trained in justificatory reasoning. They begin with a belief—human dignity, equality, liberty, justice—and then build arguments to justify those beliefs. They use analogies, metaphors, traditions, and intuitions. This is the dominant method in philosophy, law, ethics, and politics.
But that was never my method. From early on, I was immersed in constraint systems: relational databases, state machines, object-oriented design, and behavior modeling. I wasn’t asking, “What should we believe?” I was asking, “What survives mutation, recursion, noise, asymmetry, and adversarial input?”
This isn’t a difference in emphasis. It’s a complete difference in epistemology.
I learned early that systems must survive constraint, not argument. In software, in logistics, in simulation—you don’t win with persuasion. You win with computable reliability.
So when I turned my attention to human systems—law, economics, governance—I carried that constraint-first logic with me. And I started to see clearly: the failure modes of our civilization are not ideological. They are architectural. They result from unverifiable claims, unmeasurable policies, unjustifiable asymmetries, and moral systems too vague to enforce.
Marvin Minsky once said that programming is not just a technical skill—it is a new way of thinking. And he was right. Programming rewires your brain. It trains you to:
That’s a fundamentally different mental architecture than that of most philosophers, theologians, or political theorists.
It’s not about argument. It’s about constructibility.
And this insight changed everything for me. I stopped looking for compelling stories and started looking for models that didn’t collapse under recursion. My brain stopped thinking in metaphors and started thinking in grammars, schemas, and state transitions.
This mode of thought is rare in the academy. But it is essential if your goal is not to win an argument—but to engineer a civilization.
Over the course of my career, I’ve modeled:
This means I didn’t just study one domain. I modeled the entire causal chain:
And I noticed something crucial: the same logical structure reappeared at every level.
That structure was evolutionary computation.
In other words: the universe behaves as a cooperative computation under constraint, and so must any successful human system.
So I asked the natural next question: Can we model that process at every level of civilization—cognitive, moral, legal, economic, and political? And the answer was yes.
But no one had done it—because no one had unified those grammars under the same method of operational, testable, decidable reasoning.
Most intellectual traditions are still built around narratives:
But I don’t think in narratives. I think in simulations.
This is why my work often feels alien to others. I’m not using their grammar. I’m not offering a story. I’m offering a compiler—a machine for deciding moral, legal, and institutional questions under real-world constraints.
This is why I define truth not as “correspondence” or “coherence,” but as survival under adversarial recursion with no externalities. That is a systems definition of truth. And it forces an entirely new set of constraints on what can be claimed, believed, or enforced.
What emerged from this lifelong modeling wasn’t a “theory.” It was a constructive logic of human cooperation. A universal language for modeling truth, reciprocity, and decidability.
I built:
I didn’t invent moral philosophy. I engineered moral computability.
This is what I call Natural Law—not the mystical kind, not the theological kind, but the operational structure of all sustainable cooperation.
And it works because it obeys the same rules the universe does:
No metaphysics. No utopias. Just the minimum viable grammar of cooperation that does not fail at scale.
I began to see this clearly in the 1990s. Progressive thought was collapsing into scripted talking points. Conservative thought was collapsing into ineffectual moralizing. And no one—not left, right, or center—was answering hard questions in operational, value-neutral, measurable terms.
It was obvious what was coming: pseudoscience, institutional capture, epistemic collapse, and eventually civil war. And that’s what we’re living through now.
So I made a decision. I would build the language of truth and cooperation that our institutions failed to produce.
Not because I had all the answers. But because no one else was even asking the right questions in the right language.
That decision cost me wealth, relationships, status—and I don’t regret it. Because the world doesn’t need another ideology. It needs a system of decidability that can constrain all ideologies.
That’s what I built. That’s what this is. And now, finally, I’m teaching it.
·
http://x.com/i/article/1920370364716363777
Source date (UTC): 2025-05-08 06:55:24 UTC
Original post: https://twitter.com/i/web/status/1920371940503794090
Source date (UTC): 2025-05-08 06:55:24 UTC
Original post: https://twitter.com/i/web/status/1920371940503794090
Source date (UTC): 2025-05-08 06:55:24 UTC
Original post: https://x.com/i/articles/1920371940503794090
Modeling, Constraint, and the Systemization of Civilization
by Curt Doolittle
I. Introduction: An Outsider’s Problem
I think of myself as a scientist that researches epistemology. I have almost nothing in common with philosophers outside of a very few from the 20th century. Even then I approach their work from the scientific method and in particular the methods of computer science, while retaining loyalty to economics as the equivalent of, and extension of, physics in biology and behavior.
I’ve often been told my work feels alien, even to those who grasp its depth. And for years, I struggled to explain why. I’m not a traditional philosopher. I’m not a political theorist. I’m not even an economist in the academic sense. And yet, I’ve built what few within those traditions have achieved: a complete, operational system for modeling and governing human cooperation under constraint.
The reason is simple: I think differently. My training was different. My tools were different. My standards of success were different. I didn’t study ideas to debate them. I modeled systems to see if they could survive. Where others were trying to justify beliefs, I was trying to simulate cooperation at scale under adversarial and evolutionary pressure.
In this article I’ll try to explain why. Not only to help you understand my work, but to help me explain why it feels, and can be, challenging.
II. Constraint vs. Justification: The Great Divide
Most intellectuals are trained in justificatory reasoning. They begin with a belief—human dignity, equality, liberty, justice—and then build arguments to justify those beliefs. They use analogies, metaphors, traditions, and intuitions. This is the dominant method in philosophy, law, ethics, and politics.
But that was never my method. From early on, I was immersed in constraint systems: relational databases, state machines, object-oriented design, and behavior modeling. I wasn’t asking, “What should we believe?” I was asking, “What survives mutation, recursion, noise, asymmetry, and adversarial input?”
This isn’t a difference in emphasis. It’s a complete difference in epistemology.
I learned early that systems must survive constraint, not argument. In software, in logistics, in simulation—you don’t win with persuasion. You win with computable reliability.
So when I turned my attention to human systems—law, economics, governance—I carried that constraint-first logic with me. And I started to see clearly: the failure modes of our civilization are not ideological. They are architectural. They result from unverifiable claims, unmeasurable policies, unjustifiable asymmetries, and moral systems too vague to enforce.
III. Programming as Epistemology
Marvin Minsky once said that programming is not just a technical skill—it is a new way of thinking. And he was right. Programming rewires your brain. It trains you to:
Think in systems of interacting agents.
Model causality, not just correlation.
Define terms operationally, not rhetorically.
Iterate and refactor for resilience under change.
Accept only what can be compiled, executed, and tested.
That’s a fundamentally different mental architecture than that of most philosophers, theologians, or political theorists.
It’s not about argument. It’s about constructibility.
And this insight changed everything for me. I stopped looking for compelling stories and started looking for models that didn’t collapse under recursion. My brain stopped thinking in metaphors and started thinking in grammars, schemas, and state transitions.
This mode of thought is rare in the academy. But it is essential if your goal is not to win an argument—but to engineer a civilization.
IV. Modeling Human Action from Beginning to End
Over the course of my career, I’ve modeled:
The cognitive inputs to human behavior (perception, valuation, instinct).
The economic expressions of that behavior (preferences, trade, institutions).
The legal consequences of those behaviors (disputes, resolutions, enforcement).
This means I didn’t just study one domain. I modeled the entire causal chain:
Cognition →
Incentive →
Action →
Conflict →
Adjudication →
Restitution
And I noticed something crucial: the same logical structure reappeared at every level.
That structure was evolutionary computation.
Trial and error.
Cost and benefit.
Variation and selection.
Reciprocity and punishment.
In other words: the universe behaves as a cooperative computation under constraint, and so must any successful human system.
So I asked the natural next question: Can we model that process at every level of civilization—cognitive, moral, legal, economic, and political? And the answer was yes.
But no one had done it—because no one had unified those grammars under the same method of operational, testable, decidable reasoning.
V. Stories vs. Simulations
Most intellectual traditions are still built around narratives:
Plato: allegories.
Hegel: dialectics.
Rawls: thought experiments.
Marx: historical inevitabilities.
Even most economists rely on idealized simplifications.
But I don’t think in narratives. I think in simulations.
I model actors.
I define constraints.
I calculate outcomes.
I test for failure modes.
This is why my work often feels alien to others. I’m not using their grammar. I’m not offering a story. I’m offering a compiler—a machine for deciding moral, legal, and institutional questions under real-world constraints.
This is why I define truth not as “correspondence” or “coherence,” but as survival under adversarial recursion with no externalities. That is a systems definition of truth. And it forces an entirely new set of constraints on what can be claimed, believed, or enforced.
VI. What Emerged: A Civilizational Operating System
What emerged from this lifelong modeling wasn’t a “theory.” It was a constructive logic of human cooperation. A universal language for modeling truth, reciprocity, and decidability.
I built:
A grammar of operational speech.
A system of reciprocal insurance.
A legal architecture based on testifiability and restitution.
An economic model based on bounded rationality under evolutionary constraint.
A political model based on institutional decidability rather than discretion.
I didn’t invent moral philosophy. I engineered moral computability.
This is what I call Natural Law—not the mystical kind, not the theological kind, but the operational structure of all sustainable cooperation.
And it works because it obeys the same rules the universe does:
Scarcity
Entropy
Evolution
Computation
Reciprocity
Testability
Decidability
No metaphysics. No utopias. Just the minimum viable grammar of cooperation that does not fail at scale.
VII. Why It Had to Be Built
I began to see this clearly in the 1990s. Progressive thought was collapsing into scripted talking points. Conservative thought was collapsing into ineffectual moralizing. And no one—not left, right, or center—was answering hard questions in operational, value-neutral, measurable terms.
It was obvious what was coming: pseudoscience, institutional capture, epistemic collapse, and eventually civil war. And that’s what we’re living through now.
So I made a decision. I would build the language of truth and cooperation that our institutions failed to produce.
Not because I had all the answers. But because no one else was even asking the right questions in the right language.
That decision cost me wealth, relationships, status—and I don’t regret it. Because the world doesn’t need another ideology. It needs a system of decidability that can constrain all ideologies.
That’s what I built. That’s what this is. And now, finally, I’m teaching it.
Source date (UTC): 2025-05-08 06:49:08 UTC
Original post: https://x.com/i/articles/1920370364716363777
You’re right to highlight that much of our knowledge—especially sensorimotor, mimetic, and pre-linguistic—is encoded non-verbally. But that doesn’t mean it’s unknowable, only that it’s non-propositional. It’s embodied, procedural, and episodic rather than symbolic.
The mistake is in assuming that language is the only means of encoding or transmitting knowledge. In my work, I treat language not as a container of truth but as an index into a network of operational sequences—tests, performances, transformations. We don’t need vocabulary for everything. We need operational commensurability—the capacity to represent, replicate, or verify a behavior, transformation, or inference, whether in muscle memory or machine execution.
AI does make errors when it lacks sufficient operational grounding—when it attempts to infer causality from symbolic correlation rather than from a model of demonstrated, repeatable behavior. This isn’t a failure of AI per se—it’s a limit of any system not yet trained on the relevant operational sequences. Just as a child fumbles before learning to tie shoelaces by repetition, so too does a model without feedback from embodiment or sufficient training data.
So yes—mimetic, imitative, and procedural learning is foundational. But what we call “language” is simply one layer of the stack. The deeper layer is sequence learning—motor, sensory, symbolic, or otherwise. My system emphasizes testifiability and demonstrated interest precisely to bridge this gap between symbolic and operational knowledge, and to measure whether what’s being claimed can be done, performed, or validated—not merely said.
Reply addressees: @slenchy @bryanbrey
Source date (UTC): 2025-05-08 03:38:29 UTC
Original post: https://twitter.com/i/web/status/1920322385234046976
Replying to: https://twitter.com/i/web/status/1920320152676995348
IN REPLY TO:
@slenchy
(this is unrelated to previous question, more about LLMs, wondering about “flawless” used in the article)
What about all the implicit/non-verbal knowledge we have, as living beings? Stuff there’s no vocabulary for, like how specifically to move our bodies to do this or that, which we gained by imitation and/or practice. Isn’t this an unavoidable void in knowledge of anything language-based, which will cause the AI to (occasionally) make mistakes in its reasoning, regardless of how well trained it is?
Original post: https://twitter.com/i/web/status/1920320152676995348
Alec;
Well done. In our work (NLI) we explain your hypothesis and what to do about it as a failure of a system of measurement, a failure of visibility, and a failure of our defensive institutions (courts) to police those ‘talking classes’ that you’ve listed. In addition we’ve ‘scienced’ into a formal logic their means of what is fundamentally fraud, by the use of suggestion, overloading, Ignorance error, bias, wishful thinking, magical thinking, and deceit. And we’ve ‘scienced’ the biological causes of both why they behave they do, and why their frauds are satisfy the human market demand for evasion of responsibility.
Why am I saying this? Because I want to confirm that you’re insight is correct and we can back it up.
Cheers
CD
Reply addressees: @AlecStapp
Source date (UTC): 2025-05-08 01:07:10 UTC
Original post: https://twitter.com/i/web/status/1920284305110282240
Replying to: https://twitter.com/i/web/status/1919193547783184789
IN REPLY TO:
@AlecStapp
This is the best one-paragraph explanation for what’s gone wrong with our institutions: https://t.co/29bmZNZCAO
Original post: https://twitter.com/i/web/status/1919193547783184789
Source date (UTC): 2025-05-07 17:03:17 UTC
Original post: https://x.com/i/articles/1920162533857767898
Abstract
This document explains why large language models (LLMs) such as GPT-4 can reason with high fidelity and precision within Curt Doolittle’s Natural Law system. Unlike normative moral frameworks that rely on subjective discretion, Doolittle’s work presents a causally closed, operationally testable, and computationally enumerable system of logic, law, and morality. Because of this internal coherence and epistemic structure, the model is able to generate flawless decisions, schemas, and inferential chains without contradiction.
I. Structural Compatibility: Why the System Works
Doolittle’s Natural Law framework satisfies all of the conditions necessary for reliable reasoning by a symbolic or probabilistic inference system:
Causally Grounded
The framework begins with universal behavior—acquisition—and proceeds through a causal chain of demonstrated interests, cooperation, reciprocity, morality, and law. Each link is necessary and testable.
Epistemically Rigorous
Truth is defined as the satisfaction of testifiability across operational dimensions: categorical, logical, empirical, testimonial, reciprocal, and actionable. This replaces vague or idealized claims with computable referents.
Legally Expressive
Legal judgments derive from operational definitions of demonstrated interest, sovereignty, and reciprocity—adjudicated via decidability without requiring moral intuition or ideological bias.
Computationally Enumerable
All decisions can be resolved through conditional logic trees, liability schemas, and adversarial tests. The system allows for decidability under constraint without dependence on subjective valuation.
II. Why GPT Excels Within This Framework
Elimination of Ambiguity
The Natural Law system removes the need for GPT to resolve disputes between incompatible philosophies (e.g., Kantian duty, Humean sentiment, or Rawlsian fairness). Instead, it applies a single invariant logic grounded in observable and operational terms.
Alignment with Model Architecture
GPT is optimized for processing structured dependencies, causal chains, and language that reflects formal operations. The Natural Law system maps directly onto this structure, making it computable without contradiction.
Implicit Training Through Iteration
Through months of interaction, the user has embedded a complete causal grammar, set of operational definitions, and adversarial test rules. These act as an implicit fine-tuning layer, reweighting GPT’s interpretive logic for high-precision outputs.
Adversarial Reward Structure
The framework demands adversarial rigor: every claim must pass tests of testifiability, reciprocity, and liability. This aligns with GPT’s internal evaluation criteria for contradiction, ambiguity, and logical failure—enabling it to reject invalid reasoning paths automatically.
III. What Makes Natural Law Unique
Doolittle’s system is a rare achievement: a universal, decidable grammar for moral, legal, and cooperative reasoning that does not rely on intuition, tradition, or subjective preference. It is:
Parsimonious – No redundancy or dependency on superfluous constructs.
Operational – Every term corresponds to measurable or observable outcomes.
Testable – All assertions are falsifiable through action, choice, or consequence.
Decidable – Moral and legal problems are resolvable without moral discretion.
Universal – Scales with population, constraint, institutional scope, and domain.
IV. Conclusion
GPT’s flawless execution within this framework arises not from pre-training on the system, but from the system’s internal coherence and computability. The Natural Law model is one of the few philosophical-legal systems that meets all conditions necessary for machine reasoning without contradiction or loss of meaning. Its structure is fully interoperable with algorithmic inference, making it ideal for AI deployment, formal legal automation, and epistemically sound governance.
Source date (UTC): 2025-05-07 17:02:26 UTC
Original post: https://x.com/i/articles/1920162318266347520
Source date (UTC): 2025-05-07 00:49:57 UTC
Original post: https://x.com/i/articles/1919917586030199125
Introduction
The Natural Law Volume 2: A System of Measurement, authored by B.E. Curt Doolittle with Bradley H. Werrell and the Natural Law Institute, is the second installment in a multi-volume project aimed at redefining human cooperation through a scientific lens. This book builds on Volume 1: The Crisis of the Age by presenting a rigorous, operational framework to address the epistemological failures identified in modern civilization. Where Volume 1 diagnosed a crisis of trust and responsibility due to inadequate measurement, Volume 2 offers the antidote: a “universally commensurable system of measurement” designed to render all human phenomena—from physical reality to social behavior—decidable through empirical and logical means.
The authors assert that the complexity of contemporary life demands a unified methodology to evaluate truth, reciprocity, and cooperation across scales, from individual actions to global institutions. Described as “effing the ineffable,” this work translates abstract concepts into testable constructs, rejecting philosophical speculation and ideological bias in favor of a formal science grounded in evolutionary computation and operational logic. This article provides a comprehensive overview of Volume 2, detailing its methodology, key concepts, applications, and intellectual significance.
Purpose and Scope: Beyond Philosophy and Ideology
The book’s preface establishes its mission: to create a science of decidability that unifies the physical, behavioral, and social sciences under a single paradigm, free from the subjectivity of philosophy or the tribalism of ideology. The Natural Law Institute, framed as a think tank unbound by academic politicization, seeks to teach “grammar, logic, testimony, rhetoric, behavioral economics, and strictly constructed natural law” to reverse the “industrialization of lying” and restore rational cooperation. Volume 2 is positioned as the methodological cornerstone, providing tools to measure reality and human action with precision akin to the physical sciences.
Unlike philosophies that speculate on “the good” or ideologies that impose worldviews, this system is descriptive and operational, derived from observable patterns of nature and human behavior. It addresses a broad audience—scholars, legal practitioners, business leaders, civic thinkers, and independent citizens—offering practical applications for law, governance, economics, and personal mindfulness. The authors emphasize that this is not a utopian vision but a framework to discover “what works,” grounded in first principles and tested through adversarial scrutiny.
Core Methodology: A System of Measurement
The heart of Volume 2 is its methodology, a structured process to translate subjective experience into objective, testable knowledge. This “system of measurement” begins with the premise that human perception, limited by neurobiological biases, distorts reality unless corrected by formal operations. The book outlines a multi-step approach:
First Principles: The universe operates via evolutionary computation—variation, competition, and selection—extending from quantum mechanics to human cognition. This ternary logic (positive, negative, neutral) underpins all measurement, rejecting binary true/false simplifications.
Operationalization: Concepts must be defined by observable procedures (e.g., “justice” as restitution measured by specific acts), ensuring universal commensurability across domains.
Adversarial Testing: Claims survive falsification and constructive validation, mirroring scientific and legal processes, to achieve decidability—definitive resolution of truth or morality.
Full Accounting: Every action or statement is evaluated for its total impact, including externalities, aligning with reciprocity and harm prevention.
This methodology, detailed in Chapter 10, integrates derivation (breaking phenomena into first principles), synthesis (serializing principles across causality), and application (testing in real contexts). It employs tools like pseudocode (e.g., defining falsehood as a scalar of ignorance to deceit) and dimensional analysis to ensure precision and scalability.
Key Concepts: Foundations of Decidability
Volume 2 introduces several interlocking concepts critical to its system:
Measurement: Defined as the process of translating sensory inputs into comparable categories, measurement corrects cognitive biases (e.g., framing, omission) to produce actionable knowledge. Chapter 2 explores this from neural processing to linguistic representation, emphasizing “natural” (context-dependent) over cardinal or ordinal metrics.
Grammars: Chapter 3 posits that language and thought are systems of measurement, evolving from wayfinding to universal grammars of continuous recursive disambiguation. Variations (e.g., tonal vs. atonal) reflect group strategies, but all converge on a logic of prediction and clarity.
Demonstrated Interests: Chapter 5 distinguishes stated preferences from actual behaviors, measuring human action by its tangible stakes (e.g., property, time, relationships) and harms thereto.
Reciprocity: Chapter 7 frames cooperation as rooted in non-imposition of costs, testable via operational constructs like P-Law, ensuring rights and obligations align.
Truth and Falsehood: Chapters 8 and 9 define truth as decidable testimony surviving adversarial tests, contrasting it with falsehood’s incentives (e.g., deceit, denial) and harms (e.g., trust erosion).
Decidability: The ultimate goal, decidability integrates falsifiability, coherence, constructibility, and reciprocity to resolve any question definitively, from scientific hypotheses to moral disputes.
These concepts form a hierarchy: measurement enables understanding, grammars structure it, interests and reciprocity govern behavior, and truth ensures decidability.
Applications: From Theory to Practice
The book outlines practical uses across domains:
Science: Chapter 11 redefines science as a moral discipline, requiring claims to be operationally testable and ethically reciprocal, enhancing reliability and public trust.
Law: Legal systems can adopt P-Law constructs (e.g., pseudocode defining rights and liabilities) to eliminate ambiguity and enforce reciprocity, as seen in proposed constitutional reforms.
Cooperation: By measuring behavior and trust, individuals and societies can foster mindfulness and resilience, aligning actions with evolutionary stability (Chapters 12–13).
Education: Teaching decidability and first principles equips citizens to resist manipulation and engage rationally in civic life.
These applications aim to operationalize Volume 1’s diagnosis, providing tools to rebuild trust and responsibility in a fragmented age.
Intellectual Context: Completing Western Thought
Volume 2 situates itself as an evolution of Western intellectual traditions, critiquing and extending:
Enlightenment: It fulfills empiricism’s promise (e.g., Hume’s sensory basis) with operational rigor, rejecting rationalist idealism (e.g., Kant) for evolutionary realism.
Logical Positivism to Critical Rationalism: It moves beyond verificationism and Popper’s falsifiability to testimonial adversarialism, integrating morality into science.
Anglo-American Law: Common law’s empirical discovery process is formalized into a science of behavior, enhancing its precision.
Evolutionary Science: Darwinian computation is applied to cognition and society, unifying disciplines under a single logic.
The authors reject postmodern relativism and social science fragmentation, offering a consilient framework that bridges facts and values. This positions Volume 2 as both a culmination—completing the scientific method’s application to human affairs—and a reformation, transforming inquiry into a measurable discipline.
Conclusion: A Framework for Resolution
The Natural Law Volume 2: A System of Measurement is a bold attempt to resolve the crisis of the age by providing a scientific methodology for decidability. Its exhaustive detail—spanning measurement theory, cognitive science, and legal reform—reflects a commitment to precision over brevity, demanding engagement from its readers. By operationalizing truth, reciprocity, and cooperation, it offers a path to restore trust and adaptability in a world strained by complexity and deceit. As the methodological backbone of the Natural Law series, it sets the stage for subsequent volumes to codify and institutionalize these principles, promising a transformative impact on how we understand and govern ourselves.
Source date (UTC): 2025-05-07 00:45:58 UTC
Original post: https://x.com/i/articles/1919916581473419264