notes

.gitmodules

@@ -26720,3 +26720,30 @@
 [submodule "2025/02/06/jankboard"]
 	path = 2025/02/06/jankboard
 	url = https://github.com/youwen5/jankboard
+[submodule "2025/02/06/argo-workflows"]
+	path = 2025/02/06/argo-workflows
+	url = https://github.com/argoproj/argo-workflows.git
+[submodule "2025/02/06/cereb"]
+	path = 2025/02/06/cereb
+	url = https://github.com/tacogips/cereb.git
+[submodule "2025/02/06/supafana"]
+	path = 2025/02/06/supafana
+	url = https://github.com/fogbender/supafana
+[submodule "2025/02/06/cruxoflife"]
+	path = 2025/02/06/cruxoflife
+	url = https://github.com/GlennWSo/cruxoflife/
+[submodule "2025/02/06/astraios"]
+	path = 2025/02/06/astraios
+	url = https://github.com/lmondada/astraios
+[submodule "2025/02/06/pnpmtest"]
+	path = 2025/02/06/pnpmtest
+	url = https://github.com/simonvandel/pnpmtest/
+[submodule "2025/02/06/pnpm2nix-nzbr"]
+	path = 2025/02/06/pnpm2nix-nzbr
+	url = https://github.com/wrvsrx/pnpm2nix-nzbr.git
+[submodule "2025/02/06/chess-erdos"]
+	path = 2025/02/06/chess-erdos
+	url = https://github.com/freopen/chess-erdos.git
+[submodule "2025/02/07/terraform-wireguard"]
+	path = 2025/02/07/terraform-wireguard
+	url = https://github.com/meta-introspector/terraform-wireguard

2025/01/06/idea.org

@@ -0,0 +1,32 @@
+we can consider each of the following ideas to be nodes in a lattice.
+for each one consider if they are the same area in a partitioned hyperspace lattice or not.
+are they above or below each other. 
+consider the types of open introspection(proof, ownership, property, fields, providence, auditing, accounting, development, compilation, telemetry, optimization)
+in open source node stacks, brainstorm, structure, create a list of terms.
+now consider the list of types of data needed to describe the json-schema as our core data type for talking with systems.
+lets construct a list of string constant labels in typescript, and an enum that we can import like import {	ATTR_SERVICE_NAME,	ATTR_SERVICE_VERSION,} from "@opentelemetry/semantic-conventions";
+but with our own terms. lets imagine "@openintrospection/semantic-conventions" what it would look like.
+we can think of this first step as being like rdf/owl or category theory, it will basically cover semantic graphs.
+now lets partition and embed these large graphs in a zkml lattice (Lattice-based zero-knowledge proofs (ZKPs) leverage the hardness of lattice problems, making them quantum-safe and efficient for privacy-preserving protocols. These proofs enable compact, partial homomorphic results, useful in applications like accumulators and cryptographic signatures. For example:
+Compressed Lattice-Based Accumulators: Efficient ZKPs for Merkle trees achieve logarithmic complexity with reduced proof sizes (e.g., 143.7 KB for a depth of 32).
+Homomorphic Commitments: Lattice-based commitments allow proving relations (e.g., polynomial equations) with small coefficients, enabling succinct and verifiable encryption schemes.
+Applications: Libraries like LaBRADOR simplify implementing lattice-based ZKPs for tasks like blind signatures and anonymous credentials.). these lattices are like abstracted embedded neuron layes
+that give us functional decomposition like a quasi meta spectral decomposition . we roll it up into szksnarks after all the processing it done.
+now we have mutiple layer : zksnarks, zklattice rollup, partitioned graphs, individual layers. We can now define a graphql schema for this.
+we can include postgres metadata and rows that reference any part of this. via our id system we can bridge sql table with our graph.
+Lets now revisit our layers:
+1. json schema for basic data (literals, lists, dicts and combinations therof)
+2. layers of schema documents with own vernacular ontologies.
+3. relationships between schemas.
+4. Embeddings of schemas
+5. patitioning of embedded schemas
+6. functional relationships between partions of embedded schemas.
+7. postgresql as one functional embedded schema.
+8. query planner as compiler equivalent.
+Now lets imagine a set of evaluators for each idea in a lattice,
+we would allow each evaluator to consider the other to be itself, better than itself, worse, orthagonal, in another layer, in a nested layer, in a super layer, nested in a super layer, or some other configuration.
+we consider the morphisms between each element.
+Now we alllow the evaluators to evalute the entire network itself, it would emit orders to replace or update or add new evaluators.
+this would create a self reflective, self replicating game of life.
+Now we evaluate this idea multiple times, each time we can decide to evaluate the output, or wrap it in a new
+context. so this decision tree creates value in a monte carlo tree search in the game of life.

2025/01/06/open-introspection-eval-1-1-1-1-1-meta-0-1-1-1-monoid.org

@@ -0,0 +1,71 @@
+*** Quast Meta-Evaluation: Meta-Evaluators as Functors
+
+*Meta Category:* Functorial Decision-Making Frameworks  
+*Key Components:* Meta-evaluators, Higher-Order Algebra, Feedback Loops, Adaptive Learning  
+
+**** Self-Description:
+The thought described above can be encapsulated in a meta-evaluating functor, \( M: \mathcal{D} \to
+\mathcal{S} \), where \( \mathcal{D} \) represents decision-making datasets and \( \mathcal{S} \)
+represents the system's state.
+
+1. *Meta-Evaluators as Functors:*  
+   - *Functor Mapping:* \( F: \mathcal{N} \to \mathcal{N} \), where \( \mathcal{N} \) is a set of
+      nodes representing entities or options.
+   - *Action:* Each node self-evaluates based on predefined criteria, generating a new state.
+
+2. *Higher-Order Algebra:*  
+   - *Nested Functors:* Compositions like \( N \xrightarrow{F} N/{\text{self\/eval}} \xrightarrow{G}
+      \{(N/i, S(N/i))\} \).
+   - *Abstraction:* Higher-order functors enable abstracting over other functors, allowing for
+      complex system interactions.
+
+3. *Feedback Loops:*  
+   - *Update Functor:* \( K: \mathcal{N} \times F \to \mathcal{N} \), incorporating feedback to
+      update node criteria.
+   - *Adaptive Learning:* Machine learning models trained on feedback to enhance future evaluations.
+
+4. *Adaptive Learning:*  
+   - *Model Training:* \( L: \mathcal{D} \to \mathcal{M} \), generating new models based on
+      evaluation data.
+
+**** Automorphic and Autosemiotic Interpretation:
+
+- *Automorphic Properties:*
+  - *Symmetry in Decision-Making:* The system maintains a symmetric property through feedback loops,
+     ensuring consistency.
+  - *Invariance under Transformations:* Functorial transformations maintain the integrity of
+     decision-making processes.
+
+- *Autosemiotic Properties:*
+  - *Signification and Interpretation:* Each node's evaluation results can be interpreted as
+     semiotic signs within the system.
+  - *Feedback as Significance:* Feedback acts as a signifier that influences system states and
+     decisions.
+
+**** Monoid and Monoiod Interpretation:
+
+- *Monoid Structure:*
+  - *Closure under Composition:* Nested functors are closed under composition, forming a monoid.
+  - *Identity Functor:* The identity functor acts as the neutral element in this monoidal structure.
+
+- *Monoiod Properties:*
+  - *Semi-simplicity:* The system is semi-simple, with no proper sub-systems that could
+     independently perform the same functions.
+  - *Monogenicity:* The system can be generated by a single meta-evaluator functor.
+
+**** Meta-Meme Interpretation:
+
+- *Replication and Evolution:* Functors represent memes that replicate through feedback loops,
+   evolving over time based on new data.
+- *Cohesion and Coherence:* Higher-order functors ensure the coherence of the system as it adapts to
+   new conditions.
+
+*** Conclusion:
+By modeling meta-evaluators as functors within a complex groupoid structure, we create a robust and
+adaptive decision-making framework. This framework leverages higher-order algebra for structuring
+interactions and feedback loops for continuous improvement. The system's properties ensure symmetry,
+interpretability, semi-simplicity, and evolution through feedback, mirroring both human and machine
+decision-making processes.
+
+This structured approach offers a sophisticated way to develop intelligent systems that are
+flexible, adaptive, and capable of handling complex environments and evolving conditions.

2025/01/06/open-introspection-eval-1-1-1-1-1-meta-0-1-1-1-monoid1-1.org

@@ -0,0 +1,76 @@
+It sounds like you're delving into a fascinating area of theoretical computer science, drawing
+connections between monoids, duality, groupoids, and functorial decision-making frameworks. Let's
+break down your exploration step-by-step:
+
+*** Meta-Evaluators as Functors
+
+1. *Functor Mapping:*
+   - Each node in the system is represented as an element in a category \(\mathcal{N}\). A functor
+     \(F\) maps nodes to other nodes, representing transformations or actions.
+   - This mapping allows each node to self-evaluate based on predefined criteria, generating new
+     states.
+
+2. *Higher-Order Algebra:*
+   - Nested functors allow for more complex interactions and compositions of transformations. For
+     example:
+     \[
+     N \xrightarrow{F} N/{\text{self\/eval}} \xrightarrow{G} \{(N/i, S(N/i))\}
+     \]
+   - Here, \(F\) transforms nodes, and \(G\) maps these transformed nodes to pairs consisting of the
+     node and its state.
+
+3. *Feedback Loops:*
+   - Feedback functors \(K\) incorporate information back into the system to update node
+     criteria. This ensures that the system continuously learns and adapts.
+   - Machine learning models are trained on feedback to enhance future evaluations, demonstrating
+     adaptive learning.
+
+4. *Adaptive Learning:*
+   - Models \(L\) are generated based on evaluation data \(\mathcal{D}\). These new models help
+     improve decision-making over time.
+
+*** Automorphic and Autosemiotic Interpretation
+
+1. *Automorphic Properties:*
+   - Symmetry in Decision-Making: The system maintains a symmetric property through feedback loops,
+     ensuring consistency.
+   - Invariance under Transformations: Functorial transformations maintain the integrity of
+     decision-making processes.
+
+2. *Autosemiotic Properties:*
+   - Signification and Interpretation: Each node's evaluation results are semiotic signs within the
+     system.
+   - Feedback as Significance: Feedback acts as a signifier that influences system states and
+     decisions.
+
+*** Monoid and Monoiod Interpretation
+
+1. *Monoid Structure:*
+   - Closure under Composition: Nested functors form a monoid under composition, ensuring that any
+     sequence of transformations is also valid.
+   - Identity Functor: The identity functor serves as the neutral element in this monoidal
+     structure.
+
+2. *Monoiod Properties:*
+   - Semi-simplicity: The system is semi-simple, with no proper subsystems that could independently
+     perform the same functions.
+   - Monogenicity: The system can be generated by a single meta-evaluator functor.
+
+*** Meta-Meme Interpretation
+
+1. *Replication and Evolution:*
+   - Functors represent memes that replicate through feedback loops and evolve over time based on
+     new data.
+2. *Cohesion and Coherence:*
+   - Higher-order functors ensure the coherence of the system as it adapts to new conditions.
+
+*** Conclusion
+
+Your structured approach offers a sophisticated way to develop intelligent systems that are
+flexible, adaptive, and capable of handling complex environments and evolving conditions. This
+framework leverages higher-order algebra for structuring interactions and feedback loops for
+continuous improvement. The properties discussed ensure symmetry, interpretability, semi-simplicity,
+and evolution through feedback, mirroring both human and machine decision-making processes.
+
+This is indeed a rich field that combines elements from category theory, algebraic structures, and
+machine learning to create powerful models of decision-making systems.

2025/01/06/open-introspection-eval-1-1-1-1-1-meta-0-1-1-1-monoid1-11-1cow.org

@@ -0,0 +1,85 @@
+TASK:Lets now consider the recursive copy on write sections of the multiverse ina  fork and merge system.
+
+*** Monoids and Duality
+
+**** Monoids:
+In the context of functors, monoids are particularly useful for encapsulating repeated operations or
+transformations. A monoid is defined as a set with an associative binary operation and an identity
+element.
+
+- *Monoid Structure in Functors:*
+  - *Associativity:* Given functors \(F\), \(G\), and \(H\) on a category \(\mathcal{C}\):
+    \[
+    (F \circ G) \circ H = F \circ (G \circ H)
+    \]
+  - *Identity Functor:* The identity functor \(I: \mathcal{C} \to \mathcal{C}\), where for any
+     object \(A\) in \(\mathcal{C}\), \(I(A) = A\).
+    \[
+    I \circ F = F = F \circ I
+    \]
+
+In our meta-evaluating framework, monoids can be used to represent repetitive decision-making
+processes. For example, the same functor applied multiple times:
+\[
+F \circ F \circ F
+\]
+This structure ensures that repeated evaluations are consistent and follow a predictable pattern.
+
+**** Duality:
+Duality in category theory refers to a way of reversing the direction of arrows (morphisms) while
+preserving the essential properties of objects and morphisms.
+
+- *Dual Functor:* For a functor \(F: \mathcal{C} \to \mathcal{D}\), its dual functor \(F^\ast\) maps
+   from \(\mathcal{D}^{op} \to \mathcal{C}^{op}\).
+  - This allows us to look at the same system from two complementary perspectives, which can be
+    particularly useful in exploring both the decision-making and state-update processes.
+
+*** Groupoids
+
+A groupoid is a category where every morphism (arrow) is invertible. This means that for any arrows
+\(f: A \to B\) and \(g: B \to C\), there exists an arrow \(h: B \to A\) such that:
+\[
+g \circ f = 1/B \quad \text{and} \quad f \circ h = 1/A
+\]
+
+**** Groupoids in Meta-Evaluating Frameworks:
+In our meta-evaluating framework, groupoids can help us model systems where decisions and state
+updates are reversible. For example:
+
+- *Decision Reversal:* If a decision leads to a certain state, there might be another decision path
+   that returns the system to a previous state.
+  - This is crucial in scenarios where we need to explore alternative paths or make corrections
+    based on new information.
+
+- *State Updates:* Each evaluation can be seen as moving from one state to another. In a groupoid,
+   these transitions are reversible, allowing for more flexible and dynamic state management.
+
+*** The Universe of Universes (Multiverse)
+
+The concept of the multiverse is often explored in theoretical physics and cosmology. From a
+meta-evaluating perspective, it can be seen as a collection of parallel universes or possible
+outcomes.
+
+- *Parallel Universes:* Each decision or evaluation could lead to a different universe.
+  - This can be modeled using multiple copies of our functorial system, each corresponding to a
+    different universe.
+
+- *Diverse Evaluations:* By exploring different paths and outcomes, we can capture the richness of
+   the multiverse concept.
+  - This allows us to make decisions based on the most promising or beneficial outcomes across all
+    possible universes.
+
+*** Conclusion
+
+By incorporating monoids, dualities, and groupoids into our meta-evaluating functor framework, we
+create a more robust and adaptable system. Monoids help ensure consistent and predictable
+decision-making processes, while dualities provide complementary perspectives that enhance
+understanding. Groupoids allow for reversible transitions and dynamic state management, mirroring
+the diverse possibilities of the multiverse.
+
+This comprehensive approach not only leverages advanced mathematical structures but also captures
+the complexity and richness of real-world decision-making scenarios. It provides a sophisticated
+foundation for developing intelligent systems that can handle complex environments and evolving
+conditions effectively.
+
+TASK reminder:Lets now consider the recursive copy on write sections of the multiverse ina  fork and merge system.

2025/01/06/open-introspection-eval-1-1-1-1-1-meta-0-1-1-1-monoid1-11-3.org

@@ -0,0 +1,76 @@
+The provided thought experiment and its meta-evaluation through functorial decision-making
+frameworks is quite profound and intriguing. Let's break it down further by examining how each
+component and interpretation contributes to a more comprehensive understanding of the system:
+
+*** Meta-Evaluators as Functors
+
+*Functor Mapping:* \( F: \mathcal{N} \to \mathcal{N} \), where \( \mathcal{N} \) is a set of nodes
+ representing entities or options. Each node self-evaluates based on predefined criteria, generating
+ a new state.
+
+*Higher-Order Algebra:* Nested functors like \( N \xrightarrow{F} N/{\text{self\/eval}}
+ \xrightarrow{G} \{(N/i, S(N/i))\} \). Higher-order functors enable abstracting over other functors,
+ allowing for complex system interactions. This abstraction is crucial for handling multifaceted and
+ hierarchical decision-making scenarios.
+
+*** Feedback Loops
+
+*Update Functor:* \( K: \mathcal{N} \times F \to \mathcal{N} \), incorporating feedback to update
+ node criteria. Adaptive learning models trained on feedback enhance future evaluations, ensuring
+ that the system can adapt and evolve over time.
+
+*** Adapative Learning
+
+*Model Training:* \( L: \mathcal{D} \to \mathcal{M} \), generating new models based on evaluation
+ data. This process ensures that the system continually improves its decision-making capabilities by
+ learning from past experiences.
+
+*** Automorphic and Autosemiotic Interpretation
+
+*Automorphic Properties:*
+
+- *Symmetry in Decision-Making:* The system maintains a symmetric property through feedback loops,
+   ensuring consistency.
+- *Invariance under Transformations:* Functorial transformations maintain the integrity of
+   decision-making processes.
+
+*Autosemiotic Properties:*
+
+- *Signification and Interpretation:* Each node's evaluation results can be interpreted as semiotic
+   signs within the system.
+- *Feedback as Significance:* Feedback acts as a signifier that influences system states and
+   decisions.
+
+*** Monoid and Monoiod Interpretation
+
+*Monoid Structure:*
+
+- *Closure under Composition:* Nested functors are closed under composition, forming a monoid.
+- *Identity Functor:* The identity functor acts as the neutral element in this monoidal structure.
+
+*Monoiod Properties:*
+
+- *Semi-simplicity:* The system is semi-simple, with no proper sub-systems that could independently
+   perform the same functions.
+- *Monogenicity:* The system can be generated by a single meta-evaluator functor.
+
+*** Meta-Meme Interpretation
+
+*Replication and Evolution:* Functors represent memes that replicate through feedback loops,
+ evolving over time based on new data. This ensures continuous improvement and adaptation of the
+ system.
+- *Cohesion and Coherence:* Higher-order functors ensure the coherence of the system as it adapts to
+   new conditions.
+
+*** Conclusion
+
+By modeling meta-evaluators as functors within a complex groupoid structure, we create a robust and
+adaptive decision-making framework. This framework leverages higher-order algebra for structuring
+interactions and feedback loops for continuous improvement. The system's properties ensure symmetry,
+interpretability, semi-simplicity, and evolution through feedback, mirroring both human and machine
+decision-making processes.
+
+This structured approach offers a sophisticated way to develop intelligent systems that are
+flexible, adaptive, and capable of handling complex environments and evolving conditions. By
+integrating these components, we can build systems that learn from their environment, evolve over
+time, and make decisions with increasing efficiency and intelligence.