Join me today for Episode 1082 of Bitcoin And . . .
Topics for today:
liminal: [email protected]
Laeserin: [email protected]
Project Alexandria: https://next-alexandria.gitcitadel.eu/
Episode 937 of Bitcoin And liminal: https://fountain.fm/episode/vvZkH5FKqYVuyFAP312V
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Topics for today:
- liminal's Article: Understanding Knowledge
- nostr's Ecosystem
- Zettlekasten Note Taking
- LLM's Role in Deleting Relational Noise
Resources:
Understanding Knowledge https://next-alexandria.gitcitadel.eu/publication?d=understanding-knowledgeliminal: [email protected]
Laeserin: [email protected]
Project Alexandria: https://next-alexandria.gitcitadel.eu/
Episode 937 of Bitcoin And liminal: https://fountain.fm/episode/vvZkH5FKqYVuyFAP312V
Find the Bitcoin And Podcast on every podcast app here
https://episodes.fm/1438789088
Find the Bitcoin And Podcast on every podcast app here:
https://episodes.fm/1438789088
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[00:00:13]
David Bennett:
It is 09:57AM Pacific Daylight Time. It is the April 2025. This is episode ten eighty two of Bitcoin End. It's not a news day. It's a think day. Today, I'm reading a piece called Understanding Knowledge from my friend, Liminal. And before you skip ahead, let me tell you, this ain't just academic naval gazing. This one's got roots. It ties straight into how we handle knowledge in a decentralized world. Nostra, zettelkasten, mycelial structures, the whole nine. We're talking about what it means to know something in a world where knowledge doesn't live on university shelves anymore. It moves. It forks. It dies. It spawns. It's organic. And sometimes, it changes form.
The network matters more than the node. So if you're into stuff like how information spreads across Nostr, how your zettelkasten node system mimics forest fungi, or how the collapse of centralized institutions might not be a bug, but a feature, buckle up. It's Bitcoin and knowledge. Let's go. Introduction. Knowledge as a living ecosystem. Why Investigate the Nature of Knowledge? Understanding the nature of knowledge itself is fundamental, distinct from simply studying how we learn or communicate. Knowledge exists first as representations within individuals, separate from how we interact with it.
Consider a physicist's understanding of quantum mechanics, the complete interconnected web of concepts in their mind, exists differently from how they might explain it in a lecture, write it in a textbook, or summarize it in a tweet. When knowledge must be shared, it undergoes transformative process, moving from its represented form within a dynamically changing individual to something that could be transmitted. This transmission demands not just translation into words, but adaptation to specific mediums, each shaping how that knowledge is ultimately conveyed. As technology advances, developing systems that more closely mirror knowledge's true form becomes crucial.
Not just to improve the retention and navigation, but to enable more effective critical examination and connection of ideas across existing bodies of knowledge. Traditional approaches that ignore this fundamental nature of knowledge inevitably create artificial constraints on how we understand, share, and build upon human understanding. Challenging the static perception of knowledge. Traditionally, knowledge has been perceived as a static repository, a meticulously cataloged library, or a readily queryable database. This conventional viewpoint treats knowledge as a collection of discrete facts and information that can be stored, retrieved, and utilized in a linear fashion.
However, this perspective may oversimplify the complexity and fluidity inherent in how knowledge is generated, shared, and evolved. Viewing knowledge through this static lens risks neglecting the intricate interactions and continuous evolution that characterize our collective understanding. Reimagining knowledge as a living ecosystem. Contrary to the static model, this essay proposes a reimagining of knowledge as a living process. In this framework, knowledge is seen as dynamic and ever evolving, much like a natural ecosystem that adapts, grows, and thrives through complex interactions and relationships.
This biological perspective highlights several key characteristics. Resilience, the ability to maintain integrity while adapting to new information. Interconnectedness, the organic formation of relationships between ideas. Higher order interactions, the capacity to be built with other sets of ideas. Adaptability, the capacity to evolve in response to new contexts and needs. In conceptualizing knowledge in this manner, we acknowledge its capacity for growth and transformation, mirroring the resilience and adaptability found in living systems.
This approach not only provides a more accurate representation of how knowledge functions, but also offers practical insights for designing more effective and responsive knowledge systems. The four perspectives. One, the building blocks. Material cause. Just as living organisms are made up of cells, knowledge systems are built from fundamental units of understanding. These range from simple concepts to complex theories, each capable of standing alone while contributing to larger structures. Think of them as knowledge cells, Self contained, but ready to connect and combine in meaningful ways.
Two. The pattern of organization. Formal cause. If you've ever seen how mushrooms connect through underground networks, you'll have a good picture of how knowledge naturally organizes itself. Rather than fitting into neat hierarchical boxes, knowledge forms web like connections in all directions. Modern digital systems are finally catching up to this natural way of organizing information, moving beyond rigid folder structures to more fluid, interconnected approaches. Three. The forces of change. Efficient cause. Knowledge grows and changes through a complex dance of individual insights, community discussions, technological tools, and cultural developments.
Like an ecosystem, it evolves through the interaction of many forces. New tools, like Nostr, are making these natural processes more visible and more powerful, allowing knowledge to evolve more organically than ever before. Four, the purpose and direction. Final cause. Why does knowledge exist? Not just to sit there looking impressive, but to help us understand our world and make better decisions. This purpose shapes how knowledge systems develop and organize themselves. When we design knowledge tools and platforms, we need to keep this ultimate purpose in mind.
Why this matters. Understanding knowledge as a living system isn't just a nice metaphor. It has practical implications for how we create and share information, build knowledge management tools, design educational systems, approach research and discovery. When we work with knowledge's living nature rather than against it, we can create more effective ways to learn, share, and grow our collective understanding. Looking forward. Continuing to explore this perspective, we'll dive deeper into each of these aspects, examining how they play out in practical situations and how we can better align our knowledge tools and practices with the natural dynamics of living systems.
This journey will help us see knowledge not as a static resource to be stored, but as a living ecosystem to be nurtured and grown. Whether you're a student, researcher, teacher, or knowledge worker, this perspective can change how you interact with and contribute to our collective understanding. Section one, material cause, the substance of knowledge. Knowledge itself as an entity is composed of fundamental building blocks that form the basis of our understanding of the world. These knowledge cells combine and interact to create complex structures of meaning and insight in response to new stimuli and insights born from observations and existing knowledge.
To truly comprehend the material cause of knowledge, we must dive into the interplay between these basic components and the systems they engender. The modular nature of these knowledge units becomes evident when we consider how complex ideas could be broken down into smaller, more manageable components. Each of these components is capable of being studied, modified, or combined with others much like the modular organization of biological systems. This modularity is crucial for the growth and evolution of our understanding, allowing us to adapt to new insights and challenges with efficiency and resilience.
To better grasp the nature of these fundamental units, we can turn to the concept of semantic closure. This principle, particularly visible online, refers to the ability of a piece of content to generate meaning within itself without requiring extensive external context. Nowhere is this more apparent than in the realm of microblogging where platforms like X and Noster's Kind One Notes exemplifies semantic closure in action. Each note, limited in length, encapsulates a complete thought or idea capable of standing alone while contributing to broader conversations and narratives. Additionally, each note is temporally and contextually bound even though much is not explicitly noted. And when recovered out of context at some point in the future, the note may bring a new interpretation or understanding.
When we think about notes and other stuff transmitted by relays, what is a note? A short form piece of text conveying the idea. The limits imposed by the format of a short note forces the user to distill their thoughts into concise, self contained units of information. Each note becomes a semantically closed unit capable of conveying meaning independently of the broader conversation. This microcosm of ideas demonstrates key aspects of knowledge units. Conciseness, context compression, stand alone value, recombinatory potential, and the ability to contribute to larger narratives through network effects.
The principle of semantic closure extends beyond microblogging to larger knowledge structures such as videos, books, chapters, and academic papers. These structures create a hierarchy of semantically closed units that can be understood independently while also contributing to a comprehensive whole. The organization embodies Herbert Simon's concept of near decomposability, where knowledge is arranged in hierarchical levels that are relatively independent in the short term, but interconnected over the long term. Delving deeper into near decomposability, we find that it provides a powerful framework for understanding the multilayered nature of knowledge systems.
Just as biological organisms can be analyzed at multiple levels, from somatotic particles to cells, tissues, organs, and whole organisms, knowledge systems, too, can be decomposed into various levels of granularity. These levels might range from individual words or concepts to sentences, paragraphs, chapters, entire books, or even whole fields of study. Simultaneously, these knowledge structures exhibit characteristics of what Deleuze and Guitari themed rhizomatic organization. Unlike rigid hierarchies, rhizomatic structures allow for non hierarchical interconnections between knowledge units, providing multiple entry and exit points for exploration.
This facilitates creative associations and nonlinear learning, mirroring the complex, interconnected nature of living systems. The near decomposable and rhizomatic nature of knowledge systems has profound implications for how we manage and interact with information. It enables efficient information retrieval with search systems optimized to return results at the most appropriate level of granularity for a given query. It encourages adaptive knowledge creation where authors and researchers can structure their work with multiple levels of decomposability in mind, making it more accessible and useful to a wider range of audiences.
Furthermore, it facilitates interdisciplinary connections, allowing us to more easily identify links between seemingly disparate fields at various levels of abstraction. Crucially, the way we choose to decompose these systems depends entirely on our specific goals and interests. There is no single, formally correct way to decompose knowledge. Instead, we select the level of analysis that is most meaningful and useful for our current purposes. A student might study a textbook at the chapter level to understand broad themes, while a data scientist might process the same text at the sentence or word level for natural language processing tasks. This flexibility in decomposition allows us to adapt our approach to different contexts and requirements, much like how biologists might study an organism at the cellular level for some purpose and at the organ level for others.
It enables modular learning where learners can engage with knowledge at various levels of granularity depending on their needs and expertise. It also facilitates the creation of flexible knowledge representation systems that allow users to zoom in and out between different levels of details, supporting both broad overviews and deep dives. Zooming out from these individual units, we observe how they interconnect to form networks of information. These knowledge networks bear a striking resemblance to mycelial structures in nature, allowing for the dynamic exchange and evolution of ideas.
Like mycelia networks in forests, knowledge in the digital age flows through decentralized, highly interconnected pathways. The parallels between mycelial networks in nature and digital knowledge networks are indeed striking. Both exhibit a decentralized interconnected structure where information and resources flow through multiple pathways rather than a centralized hub and spoke model. This architecture ensures robustness and adaptability, allowing both networks to evolve and thrive in dynamic environments. Moreover, the living nature of knowledge demands a shift in our relationship with information.
Rather than passive consumers, we must become active participants in the growth and evolution of our collective understanding. This participatory approach to knowledge creation and curation aligns with the decentralized nature of systems like Nostr, where each contributor plays a role in shaping the overall knowledge landscape. Recognizing knowledge as a system of interconnected, semantically closed units that can be decomposed and analyzed at multiple levels, we gain a powerful framework for navigating our information rich world. From the micro scale of individual tweets to the macro scale of entire books or wikis, this perspective reveals the living, adaptive nature of knowledge.
The fundamental building blocks of information when viewed through the lens of near decomposability and rhizomatic structure reveal a living system that is at once robust and flexible, capable of adapting to new insights while maintaining its essential coherence. Section two, formal cause, the structure and organization of knowledge. The rhizomatic essence of knowledge. To understand the formal cause of knowledge, we must first grapple with the concept of form itself. In Aristotelian philosophy, the form of a thing is its essential nature, the pattern or structure that makes it what it is.
When we apply this concept to knowledge, we encounter trouble. While we often interact with knowledge through structured interfaces like books, databases, or educational curricula, the underlying essence of knowledge itself resists such rigid organization. Instead, knowledge in its purest form exhibits a rhizomatic structure, a concept borrowed from botany and popularized in philosophy by Deleuze and Guattari. In nature, rhizomes are horizontal, underground stems that sprout roots and shoots from their nodes. Examples include potatoes, crabgrass, and turmeric.
These plants grow not from a central point, but laterally, forming networks without a clear hierarchy or central organizing principle. A secondary metaphor can be drawn from patches of grass or mycelial networks, systems that seem to fill space without a clear center, but fruits are born from an interconnected mesh. In the case of patches of grass, flowers bloom. And in the case of mycelial networks, mushrooms sprout. In the case of knowledge, these fruits are the objects, the books, the articles, the notes that we interact with and are born from a surrounding context of observations.
From the analogies described above, we can draw several key characteristics of rhizomatic structures. One, no single idea serves as the root from which all others stem, non hierarchy. Two, diverse concepts coexist and can blend with one another, multiplicity. Three, ideas can be fragmented and grow from any point. A signifying rupture. Four, knowledge serves as a map of the world. Cartographic nature. Just as a bamboo grove can spread in unexpected directions, ideas from disparate fields can unexpectedly connect, leading to novel insights and innovations. However, our challenge lies in how we navigate and harness this intrinsically rhizomatic structure through more linear or hierarchical interfaces.
A tension we'll explore further in this chapter. The rhizome analogy holds strong. What may look like a hierarchical tree like structure is only a snapshot of an ongoing, ever changing exploratory process. Practical interfaces, navigating the knowledge rhizome. While the underlying structure of knowledge may be rhizomatic, a structure without a clear hierarchy, our practical interactions with it often occur through more rigid interfaces. These interfaces serve as partial captures or representations of the rhizomatic whole, allowing us to navigate and access information in manageable ways.
Textual representations, the linear pathway. Books and articles have long been our primary means of transmitting and accessing knowledge. They provide a linear pathway through information, guiding readers from beginning to end in a predetermined sequence. This linearity offers several advantages. Coherent narrative. Authors can craft a logical flow of ideas. Structured learning. Readers can progress through information in a controlled manner. Ease of reference. Page numbers and chapters allow for quick location of specific content. However, even within this linear format, elements emerge that hint at nonlinearities.
Footnotes and endnotes, these offer tangential information and connections to other sources. Cross references, directing readers to related sections within the text. Indices, providing multiple entry points into the text based on key terms or concepts. Bibliographies, linking the text to a broader network of knowledge. Digital Knowledge Structures, embracing nonlinearity. The digital age has given rise to new forms of knowledge representation that more closely mirror the rhizomatic structure. Wikis and hyperlinked documents, these formats explicitly embrace non linear navigation.
Multiple entry points, Users can begin their exploration from various pages or articles. Interconnected content. Hyperlinks create a web of relationships between concepts. Collaborative editing. The knowledge base can grow and evolve organically. Search engines and recommendation systems. These tools create dynamic pathways through information. Personalized exploration. Results are tailored to individual users and contexts. Serendipitous discovery, related content suggestions can lead to unexpected connections. Adaptive learning, Algorithms refine results based on user behavior and feedback.
Academic disciplines and curricula. Structured yet connected. Academic fields and educational curricula attempt to organize knowledge into distinct categories. Specialized focus allows for deep exploration of specific topics. Structured learning paths Curricula guides students through foundational to advanced concepts. Methodological Consistency Disciplines develop specialized tools and approaches. However, the boundaries between disciplines are increasingly recognized as permeable. Interdisciplinary concepts. The same ideas or lessons can be taught in a class from a different discipline. Cross disciplinary collaborations lead to innovative insights and methodologies creation of new fields connecting theories and methodology across domains, new fields emerge as a result of combining a consistent set of theoretical principles with another consistent set of methodologies.
Libraries and databases, balancing structure, and flexibility. These repositories of knowledge must strike a balance between organization and interconnectedness. Traditional classification systems, Dewey Decimal System and Library of Congress classifications provide hierarchical organization. Subject headings and catalogs offer standardized access points. Digital libraries and modern databases Tagging systems allow for multiple, non hierarchical categorizations. Full text search enables exploration beyond predefined categories.
User generated metadata adds diverse perspectives to classification. Recommendation algorithms suggest related resources based on usage patterns. The interfaces through which we engage with knowledge serve crucial roles in making information navigable and accessible. They act as lenses through which specific aspects of the knowledge rhizome can be examined and understood. As understanding of knowledge structures evolves, these interfaces gradually move towards more flexible interconnected systems that better reflect the true nature of human knowledge.
Nostr's protocol architecture exemplifies this evolution toward more dynamic knowledge interfaces. Its fundamental structure incorporates multiplicities of functionality, unique identifiers, authorship attribution, tagging systems, labeling capabilities, cross referencing, and nested discussions. These elements, originally conceived for social interaction, serves as building blocks for more complex knowledge structures. Early implementations like Wikifredia and Wikinoster demonstrate how these components can be leveraged to create collaborative knowledge bases that more closely mirror the interconnected nature of ideas.
The protocol's distinctive approach to community structure differentiates it from traditional platforms. Rather than enforcing rigid boundaries, Nostra enables a spectrum of closed and open communities that can intersect and interact in novel ways. This architecture reflects the real world dynamics of knowledge creation and sharing where different disciplines and schools of thought exhibit varying degrees of overlap and collaboration. The relay system within Noster provides an instructive model for understanding the organization and dissemination of knowledge, The ability to curate information sources and collaboration channels, particularly through the outbox model enables interactions beyond immediate shared networks analogous to how ideas propagate across traditional disciplinary boundaries.
Furthermore, Nostra's protocol level design, rather than platform specific implementation, creates opportunities for experimentation and specialization. The ability of different relays and clients to implement varying specifications enables the emergence of specialized knowledge communities, much as academic disciplines develop distinct methodologies and vocabularies. The interaction of these specialized communities may facilitate the evolution of new interdisciplinary languages and knowledge sharing structures. This examination of formal structure transitions naturally to an analysis of the efficient cause of knowledge, the processes driving its creation and evolution.
The protocol's decentralized and flexible architecture enables rapid iteration and adaptation in information organization and dissemination. It creates an environment where the traditional boundaries between knowledge consumers and producers become increasingly permeable, fostering participatory modes of learning and discovery. The following chapter will examine these processes in detail, exploring how the interplay between rhizomatic structures and diverse interfaces drives knowledge creation and transformation. Understanding these mechanisms enables more effective cultivation and collective intelligence, expanding the possibilities for collaborative knowledge generation and curation.
Chapter three, efficient cause, the process of knowledge and evolution. Knowledge itself demonstrates properties remarkably similar to biological organisms. Just as living systems engage in metabolism, growth, and adaptation, knowledge undergoes continuous processes of creation, integration, and evolution. This conception of knowledge as a living process provides a framework for understanding how ideas emerge, stabilize, and transform over time, revealing patterns that mirror those found in biological systems. The metabolic process of knowledge creation.
The creation and evolution of knowledge can be understood through the metaphor of metabolism, where new information is digested and integrated into existing knowledge structures. This process, like biological metabolism, involves both the breaking down of complex ideas into manageable components and the building up of new understanding. Through this lens, we can better understand how knowledge systems maintain themselves while growing and adapting to new conditions. New knowledge often begins as someone perceives a pattern, anomaly, or possibility that others have not yet recognized.
These early ideas tend to be fragile. They may be incomplete, poorly articulated, or challenge existing assumptions. When shared with others, these nascent ideas often face skepticism, dismissal, or active pushback based on others' established understanding and beliefs. This initial resistance is an important part of the knowledge validation process. Some ideas will fail this scrutiny, while others will be refined and strengthened through critical examination and evidence gathering until they become more widely accepted as valid contributions to our collective knowledge.
This initial fragility serves an essential function in the knowledge ecosystem. Just as biological systems require variability for evolution, the malleability of new ideas allows for rapid adaptation and refinement before they become firmly established. Through this process, knowledge systems can explore new possibilities while maintaining their essential structure and function. As knowledge matures, it undergoes a process of stabilization and integration just as nutrients are used to build the scaffolding of an organism. This occurs through the interplay of verification, validation, and community adoption.
Ideas are tested through peer review and empirical investigation, refined through academic discourse, and gradually integrated into existing theoretical frameworks. This process of stabilization doesn't occur in isolation, but rather through the complex interactions of academic and professional networks, educational systems, and practical applications. The translation problem in knowledge evolution. A fundamental tension exists between knowledge's rhizomatic form and our attempts to capture and transmit it through formal structures. This tension, which we might call a translation problem, lies at the heart of knowledge evolution.
Knowledge itself exists as resistant to complete formal representation. It possesses no clear boundaries between concepts, maintains multiple overlapping relationships, and constantly evolves in response to new inputs and contexts. Our need to communicate and preserve knowledge demands that we attempt to capture it in more formal structures. We create discrete units of knowledge, bounded definitions, and hierarchical organizations. This process of formal encapsulation, while necessary for transmission and preservation, inevitably simplifies and constrains the living complexity of knowledge.
The relationship between these two forms, the living rhizome and its formal representation, creates what might be called a modeling relation from Robert Rosen, an attempt to capture the complexity of living knowledge in simpler, more manageable forms. The very process of turning ideas contained in our minds to words, spoken or otherwise, is necessarily a transformative process, a process that is not reversible. The transmission of knowledge involves multiple layers of translation and interpretation, each adding complexity to the evolution of ideas. Individual understanding is shaped by personal experience, cultural context, and domain expertise.
When knowledge is transmitted, it must be translated from its rhizomatic form into linear communication, then reconstructed by the receiver through their own conceptual framework. This process of translation and reconstruction continually reshapes the knowledge itself contributing to its evolution. Dynamics of Knowledge Change The evolution of knowledge follows patterns that mirror those found in biological systems, exhibiting both gradual change and sudden transitions. Most commonly, knowledge evolves through gradual accumulation, the slow refinement of existing theories, the addition of supporting evidence, and the progressive specialization of fields.
This process resembles the gradual adaptation of organisms through natural selection. However, knowledge systems also experience periods of rapid change analogous to punctuated equilibrium in biological evolution. These episodes often coincide with paradigm shifts, revolutionary discoveries, or technological breakthroughs that fundamentally alter how we understand and interact with knowledge. The emergence of new fields or disciplines often occurs during these periods of rapid change as existing knowledge structures reorganize themselves around new insights or methodologies.
The evolution of knowledge is shaped by fundamental tensions between stability and innovation, between the need for reliable, verified knowledge and the importance of novel insights and perspectives. Similarly, knowledge systems must balance the benefits of deep specialization with the need for cross disciplinary integration and synthesis. These tensions create dynamic patterns of change that contribute to the overall resilience and adaptability of knowledge ecosystems. Further Directions and Duplications As we improve our understandings of knowledge and how we interact with it, we will need to develop tools and systems that can better support the natural dynamics of knowledge evolution.
Tools that can capture patterns, track the development of ideas, and facilitate processes of knowledge creation that is capable of maintaining semantic meaning aligned and approximated to the representation contained within the author's mind. More fundamentally, we need to re conceptualize our relationship with knowledge itself. This means creating environments that support both the stability needed for reliable knowledge and the flexibility required for innovation and adaptation. Chapter four, final cause, the purpose of knowledge and knowledge ecosystems. Knowledge serves as our guiding star, illuminating the path forward and enabling us to navigate the complexities of our world.
At its core, knowledge fulfills a fundamental purpose. It acts as a catalyst for adaptive behavior, drawing organisms, including humans, towards information that is meaningful and beneficial for their survival and thriving. However, the abstract nature of knowledge presents a significant challenge. We cannot directly manipulate or interact with knowledge in its pure, conceptual form. Instead, we engage with knowledge that has been captured and formalized in language or other representational systems. This limitation necessitates the development of interfaces and methods for knowledge interaction, leading to the creation of knowledge bases and other knowledge management systems.
The purpose of knowledge bases then extends beyond mere storage and retrieval of information. They serve as bridges between the abstract subjective knowledge and the concrete decision making. Effective knowledge bases facilitate the efficient transmission of knowledge, enabling users to navigate the information landscape, synthesize ideas to generate new insights, update and refine existing knowledge, and collaborate in the collective development of understanding. To fulfill its purpose, a knowledge base must strive to mirror the inherent structure of knowledge itself. This is where the concept of rhizomatic knowledge structures becomes crucial.
Knowledge, in its natural state, resembles a rhizome, a non hierarchical, multiply connected network of ideas and concepts. Unlike the rigid tree like structures often imposed on information, rhizomatic structures allow for dynamic growth, multiple entry points, and unexpected connections between disparate elements. The most effective knowledge bases, therefore, are those that conform to this rhizomatic structure. They should offer non hierarchical organization that allows for multiple pathways through information, dynamic linking capabilities that reveal unexpected connections between ideas, flexibility to accommodate new information without disrupting existing structures, and support for collaborative knowledge creation and curation.
Interestingly, the affordances provided by Nostr align remarkably well with these requirements for rhizome like knowledge bases. Nostra's decentralized open protocol offers several features that make it highly amenable to creating rhizomatic knowledge structures. Decentralized nature, mirroring the distributed nature of knowledge itself, Nostra's decentralized structure allows for multiple entry points and perspectives. Once a structure has been defined, anyone can take the existing structure, modify it, and build their own garden of knowledge with their own rules for contribution.
The decentralized and permissionless nature of Nostr allows for anyone to continue the conversation, build upon, and modify the existing structure while still maintaining the integrity of the original content and being interoperable with other knowledge bases. The two data specifications for a Nostr knowledge base, Nostr knowledge base implementation possibilities, n k b I p o one and n k b I p o two. Provide a foundation for creating structured knowledge bases that, when coupled with the decentralized nature of Nostr, can enable the creation of knowledge that aligns with the intrinsic rhizomatic structure of knowledge.
N k b I p o one enables for the creation of knowledge as a rhizomatic structure. Taking influence from zettelkasten, a popular note taking method, users can link ideas in a variety of ways, of which a linear assembly is one such possibility. Paired with other affordances such as tags, labels, external ID, forking of notes, knowledge can be navigated in a nonlinear way that where every note can be a starting point for new explorations. N k b I p zero two defines a specification for AI embeddings, which can be used for context free navigation. The creation of numerical representations of text allow for distance calculations between the semantic relationships of notes.
What this means is that even if the authors have not explicitly linked ideas together, embedding distances allow for a formal process of grouping disconnected ideas. What this implies is that a user who is interested in learning about a topic can be recommended notes that are semantically similar even if they are not directly linked. Additionally, just like GitHub Copilot, the AI system can take the context of already existing text and notes and synthesize new knowledge. It is important to note that these tools must be used in conjunction with human curation as the AI system must be guided in such a way that is meaningful to the writer and potential readers.
By leveraging these affordances, we can create knowledge bases that not only store information but actively support the expansion of human understanding and capability. Such systems would go beyond traditional static repositories to become dynamic environments of exploration, discovery, and the generation of new insights. Continuing to develop and refine knowledge bases, our goal should be to create interfaces that emulate the dynamic, adaptive nature of knowledge itself. These systems should support prediction, enable action, and facilitate learning in ways that mirror our cognitive processes.
They should be accessible, comprehensible, interconnected, and adaptable allowing users to navigate written knowledge with unprecedented ease and insight regardless of background. In conclusion, the purpose of knowledge is to guide adaptive behavior and enable effective interaction with our environment. This is fundamentally intertwined with the purpose of knowledge bases. Through the strive to create knowledge bases that mirror the rhizomatic structure of knowledge itself, we can develop tools that not only store and organize information, but actively support the growth and evolution of human understanding.
Nostr and its rhizome friendly affordances offer exciting possibilities for realizing this vision, potentially revolutionizing how we interact with and build upon our collective knowledge. So that was Liminal's article titled Understanding Knowledge and whoop de doo, man. Yeah. And I know it was it's a lot to take in. You know? We don't normally on this show talk about, you know, subjects as deep as what knowledge itself actually is and what structure it takes and the fact that you kinda can't directly interact with knowledge until after it's been represented in some symbolic form like letters or, you know, having a discussion with your buddy or listening to a podcast.
Those are all symbolically or symbologic instantiations of particular points of knowledge that particular people have. So why am I even bringing this to you today? Well, it's because I'm a big Nostra user, and this was essentially all about how Liminal was telling you that the very structure of Nostr, the way it exists right now, without with with almost no iterate you know, with no not even having to iterate on its structure at all is already able to be a receptacle of this ability to store, categorize, mix and match different pieces of knowledge from different authors. Some may be collaborating together.
Other authors may actually be on to the same you know, be looking at the same idea, but they don't even know each other. They don't know that they're looking at at the same idea. And then we get into where Liminal brings up the notion of bringing in artificial intelligence. What he's really talking about, and me and him have talked about this on the show before, and that was, in fact, episode nine thirty seven of Bitcoin and I'll if I remember, I'll try to put the link directly to that episode in the show notes. The ability for AI to start looking at different notes.
And sure, I know what you're saying. Nobody wants nobody really is gonna care about the shit posting. There are other notes that are out there. Right? And and and we're also, you know, not we're we're also not thinking of the capability of saying, well, me making a shitpost probably is so semantically divergent from the idea of, I don't know, solar processes that maybe some other Nostra user decided to write a small essay on. How does Stellar Dynamics work? Right? My ship posting is gonna be so distant away from that that the that if we do crawl this entire network of knowledge with an AI, it's probably just gonna throw it out. It's gonna throw out memes. It's gonna it's just gonna be going through. It's like like, think of it like some kid looking for something that they lost in a big old trunk upstairs in the attic, and they're just chucking crap out of the you looking for what chucking crap out of the looking for what it is that they want. I mean, that's what that's sort of where Liminal's getting. And I'm I know that I'm not touching as deep down as what what Liminal is thinking in his mind, but that is exactly the essence of what he's talking about. I'm not Liminal.
I do not see exactly how he's thinking about using AI to connect possibly different threads or context, the semanticism of notes. I I'm not in his mind. I cannot touch his knowledge. The only thing that I can touch is what he wrote here and what he's writing about AI. So I have to interpolate. I have to, like, expand what I think he's talking about, which causes me to do what? Make a whole bunch of assumptions. And in many cases, you kinda have to. And that way, when Liminal hopefully, he'll listen to this because he I haven't told him that I'm gonna be reading this on the show today, so I I will let him know that I've done so. But Liminal, if you're listening to this, I kinda get what you're talking about here.
You have to symbolically reflect what it is you're thinking in the form of the written word, and I've got to ingest that. And then I make assumptions about it, which can make an ass out of you and me. But when you hear this and you say, that's not at all what I was thinking. Let me explain it further to you. Or maybe I did get part of it, but I know I'm not getting all of it. I'm not in your head. But I do I think that I feel at a fundamental level what you're talking about when it comes to knowledge. What's in my head about things like like the Forest Walker idea that I have?
Man, I like I like, Guy Swan read it on his podcast, and he picked apart a couple of good problems. But he didn't even he didn't even touch the surface of just how many problems there is with that system. But I had to actually formalize it into words. And once I formalize it into words and I heard Guy Swan read it back to me, I was like, I now I've gotta go through it. I gotta go do that one, and I've got to address this. And, oh, by the way, he didn't think about what happens in the wintertime, is what happens if the wood is too wet because that can that can actually be a thing. You know, there's all manner of issues about the Forest Walker, but it's all in my head. And if I don't formalize it in a symbol logical way, like writing it out in in symbols in the form of letters of the alphabet, right, then there's no hope of having any of that knowledge restructured by somebody else. Or the fact of me writing it down does something magical to what's in my head. It causes it to exist outside of me.
If I die tomorrow, the Forest Walker article is out there. At least one other person has heard that article because Guy Swan read it on his podcast. You guys have heard me talk about it before. But the idea is out there. It's it's a baby. It's walking around. But when we take it back to Noster, that article, I can get to that article on Noster, and I can't remember which NIP it uses, but it was I I published it I published Forest Walker in a couple places, and one of them I did on highlighter, Pablo f seven z. Right? Now that nip is a long form nip. It's like if if your relay isn't reading, like, kind one is only reading kind one notes, then you're not seeing that article because it is not a kind one note.
But that note is out there, and it is available by somebody who's reading that particular kind of NIP on their relay. Right? So it exists, and I can die tomorrow, and it will still exist. I pulled something out of my head. Now other people can attach to it. And like like what Liminal's saying, what if we have a AI crawl it? The Noster network. Just start crawling and say, okay. Given what you've said here in this Forest War Walker article, you know, there was a guy over here that wrote this other article about biochar, and y'all both use the word biochar.
Maybe that is semantically close enough that I should start looking deeper. And I when I say I, I mean, I as an AI that's crawling the system. Maybe I should look deeper and see if there's any other connections. And maybe the guy uses the word forest. And maybe the guy, you know, ends up using the words, oh, God, wood vinegar. Maybe the guy uses something like data collection. Well, shit. At that point, we start getting very, very semantically close. Even though I have no idea what this theoretical article said, I don't know the author that wrote it. The fact that we are becoming very, very, you know, very much closer because we're using a lot of the same words, then maybe we get a semantic distance, a value that's assigned to those two notes as a connect as a bridging connection that says, I don't know, something like a short distance, which means these are very similar and some other author may pick that up and say, you know, scan the Nostra network and look for all semantic differences with a distance of no more than, I don't know, x.
Right? And then a filter is enacted, and then every they can see all these things and say, okay. Well, here's all these connected notes. Let's go through them and see what I can dig up. Let's go gardening. Let's go harvesting. Maybe they start looking at these notes in an interconnected ways and then start looking at it from a completely different lens and comes up with something of their own, either completely novel or helping me and this other author get our shit together because we were wrong about whatever. Right? This is what Liminal's talking about.
And I saw this a long time ago. I saw this he calls it affordances. And he's that's a great word to use. Nostra affords the ability for us to look at notes and their interconnectedness in this very, not only fundamental way, but a highly utilitarian way. And I don't mean utilitarianism in the old you know, if somebody's thinking, oh, yours sounds like Soviet Russia. No. No. No. I'm actually talking about utility, something I can use, something that brings, you know, a better, you know, a better way of thinking for me or something. Like, teaches me, like, four notes that if I take them all together, teaches me exactly how to go garden, I don't know, golden beets.
That you know, maybe I like golden beets. Maybe I've never been able to grow golden beets. But some somehow or another, by asking AI to filter out all the Nostra network except everything that is about beets and and and gardening beets and and make sure that semantic differences are no more than this x amount of distance away so that I can collect up very close notes. Maybe it teaches me how to grow golden beets, and I start growing them and it's completely successful. It actually, from that standpoint, changes my life. I mean, in this particular example, it's kinda dumb because it's just golden beets. Shit. You can go get that at the store, but you get my point. We're playing with Nostra like it's a toy right now. It's not a toy. It's Nostra is so far from being a toy, it's not even funny. And this is why I get really, really miffed when people shit all over Nostra because they're like, it's not as good as Twitter.
Twitter has no freaking hope to scale knowledge at all. It'll scale Grok's knowledge, and that knowledge will make a shit ton of money for the people over at at x or whatever, but it's not going to expand human knowledge. It doesn't have the capacity, and neither does Facebook, and neither does Instagram, and neither does TikTok. None of these things. Because they are all so centralized. The only other thing that I could think of is possibly the Feddy verse, but that is so filled with trite bullshit that it's not even funny. And it's a real pain in the ass to jump from one of their servers to another. And and from that standpoint, it's not a walled garden. It's like a million walled gardens all contained in a something that they would call an open garden, but it's not really all that easy. Nostr is the only thing that has, as Liminal says, the affordances to be able to mimic a structure that would provide us a pathway to discover new knowledge.
Yeah. It's okay. Post memes. I think memes are hilarious. I think it's funny when people shitpost. It doesn't mean that I don't find value in it. I actually do. There's nothing better than having somebody make you laugh when you've had a shitty day. That's of real value. Maybe there's some semanticism that can be discovered there by somebody who wants to be a stand up comic. Hey. Go show me all the shitposts. How are how are all these shitposts connected? How's Korn DeLorean shitpost, you know, semantically close to this this other author's shitposting?
That's not without value. This all exists inside this structure that Fiat Joff gave us. And even fiat Joff sometimes, I think, doesn't like Noster. But who knows what fiat Joff is actually kinda crazy. It's okay. I love that I he it's it's like Shia Labeouf. They're both batshit crazy, and I don't care. I love them both. I don't care what anybody thinks about me liking Shia Shia Labouf because he's, like, just hated Trump. I don't care. I don't care. I don't care. Fiat Joffe has said on several occasions some bad shit about his own creation, and people got got mad at him. I don't care. He's batshit crazy, and I love him to death for it.
Only a batshit crazy person could come up with something like Nostr. Only liminal could come up with something that I just revved you. So with that said, I really, really am desperate for people to stop looking at Nostril like it's simply a social media platform. It's not. It is probably going to become one of the most extensive knowledge bases that humankind has ever come to know, and Fiat Joff can't even stop it. Oh, nobody's gonna use it. Yeah. We're I use it every day. I know hundreds of people that use Nostra every day.
And every single day, more and more knowledge gets poured into this thing. And people like Liminal and Lissarian, if I'm pronouncing her name right. She's one of the other developers on this project Alexandria. If they could just get some freaking grant funding and I really mean that. They've been rejected by a couple of different places, and I don't understand why. It is one of the most pivotal things in the human spirit to create knowledge, to organize knowledge. Remember all the way even if you're a Christian or not, remember all the way back to the story of Adam and Eve. What was Adam's job?
Adam had a couple of jobs. Basically, he had one real huge job. Him and Eve were supposed to name all of the creatures and plants and animals and trees and blades of grass. He was supposed to give everything in the Garden of Eden Eden a name. Honey, that's categorization of knowledge. That that's that is that is turning a thing that is amorphous into a static structure. By giving a tree a name, like it's a tree, I've turned this thing that I would have to normally describe by sight to somebody who is blind, instead of doing it that way, I could just say, ah, we both agree, we both look at this thing, and we both say, that's going to now be a tree.
Now when I go and tell somebody that I saw a tree, the idea forms in their mind through that one symbol. Nostra's way more important than people think it is, and it's kind of embarrassing that we're missing it. It's right here, right in front of us. It's going to be everything that Wikipedia thought it was going to be and could never be. It's going to be everything that Twitter thought it could be and is never going to be because it never could be. It never had the structure for it. Only Nostr has the structure to usher in what comes next. And you'll go, I I know. You'll say, David, I think you've just damn near lost your mind on this one. No. No. I really haven't.
We've been forced into thinking that McDonald's and Taco Bell is real food. It's not. It's bullshit. It's crap. It's garbage. It's poison. But if I go to a party and I say that, people are gonna look at me funny, aren't they? But I guarantee the people listening to my voice, most of you are nodding your head going, yes. It's poison. We've all we we're we're breaking out of this knowledge structure that we've been given through media since we were children. Probably even before we were children, we were probably hearing the bullshit from the news in our mother's wombs. We were probably already well on our way to being programmed to be in good little dogs and cats so that we could function in society and not cause problems.
You know, like that Gutenberg dude that invented the printing press and started printing of all things. The first thing that he printed was Bibles. He didn't he didn't print Odyssey and Iliad. He didn't print the tomes of Plato and Socrates and all that shit. No. He's like the Bible. The most disruptive thing that you could have done at the time that Gutenberg was doing his shit was to print the freaking Bible. Why? Because it was always, always only ever held by the people who owned the church. And if you wanted to know what God was actually saying in the book, you couldn't have one yourself, you had to go to church, which meant that knowledge was gatekept.
And it always has been. It's never changed until people like Gutenberg come along. And I always used to think that the World Wide Web was gonna be the printing press two point o, and I was so wrong because everything was gated, and it's become more so. And now we have Nostr, and yet we treat it like social media. Nobody sees any potential at all, apparently. All I hear about is people bitching that, you know, direct messages don't work. So what? The other shit works. Well, nobody's gonna read these these long ass notes. I do. I read this one. This one is in the form of a note on Nostra through project Alexandria.
If you have not gone and looked at what Liminal and LeSaren and a couple of other people are doing, you're really missing out on what the potential of all this actually is. Because like I said, right now, we're just like little kids playing with a a toy Ferrari on the floor. But what we're really playing with is a real Ferrari. We just haven't turned the key in the ignition yet. In fact, we can't even climb into the driver's seat yet because the door's closed and we're too small to reach the handle. But once that son of a bitch handle gets reached for and we open the door, and we sit our happy asses down, and we plug that freaking key into the ignition and fire that son of a bitch up, watch out.
A 80 miles an hour plus. Okay. Y'all, reach out to Liminal, Lycerin. I'm gonna put both of their, their Noster in pubs into the show notes. Let them know what you thought, if you have any ideas about what they're doing. And honestly, if you don't wanna do any of that, reach out to, OpenSats and ask them to fund Project Alexandria. I love Matt O'Dell. I really do. But they're one of the people that have denied a grant to Project Alexandria. And I for the life of me, I'm going to I'm not even gonna make any assumptions about the grant proposal and what its format was versus how Gigi and all the guys over at OpenSats read it. I'm not gonna make any assumptions at all as to why that the this granting opportunity has not come to Project Alexandria.
But I get the feeling that if done right, politely, respectfully, with kind words, and an appreciation for the project that maybe, just maybe, if they're Gigi, Matt, some of the other guys at at OpenSats, hears it enough in a again, in a positive light, not, hey. Screw you. David said you did you you didn't give no money. No. Don't do that shit. Just tell him what if you agree with me and you think that Noster is as important as it is and that Project Alexandria, which is part of what Liminal's discussing here in this piece, if you think it has the potential to unlock it and to crack an egg the likes of which we have never seen before, to get a hold of those guys and say, take a look at their grant proposal one more time, please.
Please. You do that, and I will see you on the other side. This has been Bitcoin, and and I'm your host, David Bennett. I hope you enjoyed today's episode episode and hope to see you again real soon. Have a great day.
It is 09:57AM Pacific Daylight Time. It is the April 2025. This is episode ten eighty two of Bitcoin End. It's not a news day. It's a think day. Today, I'm reading a piece called Understanding Knowledge from my friend, Liminal. And before you skip ahead, let me tell you, this ain't just academic naval gazing. This one's got roots. It ties straight into how we handle knowledge in a decentralized world. Nostra, zettelkasten, mycelial structures, the whole nine. We're talking about what it means to know something in a world where knowledge doesn't live on university shelves anymore. It moves. It forks. It dies. It spawns. It's organic. And sometimes, it changes form.
The network matters more than the node. So if you're into stuff like how information spreads across Nostr, how your zettelkasten node system mimics forest fungi, or how the collapse of centralized institutions might not be a bug, but a feature, buckle up. It's Bitcoin and knowledge. Let's go. Introduction. Knowledge as a living ecosystem. Why Investigate the Nature of Knowledge? Understanding the nature of knowledge itself is fundamental, distinct from simply studying how we learn or communicate. Knowledge exists first as representations within individuals, separate from how we interact with it.
Consider a physicist's understanding of quantum mechanics, the complete interconnected web of concepts in their mind, exists differently from how they might explain it in a lecture, write it in a textbook, or summarize it in a tweet. When knowledge must be shared, it undergoes transformative process, moving from its represented form within a dynamically changing individual to something that could be transmitted. This transmission demands not just translation into words, but adaptation to specific mediums, each shaping how that knowledge is ultimately conveyed. As technology advances, developing systems that more closely mirror knowledge's true form becomes crucial.
Not just to improve the retention and navigation, but to enable more effective critical examination and connection of ideas across existing bodies of knowledge. Traditional approaches that ignore this fundamental nature of knowledge inevitably create artificial constraints on how we understand, share, and build upon human understanding. Challenging the static perception of knowledge. Traditionally, knowledge has been perceived as a static repository, a meticulously cataloged library, or a readily queryable database. This conventional viewpoint treats knowledge as a collection of discrete facts and information that can be stored, retrieved, and utilized in a linear fashion.
However, this perspective may oversimplify the complexity and fluidity inherent in how knowledge is generated, shared, and evolved. Viewing knowledge through this static lens risks neglecting the intricate interactions and continuous evolution that characterize our collective understanding. Reimagining knowledge as a living ecosystem. Contrary to the static model, this essay proposes a reimagining of knowledge as a living process. In this framework, knowledge is seen as dynamic and ever evolving, much like a natural ecosystem that adapts, grows, and thrives through complex interactions and relationships.
This biological perspective highlights several key characteristics. Resilience, the ability to maintain integrity while adapting to new information. Interconnectedness, the organic formation of relationships between ideas. Higher order interactions, the capacity to be built with other sets of ideas. Adaptability, the capacity to evolve in response to new contexts and needs. In conceptualizing knowledge in this manner, we acknowledge its capacity for growth and transformation, mirroring the resilience and adaptability found in living systems.
This approach not only provides a more accurate representation of how knowledge functions, but also offers practical insights for designing more effective and responsive knowledge systems. The four perspectives. One, the building blocks. Material cause. Just as living organisms are made up of cells, knowledge systems are built from fundamental units of understanding. These range from simple concepts to complex theories, each capable of standing alone while contributing to larger structures. Think of them as knowledge cells, Self contained, but ready to connect and combine in meaningful ways.
Two. The pattern of organization. Formal cause. If you've ever seen how mushrooms connect through underground networks, you'll have a good picture of how knowledge naturally organizes itself. Rather than fitting into neat hierarchical boxes, knowledge forms web like connections in all directions. Modern digital systems are finally catching up to this natural way of organizing information, moving beyond rigid folder structures to more fluid, interconnected approaches. Three. The forces of change. Efficient cause. Knowledge grows and changes through a complex dance of individual insights, community discussions, technological tools, and cultural developments.
Like an ecosystem, it evolves through the interaction of many forces. New tools, like Nostr, are making these natural processes more visible and more powerful, allowing knowledge to evolve more organically than ever before. Four, the purpose and direction. Final cause. Why does knowledge exist? Not just to sit there looking impressive, but to help us understand our world and make better decisions. This purpose shapes how knowledge systems develop and organize themselves. When we design knowledge tools and platforms, we need to keep this ultimate purpose in mind.
Why this matters. Understanding knowledge as a living system isn't just a nice metaphor. It has practical implications for how we create and share information, build knowledge management tools, design educational systems, approach research and discovery. When we work with knowledge's living nature rather than against it, we can create more effective ways to learn, share, and grow our collective understanding. Looking forward. Continuing to explore this perspective, we'll dive deeper into each of these aspects, examining how they play out in practical situations and how we can better align our knowledge tools and practices with the natural dynamics of living systems.
This journey will help us see knowledge not as a static resource to be stored, but as a living ecosystem to be nurtured and grown. Whether you're a student, researcher, teacher, or knowledge worker, this perspective can change how you interact with and contribute to our collective understanding. Section one, material cause, the substance of knowledge. Knowledge itself as an entity is composed of fundamental building blocks that form the basis of our understanding of the world. These knowledge cells combine and interact to create complex structures of meaning and insight in response to new stimuli and insights born from observations and existing knowledge.
To truly comprehend the material cause of knowledge, we must dive into the interplay between these basic components and the systems they engender. The modular nature of these knowledge units becomes evident when we consider how complex ideas could be broken down into smaller, more manageable components. Each of these components is capable of being studied, modified, or combined with others much like the modular organization of biological systems. This modularity is crucial for the growth and evolution of our understanding, allowing us to adapt to new insights and challenges with efficiency and resilience.
To better grasp the nature of these fundamental units, we can turn to the concept of semantic closure. This principle, particularly visible online, refers to the ability of a piece of content to generate meaning within itself without requiring extensive external context. Nowhere is this more apparent than in the realm of microblogging where platforms like X and Noster's Kind One Notes exemplifies semantic closure in action. Each note, limited in length, encapsulates a complete thought or idea capable of standing alone while contributing to broader conversations and narratives. Additionally, each note is temporally and contextually bound even though much is not explicitly noted. And when recovered out of context at some point in the future, the note may bring a new interpretation or understanding.
When we think about notes and other stuff transmitted by relays, what is a note? A short form piece of text conveying the idea. The limits imposed by the format of a short note forces the user to distill their thoughts into concise, self contained units of information. Each note becomes a semantically closed unit capable of conveying meaning independently of the broader conversation. This microcosm of ideas demonstrates key aspects of knowledge units. Conciseness, context compression, stand alone value, recombinatory potential, and the ability to contribute to larger narratives through network effects.
The principle of semantic closure extends beyond microblogging to larger knowledge structures such as videos, books, chapters, and academic papers. These structures create a hierarchy of semantically closed units that can be understood independently while also contributing to a comprehensive whole. The organization embodies Herbert Simon's concept of near decomposability, where knowledge is arranged in hierarchical levels that are relatively independent in the short term, but interconnected over the long term. Delving deeper into near decomposability, we find that it provides a powerful framework for understanding the multilayered nature of knowledge systems.
Just as biological organisms can be analyzed at multiple levels, from somatotic particles to cells, tissues, organs, and whole organisms, knowledge systems, too, can be decomposed into various levels of granularity. These levels might range from individual words or concepts to sentences, paragraphs, chapters, entire books, or even whole fields of study. Simultaneously, these knowledge structures exhibit characteristics of what Deleuze and Guitari themed rhizomatic organization. Unlike rigid hierarchies, rhizomatic structures allow for non hierarchical interconnections between knowledge units, providing multiple entry and exit points for exploration.
This facilitates creative associations and nonlinear learning, mirroring the complex, interconnected nature of living systems. The near decomposable and rhizomatic nature of knowledge systems has profound implications for how we manage and interact with information. It enables efficient information retrieval with search systems optimized to return results at the most appropriate level of granularity for a given query. It encourages adaptive knowledge creation where authors and researchers can structure their work with multiple levels of decomposability in mind, making it more accessible and useful to a wider range of audiences.
Furthermore, it facilitates interdisciplinary connections, allowing us to more easily identify links between seemingly disparate fields at various levels of abstraction. Crucially, the way we choose to decompose these systems depends entirely on our specific goals and interests. There is no single, formally correct way to decompose knowledge. Instead, we select the level of analysis that is most meaningful and useful for our current purposes. A student might study a textbook at the chapter level to understand broad themes, while a data scientist might process the same text at the sentence or word level for natural language processing tasks. This flexibility in decomposition allows us to adapt our approach to different contexts and requirements, much like how biologists might study an organism at the cellular level for some purpose and at the organ level for others.
It enables modular learning where learners can engage with knowledge at various levels of granularity depending on their needs and expertise. It also facilitates the creation of flexible knowledge representation systems that allow users to zoom in and out between different levels of details, supporting both broad overviews and deep dives. Zooming out from these individual units, we observe how they interconnect to form networks of information. These knowledge networks bear a striking resemblance to mycelial structures in nature, allowing for the dynamic exchange and evolution of ideas.
Like mycelia networks in forests, knowledge in the digital age flows through decentralized, highly interconnected pathways. The parallels between mycelial networks in nature and digital knowledge networks are indeed striking. Both exhibit a decentralized interconnected structure where information and resources flow through multiple pathways rather than a centralized hub and spoke model. This architecture ensures robustness and adaptability, allowing both networks to evolve and thrive in dynamic environments. Moreover, the living nature of knowledge demands a shift in our relationship with information.
Rather than passive consumers, we must become active participants in the growth and evolution of our collective understanding. This participatory approach to knowledge creation and curation aligns with the decentralized nature of systems like Nostr, where each contributor plays a role in shaping the overall knowledge landscape. Recognizing knowledge as a system of interconnected, semantically closed units that can be decomposed and analyzed at multiple levels, we gain a powerful framework for navigating our information rich world. From the micro scale of individual tweets to the macro scale of entire books or wikis, this perspective reveals the living, adaptive nature of knowledge.
The fundamental building blocks of information when viewed through the lens of near decomposability and rhizomatic structure reveal a living system that is at once robust and flexible, capable of adapting to new insights while maintaining its essential coherence. Section two, formal cause, the structure and organization of knowledge. The rhizomatic essence of knowledge. To understand the formal cause of knowledge, we must first grapple with the concept of form itself. In Aristotelian philosophy, the form of a thing is its essential nature, the pattern or structure that makes it what it is.
When we apply this concept to knowledge, we encounter trouble. While we often interact with knowledge through structured interfaces like books, databases, or educational curricula, the underlying essence of knowledge itself resists such rigid organization. Instead, knowledge in its purest form exhibits a rhizomatic structure, a concept borrowed from botany and popularized in philosophy by Deleuze and Guattari. In nature, rhizomes are horizontal, underground stems that sprout roots and shoots from their nodes. Examples include potatoes, crabgrass, and turmeric.
These plants grow not from a central point, but laterally, forming networks without a clear hierarchy or central organizing principle. A secondary metaphor can be drawn from patches of grass or mycelial networks, systems that seem to fill space without a clear center, but fruits are born from an interconnected mesh. In the case of patches of grass, flowers bloom. And in the case of mycelial networks, mushrooms sprout. In the case of knowledge, these fruits are the objects, the books, the articles, the notes that we interact with and are born from a surrounding context of observations.
From the analogies described above, we can draw several key characteristics of rhizomatic structures. One, no single idea serves as the root from which all others stem, non hierarchy. Two, diverse concepts coexist and can blend with one another, multiplicity. Three, ideas can be fragmented and grow from any point. A signifying rupture. Four, knowledge serves as a map of the world. Cartographic nature. Just as a bamboo grove can spread in unexpected directions, ideas from disparate fields can unexpectedly connect, leading to novel insights and innovations. However, our challenge lies in how we navigate and harness this intrinsically rhizomatic structure through more linear or hierarchical interfaces.
A tension we'll explore further in this chapter. The rhizome analogy holds strong. What may look like a hierarchical tree like structure is only a snapshot of an ongoing, ever changing exploratory process. Practical interfaces, navigating the knowledge rhizome. While the underlying structure of knowledge may be rhizomatic, a structure without a clear hierarchy, our practical interactions with it often occur through more rigid interfaces. These interfaces serve as partial captures or representations of the rhizomatic whole, allowing us to navigate and access information in manageable ways.
Textual representations, the linear pathway. Books and articles have long been our primary means of transmitting and accessing knowledge. They provide a linear pathway through information, guiding readers from beginning to end in a predetermined sequence. This linearity offers several advantages. Coherent narrative. Authors can craft a logical flow of ideas. Structured learning. Readers can progress through information in a controlled manner. Ease of reference. Page numbers and chapters allow for quick location of specific content. However, even within this linear format, elements emerge that hint at nonlinearities.
Footnotes and endnotes, these offer tangential information and connections to other sources. Cross references, directing readers to related sections within the text. Indices, providing multiple entry points into the text based on key terms or concepts. Bibliographies, linking the text to a broader network of knowledge. Digital Knowledge Structures, embracing nonlinearity. The digital age has given rise to new forms of knowledge representation that more closely mirror the rhizomatic structure. Wikis and hyperlinked documents, these formats explicitly embrace non linear navigation.
Multiple entry points, Users can begin their exploration from various pages or articles. Interconnected content. Hyperlinks create a web of relationships between concepts. Collaborative editing. The knowledge base can grow and evolve organically. Search engines and recommendation systems. These tools create dynamic pathways through information. Personalized exploration. Results are tailored to individual users and contexts. Serendipitous discovery, related content suggestions can lead to unexpected connections. Adaptive learning, Algorithms refine results based on user behavior and feedback.
Academic disciplines and curricula. Structured yet connected. Academic fields and educational curricula attempt to organize knowledge into distinct categories. Specialized focus allows for deep exploration of specific topics. Structured learning paths Curricula guides students through foundational to advanced concepts. Methodological Consistency Disciplines develop specialized tools and approaches. However, the boundaries between disciplines are increasingly recognized as permeable. Interdisciplinary concepts. The same ideas or lessons can be taught in a class from a different discipline. Cross disciplinary collaborations lead to innovative insights and methodologies creation of new fields connecting theories and methodology across domains, new fields emerge as a result of combining a consistent set of theoretical principles with another consistent set of methodologies.
Libraries and databases, balancing structure, and flexibility. These repositories of knowledge must strike a balance between organization and interconnectedness. Traditional classification systems, Dewey Decimal System and Library of Congress classifications provide hierarchical organization. Subject headings and catalogs offer standardized access points. Digital libraries and modern databases Tagging systems allow for multiple, non hierarchical categorizations. Full text search enables exploration beyond predefined categories.
User generated metadata adds diverse perspectives to classification. Recommendation algorithms suggest related resources based on usage patterns. The interfaces through which we engage with knowledge serve crucial roles in making information navigable and accessible. They act as lenses through which specific aspects of the knowledge rhizome can be examined and understood. As understanding of knowledge structures evolves, these interfaces gradually move towards more flexible interconnected systems that better reflect the true nature of human knowledge.
Nostr's protocol architecture exemplifies this evolution toward more dynamic knowledge interfaces. Its fundamental structure incorporates multiplicities of functionality, unique identifiers, authorship attribution, tagging systems, labeling capabilities, cross referencing, and nested discussions. These elements, originally conceived for social interaction, serves as building blocks for more complex knowledge structures. Early implementations like Wikifredia and Wikinoster demonstrate how these components can be leveraged to create collaborative knowledge bases that more closely mirror the interconnected nature of ideas.
The protocol's distinctive approach to community structure differentiates it from traditional platforms. Rather than enforcing rigid boundaries, Nostra enables a spectrum of closed and open communities that can intersect and interact in novel ways. This architecture reflects the real world dynamics of knowledge creation and sharing where different disciplines and schools of thought exhibit varying degrees of overlap and collaboration. The relay system within Noster provides an instructive model for understanding the organization and dissemination of knowledge, The ability to curate information sources and collaboration channels, particularly through the outbox model enables interactions beyond immediate shared networks analogous to how ideas propagate across traditional disciplinary boundaries.
Furthermore, Nostra's protocol level design, rather than platform specific implementation, creates opportunities for experimentation and specialization. The ability of different relays and clients to implement varying specifications enables the emergence of specialized knowledge communities, much as academic disciplines develop distinct methodologies and vocabularies. The interaction of these specialized communities may facilitate the evolution of new interdisciplinary languages and knowledge sharing structures. This examination of formal structure transitions naturally to an analysis of the efficient cause of knowledge, the processes driving its creation and evolution.
The protocol's decentralized and flexible architecture enables rapid iteration and adaptation in information organization and dissemination. It creates an environment where the traditional boundaries between knowledge consumers and producers become increasingly permeable, fostering participatory modes of learning and discovery. The following chapter will examine these processes in detail, exploring how the interplay between rhizomatic structures and diverse interfaces drives knowledge creation and transformation. Understanding these mechanisms enables more effective cultivation and collective intelligence, expanding the possibilities for collaborative knowledge generation and curation.
Chapter three, efficient cause, the process of knowledge and evolution. Knowledge itself demonstrates properties remarkably similar to biological organisms. Just as living systems engage in metabolism, growth, and adaptation, knowledge undergoes continuous processes of creation, integration, and evolution. This conception of knowledge as a living process provides a framework for understanding how ideas emerge, stabilize, and transform over time, revealing patterns that mirror those found in biological systems. The metabolic process of knowledge creation.
The creation and evolution of knowledge can be understood through the metaphor of metabolism, where new information is digested and integrated into existing knowledge structures. This process, like biological metabolism, involves both the breaking down of complex ideas into manageable components and the building up of new understanding. Through this lens, we can better understand how knowledge systems maintain themselves while growing and adapting to new conditions. New knowledge often begins as someone perceives a pattern, anomaly, or possibility that others have not yet recognized.
These early ideas tend to be fragile. They may be incomplete, poorly articulated, or challenge existing assumptions. When shared with others, these nascent ideas often face skepticism, dismissal, or active pushback based on others' established understanding and beliefs. This initial resistance is an important part of the knowledge validation process. Some ideas will fail this scrutiny, while others will be refined and strengthened through critical examination and evidence gathering until they become more widely accepted as valid contributions to our collective knowledge.
This initial fragility serves an essential function in the knowledge ecosystem. Just as biological systems require variability for evolution, the malleability of new ideas allows for rapid adaptation and refinement before they become firmly established. Through this process, knowledge systems can explore new possibilities while maintaining their essential structure and function. As knowledge matures, it undergoes a process of stabilization and integration just as nutrients are used to build the scaffolding of an organism. This occurs through the interplay of verification, validation, and community adoption.
Ideas are tested through peer review and empirical investigation, refined through academic discourse, and gradually integrated into existing theoretical frameworks. This process of stabilization doesn't occur in isolation, but rather through the complex interactions of academic and professional networks, educational systems, and practical applications. The translation problem in knowledge evolution. A fundamental tension exists between knowledge's rhizomatic form and our attempts to capture and transmit it through formal structures. This tension, which we might call a translation problem, lies at the heart of knowledge evolution.
Knowledge itself exists as resistant to complete formal representation. It possesses no clear boundaries between concepts, maintains multiple overlapping relationships, and constantly evolves in response to new inputs and contexts. Our need to communicate and preserve knowledge demands that we attempt to capture it in more formal structures. We create discrete units of knowledge, bounded definitions, and hierarchical organizations. This process of formal encapsulation, while necessary for transmission and preservation, inevitably simplifies and constrains the living complexity of knowledge.
The relationship between these two forms, the living rhizome and its formal representation, creates what might be called a modeling relation from Robert Rosen, an attempt to capture the complexity of living knowledge in simpler, more manageable forms. The very process of turning ideas contained in our minds to words, spoken or otherwise, is necessarily a transformative process, a process that is not reversible. The transmission of knowledge involves multiple layers of translation and interpretation, each adding complexity to the evolution of ideas. Individual understanding is shaped by personal experience, cultural context, and domain expertise.
When knowledge is transmitted, it must be translated from its rhizomatic form into linear communication, then reconstructed by the receiver through their own conceptual framework. This process of translation and reconstruction continually reshapes the knowledge itself contributing to its evolution. Dynamics of Knowledge Change The evolution of knowledge follows patterns that mirror those found in biological systems, exhibiting both gradual change and sudden transitions. Most commonly, knowledge evolves through gradual accumulation, the slow refinement of existing theories, the addition of supporting evidence, and the progressive specialization of fields.
This process resembles the gradual adaptation of organisms through natural selection. However, knowledge systems also experience periods of rapid change analogous to punctuated equilibrium in biological evolution. These episodes often coincide with paradigm shifts, revolutionary discoveries, or technological breakthroughs that fundamentally alter how we understand and interact with knowledge. The emergence of new fields or disciplines often occurs during these periods of rapid change as existing knowledge structures reorganize themselves around new insights or methodologies.
The evolution of knowledge is shaped by fundamental tensions between stability and innovation, between the need for reliable, verified knowledge and the importance of novel insights and perspectives. Similarly, knowledge systems must balance the benefits of deep specialization with the need for cross disciplinary integration and synthesis. These tensions create dynamic patterns of change that contribute to the overall resilience and adaptability of knowledge ecosystems. Further Directions and Duplications As we improve our understandings of knowledge and how we interact with it, we will need to develop tools and systems that can better support the natural dynamics of knowledge evolution.
Tools that can capture patterns, track the development of ideas, and facilitate processes of knowledge creation that is capable of maintaining semantic meaning aligned and approximated to the representation contained within the author's mind. More fundamentally, we need to re conceptualize our relationship with knowledge itself. This means creating environments that support both the stability needed for reliable knowledge and the flexibility required for innovation and adaptation. Chapter four, final cause, the purpose of knowledge and knowledge ecosystems. Knowledge serves as our guiding star, illuminating the path forward and enabling us to navigate the complexities of our world.
At its core, knowledge fulfills a fundamental purpose. It acts as a catalyst for adaptive behavior, drawing organisms, including humans, towards information that is meaningful and beneficial for their survival and thriving. However, the abstract nature of knowledge presents a significant challenge. We cannot directly manipulate or interact with knowledge in its pure, conceptual form. Instead, we engage with knowledge that has been captured and formalized in language or other representational systems. This limitation necessitates the development of interfaces and methods for knowledge interaction, leading to the creation of knowledge bases and other knowledge management systems.
The purpose of knowledge bases then extends beyond mere storage and retrieval of information. They serve as bridges between the abstract subjective knowledge and the concrete decision making. Effective knowledge bases facilitate the efficient transmission of knowledge, enabling users to navigate the information landscape, synthesize ideas to generate new insights, update and refine existing knowledge, and collaborate in the collective development of understanding. To fulfill its purpose, a knowledge base must strive to mirror the inherent structure of knowledge itself. This is where the concept of rhizomatic knowledge structures becomes crucial.
Knowledge, in its natural state, resembles a rhizome, a non hierarchical, multiply connected network of ideas and concepts. Unlike the rigid tree like structures often imposed on information, rhizomatic structures allow for dynamic growth, multiple entry points, and unexpected connections between disparate elements. The most effective knowledge bases, therefore, are those that conform to this rhizomatic structure. They should offer non hierarchical organization that allows for multiple pathways through information, dynamic linking capabilities that reveal unexpected connections between ideas, flexibility to accommodate new information without disrupting existing structures, and support for collaborative knowledge creation and curation.
Interestingly, the affordances provided by Nostr align remarkably well with these requirements for rhizome like knowledge bases. Nostra's decentralized open protocol offers several features that make it highly amenable to creating rhizomatic knowledge structures. Decentralized nature, mirroring the distributed nature of knowledge itself, Nostra's decentralized structure allows for multiple entry points and perspectives. Once a structure has been defined, anyone can take the existing structure, modify it, and build their own garden of knowledge with their own rules for contribution.
The decentralized and permissionless nature of Nostr allows for anyone to continue the conversation, build upon, and modify the existing structure while still maintaining the integrity of the original content and being interoperable with other knowledge bases. The two data specifications for a Nostr knowledge base, Nostr knowledge base implementation possibilities, n k b I p o one and n k b I p o two. Provide a foundation for creating structured knowledge bases that, when coupled with the decentralized nature of Nostr, can enable the creation of knowledge that aligns with the intrinsic rhizomatic structure of knowledge.
N k b I p o one enables for the creation of knowledge as a rhizomatic structure. Taking influence from zettelkasten, a popular note taking method, users can link ideas in a variety of ways, of which a linear assembly is one such possibility. Paired with other affordances such as tags, labels, external ID, forking of notes, knowledge can be navigated in a nonlinear way that where every note can be a starting point for new explorations. N k b I p zero two defines a specification for AI embeddings, which can be used for context free navigation. The creation of numerical representations of text allow for distance calculations between the semantic relationships of notes.
What this means is that even if the authors have not explicitly linked ideas together, embedding distances allow for a formal process of grouping disconnected ideas. What this implies is that a user who is interested in learning about a topic can be recommended notes that are semantically similar even if they are not directly linked. Additionally, just like GitHub Copilot, the AI system can take the context of already existing text and notes and synthesize new knowledge. It is important to note that these tools must be used in conjunction with human curation as the AI system must be guided in such a way that is meaningful to the writer and potential readers.
By leveraging these affordances, we can create knowledge bases that not only store information but actively support the expansion of human understanding and capability. Such systems would go beyond traditional static repositories to become dynamic environments of exploration, discovery, and the generation of new insights. Continuing to develop and refine knowledge bases, our goal should be to create interfaces that emulate the dynamic, adaptive nature of knowledge itself. These systems should support prediction, enable action, and facilitate learning in ways that mirror our cognitive processes.
They should be accessible, comprehensible, interconnected, and adaptable allowing users to navigate written knowledge with unprecedented ease and insight regardless of background. In conclusion, the purpose of knowledge is to guide adaptive behavior and enable effective interaction with our environment. This is fundamentally intertwined with the purpose of knowledge bases. Through the strive to create knowledge bases that mirror the rhizomatic structure of knowledge itself, we can develop tools that not only store and organize information, but actively support the growth and evolution of human understanding.
Nostr and its rhizome friendly affordances offer exciting possibilities for realizing this vision, potentially revolutionizing how we interact with and build upon our collective knowledge. So that was Liminal's article titled Understanding Knowledge and whoop de doo, man. Yeah. And I know it was it's a lot to take in. You know? We don't normally on this show talk about, you know, subjects as deep as what knowledge itself actually is and what structure it takes and the fact that you kinda can't directly interact with knowledge until after it's been represented in some symbolic form like letters or, you know, having a discussion with your buddy or listening to a podcast.
Those are all symbolically or symbologic instantiations of particular points of knowledge that particular people have. So why am I even bringing this to you today? Well, it's because I'm a big Nostra user, and this was essentially all about how Liminal was telling you that the very structure of Nostr, the way it exists right now, without with with almost no iterate you know, with no not even having to iterate on its structure at all is already able to be a receptacle of this ability to store, categorize, mix and match different pieces of knowledge from different authors. Some may be collaborating together.
Other authors may actually be on to the same you know, be looking at the same idea, but they don't even know each other. They don't know that they're looking at at the same idea. And then we get into where Liminal brings up the notion of bringing in artificial intelligence. What he's really talking about, and me and him have talked about this on the show before, and that was, in fact, episode nine thirty seven of Bitcoin and I'll if I remember, I'll try to put the link directly to that episode in the show notes. The ability for AI to start looking at different notes.
And sure, I know what you're saying. Nobody wants nobody really is gonna care about the shit posting. There are other notes that are out there. Right? And and and we're also, you know, not we're we're also not thinking of the capability of saying, well, me making a shitpost probably is so semantically divergent from the idea of, I don't know, solar processes that maybe some other Nostra user decided to write a small essay on. How does Stellar Dynamics work? Right? My ship posting is gonna be so distant away from that that the that if we do crawl this entire network of knowledge with an AI, it's probably just gonna throw it out. It's gonna throw out memes. It's gonna it's just gonna be going through. It's like like, think of it like some kid looking for something that they lost in a big old trunk upstairs in the attic, and they're just chucking crap out of the you looking for what chucking crap out of the looking for what it is that they want. I mean, that's what that's sort of where Liminal's getting. And I'm I know that I'm not touching as deep down as what what Liminal is thinking in his mind, but that is exactly the essence of what he's talking about. I'm not Liminal.
I do not see exactly how he's thinking about using AI to connect possibly different threads or context, the semanticism of notes. I I'm not in his mind. I cannot touch his knowledge. The only thing that I can touch is what he wrote here and what he's writing about AI. So I have to interpolate. I have to, like, expand what I think he's talking about, which causes me to do what? Make a whole bunch of assumptions. And in many cases, you kinda have to. And that way, when Liminal hopefully, he'll listen to this because he I haven't told him that I'm gonna be reading this on the show today, so I I will let him know that I've done so. But Liminal, if you're listening to this, I kinda get what you're talking about here.
You have to symbolically reflect what it is you're thinking in the form of the written word, and I've got to ingest that. And then I make assumptions about it, which can make an ass out of you and me. But when you hear this and you say, that's not at all what I was thinking. Let me explain it further to you. Or maybe I did get part of it, but I know I'm not getting all of it. I'm not in your head. But I do I think that I feel at a fundamental level what you're talking about when it comes to knowledge. What's in my head about things like like the Forest Walker idea that I have?
Man, I like I like, Guy Swan read it on his podcast, and he picked apart a couple of good problems. But he didn't even he didn't even touch the surface of just how many problems there is with that system. But I had to actually formalize it into words. And once I formalize it into words and I heard Guy Swan read it back to me, I was like, I now I've gotta go through it. I gotta go do that one, and I've got to address this. And, oh, by the way, he didn't think about what happens in the wintertime, is what happens if the wood is too wet because that can that can actually be a thing. You know, there's all manner of issues about the Forest Walker, but it's all in my head. And if I don't formalize it in a symbol logical way, like writing it out in in symbols in the form of letters of the alphabet, right, then there's no hope of having any of that knowledge restructured by somebody else. Or the fact of me writing it down does something magical to what's in my head. It causes it to exist outside of me.
If I die tomorrow, the Forest Walker article is out there. At least one other person has heard that article because Guy Swan read it on his podcast. You guys have heard me talk about it before. But the idea is out there. It's it's a baby. It's walking around. But when we take it back to Noster, that article, I can get to that article on Noster, and I can't remember which NIP it uses, but it was I I published it I published Forest Walker in a couple places, and one of them I did on highlighter, Pablo f seven z. Right? Now that nip is a long form nip. It's like if if your relay isn't reading, like, kind one is only reading kind one notes, then you're not seeing that article because it is not a kind one note.
But that note is out there, and it is available by somebody who's reading that particular kind of NIP on their relay. Right? So it exists, and I can die tomorrow, and it will still exist. I pulled something out of my head. Now other people can attach to it. And like like what Liminal's saying, what if we have a AI crawl it? The Noster network. Just start crawling and say, okay. Given what you've said here in this Forest War Walker article, you know, there was a guy over here that wrote this other article about biochar, and y'all both use the word biochar.
Maybe that is semantically close enough that I should start looking deeper. And I when I say I, I mean, I as an AI that's crawling the system. Maybe I should look deeper and see if there's any other connections. And maybe the guy uses the word forest. And maybe the guy, you know, ends up using the words, oh, God, wood vinegar. Maybe the guy uses something like data collection. Well, shit. At that point, we start getting very, very semantically close. Even though I have no idea what this theoretical article said, I don't know the author that wrote it. The fact that we are becoming very, very, you know, very much closer because we're using a lot of the same words, then maybe we get a semantic distance, a value that's assigned to those two notes as a connect as a bridging connection that says, I don't know, something like a short distance, which means these are very similar and some other author may pick that up and say, you know, scan the Nostra network and look for all semantic differences with a distance of no more than, I don't know, x.
Right? And then a filter is enacted, and then every they can see all these things and say, okay. Well, here's all these connected notes. Let's go through them and see what I can dig up. Let's go gardening. Let's go harvesting. Maybe they start looking at these notes in an interconnected ways and then start looking at it from a completely different lens and comes up with something of their own, either completely novel or helping me and this other author get our shit together because we were wrong about whatever. Right? This is what Liminal's talking about.
And I saw this a long time ago. I saw this he calls it affordances. And he's that's a great word to use. Nostra affords the ability for us to look at notes and their interconnectedness in this very, not only fundamental way, but a highly utilitarian way. And I don't mean utilitarianism in the old you know, if somebody's thinking, oh, yours sounds like Soviet Russia. No. No. No. I'm actually talking about utility, something I can use, something that brings, you know, a better, you know, a better way of thinking for me or something. Like, teaches me, like, four notes that if I take them all together, teaches me exactly how to go garden, I don't know, golden beets.
That you know, maybe I like golden beets. Maybe I've never been able to grow golden beets. But some somehow or another, by asking AI to filter out all the Nostra network except everything that is about beets and and and gardening beets and and make sure that semantic differences are no more than this x amount of distance away so that I can collect up very close notes. Maybe it teaches me how to grow golden beets, and I start growing them and it's completely successful. It actually, from that standpoint, changes my life. I mean, in this particular example, it's kinda dumb because it's just golden beets. Shit. You can go get that at the store, but you get my point. We're playing with Nostra like it's a toy right now. It's not a toy. It's Nostra is so far from being a toy, it's not even funny. And this is why I get really, really miffed when people shit all over Nostra because they're like, it's not as good as Twitter.
Twitter has no freaking hope to scale knowledge at all. It'll scale Grok's knowledge, and that knowledge will make a shit ton of money for the people over at at x or whatever, but it's not going to expand human knowledge. It doesn't have the capacity, and neither does Facebook, and neither does Instagram, and neither does TikTok. None of these things. Because they are all so centralized. The only other thing that I could think of is possibly the Feddy verse, but that is so filled with trite bullshit that it's not even funny. And it's a real pain in the ass to jump from one of their servers to another. And and from that standpoint, it's not a walled garden. It's like a million walled gardens all contained in a something that they would call an open garden, but it's not really all that easy. Nostr is the only thing that has, as Liminal says, the affordances to be able to mimic a structure that would provide us a pathway to discover new knowledge.
Yeah. It's okay. Post memes. I think memes are hilarious. I think it's funny when people shitpost. It doesn't mean that I don't find value in it. I actually do. There's nothing better than having somebody make you laugh when you've had a shitty day. That's of real value. Maybe there's some semanticism that can be discovered there by somebody who wants to be a stand up comic. Hey. Go show me all the shitposts. How are how are all these shitposts connected? How's Korn DeLorean shitpost, you know, semantically close to this this other author's shitposting?
That's not without value. This all exists inside this structure that Fiat Joff gave us. And even fiat Joff sometimes, I think, doesn't like Noster. But who knows what fiat Joff is actually kinda crazy. It's okay. I love that I he it's it's like Shia Labeouf. They're both batshit crazy, and I don't care. I love them both. I don't care what anybody thinks about me liking Shia Shia Labouf because he's, like, just hated Trump. I don't care. I don't care. I don't care. Fiat Joffe has said on several occasions some bad shit about his own creation, and people got got mad at him. I don't care. He's batshit crazy, and I love him to death for it.
Only a batshit crazy person could come up with something like Nostr. Only liminal could come up with something that I just revved you. So with that said, I really, really am desperate for people to stop looking at Nostril like it's simply a social media platform. It's not. It is probably going to become one of the most extensive knowledge bases that humankind has ever come to know, and Fiat Joff can't even stop it. Oh, nobody's gonna use it. Yeah. We're I use it every day. I know hundreds of people that use Nostra every day.
And every single day, more and more knowledge gets poured into this thing. And people like Liminal and Lissarian, if I'm pronouncing her name right. She's one of the other developers on this project Alexandria. If they could just get some freaking grant funding and I really mean that. They've been rejected by a couple of different places, and I don't understand why. It is one of the most pivotal things in the human spirit to create knowledge, to organize knowledge. Remember all the way even if you're a Christian or not, remember all the way back to the story of Adam and Eve. What was Adam's job?
Adam had a couple of jobs. Basically, he had one real huge job. Him and Eve were supposed to name all of the creatures and plants and animals and trees and blades of grass. He was supposed to give everything in the Garden of Eden Eden a name. Honey, that's categorization of knowledge. That that's that is that is turning a thing that is amorphous into a static structure. By giving a tree a name, like it's a tree, I've turned this thing that I would have to normally describe by sight to somebody who is blind, instead of doing it that way, I could just say, ah, we both agree, we both look at this thing, and we both say, that's going to now be a tree.
Now when I go and tell somebody that I saw a tree, the idea forms in their mind through that one symbol. Nostra's way more important than people think it is, and it's kind of embarrassing that we're missing it. It's right here, right in front of us. It's going to be everything that Wikipedia thought it was going to be and could never be. It's going to be everything that Twitter thought it could be and is never going to be because it never could be. It never had the structure for it. Only Nostr has the structure to usher in what comes next. And you'll go, I I know. You'll say, David, I think you've just damn near lost your mind on this one. No. No. I really haven't.
We've been forced into thinking that McDonald's and Taco Bell is real food. It's not. It's bullshit. It's crap. It's garbage. It's poison. But if I go to a party and I say that, people are gonna look at me funny, aren't they? But I guarantee the people listening to my voice, most of you are nodding your head going, yes. It's poison. We've all we we're we're breaking out of this knowledge structure that we've been given through media since we were children. Probably even before we were children, we were probably hearing the bullshit from the news in our mother's wombs. We were probably already well on our way to being programmed to be in good little dogs and cats so that we could function in society and not cause problems.
You know, like that Gutenberg dude that invented the printing press and started printing of all things. The first thing that he printed was Bibles. He didn't he didn't print Odyssey and Iliad. He didn't print the tomes of Plato and Socrates and all that shit. No. He's like the Bible. The most disruptive thing that you could have done at the time that Gutenberg was doing his shit was to print the freaking Bible. Why? Because it was always, always only ever held by the people who owned the church. And if you wanted to know what God was actually saying in the book, you couldn't have one yourself, you had to go to church, which meant that knowledge was gatekept.
And it always has been. It's never changed until people like Gutenberg come along. And I always used to think that the World Wide Web was gonna be the printing press two point o, and I was so wrong because everything was gated, and it's become more so. And now we have Nostr, and yet we treat it like social media. Nobody sees any potential at all, apparently. All I hear about is people bitching that, you know, direct messages don't work. So what? The other shit works. Well, nobody's gonna read these these long ass notes. I do. I read this one. This one is in the form of a note on Nostra through project Alexandria.
If you have not gone and looked at what Liminal and LeSaren and a couple of other people are doing, you're really missing out on what the potential of all this actually is. Because like I said, right now, we're just like little kids playing with a a toy Ferrari on the floor. But what we're really playing with is a real Ferrari. We just haven't turned the key in the ignition yet. In fact, we can't even climb into the driver's seat yet because the door's closed and we're too small to reach the handle. But once that son of a bitch handle gets reached for and we open the door, and we sit our happy asses down, and we plug that freaking key into the ignition and fire that son of a bitch up, watch out.
A 80 miles an hour plus. Okay. Y'all, reach out to Liminal, Lycerin. I'm gonna put both of their, their Noster in pubs into the show notes. Let them know what you thought, if you have any ideas about what they're doing. And honestly, if you don't wanna do any of that, reach out to, OpenSats and ask them to fund Project Alexandria. I love Matt O'Dell. I really do. But they're one of the people that have denied a grant to Project Alexandria. And I for the life of me, I'm going to I'm not even gonna make any assumptions about the grant proposal and what its format was versus how Gigi and all the guys over at OpenSats read it. I'm not gonna make any assumptions at all as to why that the this granting opportunity has not come to Project Alexandria.
But I get the feeling that if done right, politely, respectfully, with kind words, and an appreciation for the project that maybe, just maybe, if they're Gigi, Matt, some of the other guys at at OpenSats, hears it enough in a again, in a positive light, not, hey. Screw you. David said you did you you didn't give no money. No. Don't do that shit. Just tell him what if you agree with me and you think that Noster is as important as it is and that Project Alexandria, which is part of what Liminal's discussing here in this piece, if you think it has the potential to unlock it and to crack an egg the likes of which we have never seen before, to get a hold of those guys and say, take a look at their grant proposal one more time, please.
Please. You do that, and I will see you on the other side. This has been Bitcoin, and and I'm your host, David Bennett. I hope you enjoyed today's episode episode and hope to see you again real soon. Have a great day.
Understanding Knowledge in a Decentralized World
Introduction: Knowledge as a Living Ecosystem
Challenging the Static Perception of Knowledge
Reimagining Knowledge as a Living Ecosystem
Material Cause: The Substance of Knowledge
Formal Cause: The Structure and Organization of Knowledge
Efficient Cause: The Process of Knowledge and Evolution
Final Cause: The Purpose of Knowledge and Knowledge Ecosystems
Reflections on Liminal's Article and Nostr's Potential
