Design

[jmikedupont2]

Sure, here's how you could split the visualization into two documents, one focusing on GraphQL and the other on the introspector model:

Document 1: GraphQL

  🧬
  🔍       📄       📋
      📁        📁
  🔄      📦       🚀
       🔗

     GraphQL

📄: GraphQL Schema Document
🔍: Query (Search for Data)
📋: Mutation (Change Data)
📁: Type (Define Custom Types)
🔄: Mutation (Update Data)
📦: Subscription (Real-Time Updates)
🔗: Link (Connect Data)

Document 2: Introspector Model

   🌱     🚀    🧩
     🔍
  🔄       📁      🔄
       🧬

  Introspector Model

🌱: N-Gram (Grouping from Introspector Model)
🚀: Morphism (Transformation from Introspector Model)
🧩: Rewrite Rule (Modification from Introspector Model)
🔍: Query (Access Data)
📁: Type (Custom Type Definition)
🔄: Mutation (Data Update)
🧬: Introspection (Understanding Relationships)

These two documents represent the separation between GraphQL and the introspector model, emphasizing the interactions and relationships within each domain. Feel free to adjust the arrangement or content as needed to best convey the information.

[jmikedupont2]

Using Git for data transfer and incorporating multiple layers of encoding for reification is an interesting approach. By leveraging Git's version control capabilities and combining them with your encoding process, you can create a structured and traceable way to manage and exchange data. Reifying the data through encoding adds an additional layer of abstraction and can help ensure the integrity and security of the information.

Here are a few points to consider as you move forward with this approach:

1. Version Control: Git's version control will allow you to track changes and maintain a history of your data. This can be especially useful when working with decentralized systems, as each participant can have a copy of the repository.

2. Encoding Layers: Make sure to have a well-defined and documented process for encoding and decoding the data at each layer. This will ensure that participants can easily understand and interpret the information.

3. Data Security: While Git offers version control, you should still consider the security of the data you're sharing. Ensure that sensitive information is appropriately encrypted or obfuscated, especially if it's stored in a public repository.

4. Collaboration: Since Git supports collaboration, multiple participants can contribute to the repository. Establish clear guidelines and workflows to manage contributions and resolve conflicts.

5. Documentation: Maintain clear documentation that outlines the purpose, structure, and encoding process of the data. This will help others understand how to interact with the repository.

6. Backup and Recovery: Implement backup and recovery strategies to ensure the availability of data in case of unforeseen events or data loss.

7. Testing: Before fully implementing your approach, consider testing it with a small-scale pilot project to identify any potential issues or challenges.

Remember that while this approach offers flexibility and decentralization, it may also introduce complexity. Ensuring that participants understand the encoding process and can navigate the repository effectively will be crucial for its success.

[jmikedupont2]

Creating a centralized entry point like run.sh or run.bat to manage and automate your processes is a practical way to abstract from GitHub and Docker while providing a streamlined experience for users. This approach simplifies the execution of your workflows and allows users to interact with your system using a single command. Here's how you might structure such an entry point:

1. Automation Script: Create a shell script (for Unix-like systems) or a batch script (for Windows) that automates the entire process. This script will encapsulate the steps required to manage your data, including encoding, pushing to Git, and handling data interactions.

2. Steps and Actions: Within the script, define each step and action that needs to be performed. These steps can include encoding data, interacting with Git, and executing various processes. Break down the workflow into manageable tasks.

3. Configuration: Include any configuration variables or settings that your system requires. This could include repository URLs, file paths, authentication tokens, and any other parameters necessary for the tasks.

4. User Input: Consider providing options for user input within the script. For instance, users might specify parameters or choose specific actions to perform by providing command-line arguments or prompts.

5. Logging: Implement logging within the script to capture important information, errors, and progress updates. This can be useful for troubleshooting and monitoring the execution.

6. Dependencies: Ensure that the script checks for dependencies and prerequisites before proceeding. If certain tools or software are required, the script can verify their presence and provide installation instructions if needed.

7. Documentation: Include comments in the script to explain each step and action. This will help users understand what the script is doing and how to use it effectively.

8. Error Handling: Implement error handling and exit codes to handle unexpected scenarios gracefully. Provide meaningful error messages to guide users in resolving issues.

9. Execution: Users can run the script from the command line by invoking ./run.sh (Unix) or run.bat (Windows). If needed, you can also make the script executable with appropriate permissions.

By providing an entry point like this, you're offering users a convenient way to interact with your system without needing to understand the underlying intricacies of GitHub, Docker, and other components. It centralizes the process and abstracts away complexities, making it more user-friendly. Just be sure to provide clear documentation for users who may need to use or customize the script.

[jmikedupont2]

Creating a centralized entry point like run.sh or run.bat to manage and automate your processes is a practical way to abstract from GitHub and Docker while providing a streamlined experience for users. This approach simplifies the execution of your workflows and allows users to interact with your system using a single command. Here's how you might structure such an entry point:

1. Automation Script: Create a shell script (for Unix-like systems) or a batch script (for Windows) that automates the entire process. This script will encapsulate the steps required to manage your data, including encoding, pushing to Git, and handling data interactions.

2. Steps and Actions: Within the script, define each step and action that needs to be performed. These steps can include encoding data, interacting with Git, and executing various processes. Break down the workflow into manageable tasks.

3. Configuration: Include any configuration variables or settings that your system requires. This could include repository URLs, file paths, authentication tokens, and any other parameters necessary for the tasks.

4. User Input: Consider providing options for user input within the script. For instance, users might specify parameters or choose specific actions to perform by providing command-line arguments or prompts.

5. Logging: Implement logging within the script to capture important information, errors, and progress updates. This can be useful for troubleshooting and monitoring the execution.

6. Dependencies: Ensure that the script checks for dependencies and prerequisites before proceeding. If certain tools or software are required, the script can verify their presence and provide installation instructions if needed.

7. Documentation: Include comments in the script to explain each step and action. This will help users understand what the script is doing and how to use it effectively.

8. Error Handling: Implement error handling and exit codes to handle unexpected scenarios gracefully. Provide meaningful error messages to guide users in resolving issues.

9. Execution: Users can run the script from the command line by invoking ./run.sh (Unix) or run.bat (Windows). If needed, you can also make the script executable with appropriate permissions.

By providing an entry point like this, you're offering users a convenient way to interact with your system without needing to understand the underlying intricacies of GitHub, Docker, and other components. It centralizes the process and abstracts away complexities, making it more user-friendly. Just be sure to provide clear documentation for users who may need to use or customize the script.

[jmikedupont2]

Absolutely, it sounds like you're looking to build a lightweight Continuous Integration/Continuous Deployment (CI/CD) system that integrates with GitHub events and abstracts the complexities via interface layers and monads. Here's how you might approach this:

1. Event Triggering:

   • Configure your CI/CD system to listen to GitHub events using webhooks or GitHub Actions.
   • Set up event triggers for specific actions in your repository, such as pushes, pull requests, or releases.

2. Abstraction Layers:

   • Create abstraction layers that encapsulate the logic for different CI/CD tasks. Each abstraction layer can represent a specific workflow or action you want to perform.
   • These abstraction layers can be designed using monads, which are functional programming constructs that allow for chaining and sequencing of actions in a structured manner.

3. Task Definitions:

   • Define tasks as abstract actions within your CI/CD system. For example, you might have tasks like "Build," "Test," "Deploy," etc.
   • Implement each task as a separate abstraction layer, handling the specific logic and steps required for that task.

4. Source Binding:

   • Bind the abstraction layers to the source (GitHub) using event handlers. When a GitHub event is triggered, the associated event handler will invoke the relevant abstraction layer.
   • This source binding ensures that the CI/CD system responds to GitHub events and executes the appropriate tasks.

5. Error Handling and Logging:

   • Implement error handling and logging mechanisms within each abstraction layer. This helps capture errors, provide detailed information, and facilitate debugging.

6. Event Data and Parameters:

   • Extract data and parameters from GitHub events to guide the behavior of your abstraction layers.
   • This might involve parsing payload data from events like pull requests, commits, and releases.

7. Feedback and Notifications:

   • Implement feedback and notification mechanisms to keep users informed about the status of CI/CD tasks.
   • Notifications can be sent via email, Slack, or other messaging platforms.

8. Customization and Configuration:

   • Allow users to customize and configure the behavior of the CI/CD system by providing configuration files or environment variables.

9. Testing and Validation:

   • Test your abstraction layers and monadic constructs to ensure they work as expected and handle various scenarios.
   • Validate the entire CI/CD system by simulating different GitHub events and monitoring the outcomes.

10. Documentation:

    • Provide comprehensive documentation on how to set up and use your lightweight CI/CD system. This includes instructions for configuring GitHub events, understanding the monads and abstraction layers, and customizing the behavior.

By designing your CI/CD system with abstraction layers and monadic constructs, you're creating a structured and modular approach that abstracts away the underlying complexities of GitHub events and CI/CD tasks. This approach enhances maintainability, flexibility, and ease of use.

[jmikedupont2]

Understood. It sounds like you're envisioning a decentralized and abstracted architecture where your system interacts with external services and tools through reified interfaces, essentially acting as intermediaries or "minions." This approach allows you to communicate with external systems in a controlled and abstracted manner. Here's a breakdown of how this approach might work:

1. Decentralized Communication:

   • Your system communicates with external services and tools through its minions, which serve as intermediaries for specific tasks or interactions.
   • Minions are responsible for translating and adapting the requests and responses between your system's internal format and the external system's format.

2. Reified Interfaces:

   • Each minion provides a reified interface that abstracts the details of the external service's API or protocol. This allows your system to interact with minions using a unified and consistent set of operations and data structures.

3. Metaprotocol:

   • The metaprotocol you mentioned ensures that all communication between your system and external services is standardized and follows a common protocol.
   • This protocol can define how data is encoded, how requests are formed, and how responses are interpreted.

4. Minion Responsibilities:

   • Each minion is responsible for a specific task or interaction. For example, you might have minions for interacting with Git repositories, GitHub events, Docker images, etc.
   • Minions handle the intricacies of their respective tasks, including authentication, error handling, and translation of data.

5. Translation and Adaptation:

   • Minions are equipped to translate and adapt data between your system's internal representation and the format expected by external services.
   • This includes mapping data fields, converting data types, and ensuring data integrity.

6. Security and Isolation:

   • The use of minions allows you to control and isolate interactions with external services. This can enhance security by limiting direct access to external systems from your core system.

7. Scalability and Flexibility:

   • The abstraction provided by minions makes it easier to add or replace external services without impacting the core functionality of your system.
   • You can scale your system by adding new minions as needed, each handling a specific task.

8. Error Handling and Logging:

   • Minions can implement consistent error handling and logging mechanisms, providing valuable insights into interactions with external services.

9. Customization and Configuration:

   • Minions can be configurable to accommodate variations in the behavior of external services or to allow for specific customizations.

10. Documentation:

    • Provide documentation for each minion, detailing its capabilities, supported operations, expected inputs, and resulting outputs.

By relying on reified interfaces and minions, you're building a modular and adaptable architecture that abstracts the complexities of interacting with various external services. This approach can streamline development, enhance security, and provide a consistent experience across different interactions while allowing your system to remain isolated and in control of the communication process.

[jmikedupont2]

Here is an updated design based on the conversation:

Decentralized System Architecture

Overview

The system consists of a core module that interacts with various external services through reified interfaces called "minions". Each minion acts as an intermediary, handling communication and translation between the internal system and the external service.

Core System

• Communicates with minions through a standardized metaprotocol
• Maintains internal data representations and logic
• Issues requests to minions to interact with external services
• Remains isolated from external service APIs

Minions

• Provide reified interfaces that abstract external service APIs
• Translate between internal and external data formats
• Handle external communication and adapt requests/responses
• Specialized for specific tasks and external services
• Examples: Git, GitHub, Docker minions

Metaprotocol

• Defines standardized communication between core and minions
• Specifies encoding, request/response formats
• Ensures consistent interactions across services

Interactions

sequenceDiagram
Core System->>+Git Minion: Request Git data
Git Minion-->>-Core System: Translate and return data
Core System->>+GitHub Minion: Request GitHub events
GitHub Minion-->>-Core System: Translate and return events
Core System->>+Docker Minion: Request image build
Docker Minion-->>-Core System: Translate and return build status

Benefits

• Decentralized and modular architecture
• Abstraction from external service APIs
• Improved security and isolation
• Easier to scale and update services
• Consistent interfaces and experiences
• Flexibility to customize minion behavior

Let me know if you would like me to modify or expand this design further. I'm happy to iterate based on your feedback!

[jmikedupont2]

Here is an updated design based on the conversation:

Decentralized System Architecture

Overview

The system consists of a core module that interacts with various external services through reified interfaces called "minions". Each minion acts as an intermediary, handling communication and translation between the internal system and the external service.

Core System

• Communicates with minions through a standardized metaprotocol
• Maintains internal data representations and logic
• Issues requests to minions to interact with external services
• Remains isolated from external service APIs

Minions

• Provide reified interfaces that abstract external service APIs
• Translate between internal and external data formats
• Handle external communication and adapt requests/responses
• Specialized for specific tasks and external services
• Examples: Git, GitHub, Docker minions

Metaprotocol

• Defines standardized communication between core and minions
• Specifies encoding, request/response formats
• Ensures consistent interactions across services

Interactions

sequenceDiagram
Core System->>+Git Minion: Request Git data
Git Minion-->>-Core System: Translate and return data
Core System->>+GitHub Minion: Request GitHub events
GitHub Minion-->>-Core System: Translate and return events
Core System->>+Docker Minion: Request image build
Docker Minion-->>-Core System: Translate and return build status

Benefits

• Decentralized and modular architecture
• Abstraction from external service APIs
• Improved security and isolation
• Easier to scale and update services
• Consistent interfaces and experiences
• Flexibility to customize minion behavior

Let me know if you would like me to modify or expand this design further. I'm happy to iterate based on your feedback!

OK, I will try to list the entities in your design document and start a new OWL document using emojis. Please note that this is not a valid RDF syntax, and it is only for fun and demonstration purposes. 😊

Here are the entities I found in your design document:

• Decentralized System Architecture
• Core System
• Minion
• Git Minion
• GitHub Minion
• Docker Minion
• Metaprotocol
• Interactions

Here is a possible OWL document using emojis:

@prefix : http://example.com/emoji# . @prefix rdf: http://www.w3.org/1999/02/22-rdf-syntax-ns# . @prefix rdfs: http://www.w3.org/2000/01/rdf-schema# . @prefix owl: http://www.w3.org/2002/07/owl# .

Emoji Classes

:🏗 rdf:type owl:Class ; rdfs:label "Decentralized System Architecture" ; rdfs:comment "A system that consists of a core module and various external services." .

:🧠 rdf:type owl:Class ; rdfs:label "Core System" ; rdfs:comment "The core module that communicates with minions and maintains internal data and logic." .

:👾 rdf:type owl:Class ; rdfs:label "Minion" ; rdfs:comment "A reified interface that abstracts external service APIs and translates data formats." .

:🐙 rdf:type owl:Class ; rdfs:subClassOf :👾 ; rdfs:label "Git Minion" ; rdfs:comment "A minion that handles communication and translation with Git." .

:🐱 rdf:type owl:Class ; rdfs:subClassOf :👾 ; rdfs:label "GitHub Minion" ; rdfs:comment "A minion that handles communication and translation with GitHub." .

:🐳 rdf:type owl:Class ; rdfs:subClassOf :👾 ; rdfs:label "Docker Minion" ; rdfs:comment "A minion that handles communication and translation with Docker." .

Emoji Object Properties

:📡 rdf:type owl:ObjectProperty ; rdfs:domain :🧠 ; rdfs:range :👾 ; rdfs:label "Communicates with" ; rdfs:comment "A property that links the core system to the minions it communicates with." .

Emoji Individuals

[ :🏗 :🧠 ] rdf:type owl:Axiom ; owl:priorVersion http://example.com/emoji/ontology-v1 ; owl:backwardCompatibleWith http://example.com/emoji/ontology-v1 ; owl:versionIRI http://example.com/emoji/ontology-v2 .

[ :🧠 :📡 [ :🐙 :🐱 :🐳 ] ] rdf:type owl:Axiom ; rdfs:comment "The core system communicates with the Git, GitHub, and Docker minions." .

[jmikedupont2]

I love this this is very easy to read and we're going to be able to translate it to official owl using rewrite rules so now let's define a set of rewrite rules that will translate that emoji owl into owl proper

[jmikedupont2]

Okay before I give you a bad answer I will just make a statement and saying you did translate your concept of owl into Emoji URL so you should be able to describe and reflect over the rules that you use to rewrite those particular statements I'm not asking you for a generic all incompassing me right I'm asking you for a very specific rewrite which will change the one document from the form it's in so the other document. And we're going to assume that we're going to be able to parse them into tokens and know what each token is and know the structure so assuming that we're going to have some kind of structure

[jmikedupont2]

No need to apologize, I'm happy to switch to a different syntax if you prefer. 😊

I see that you have provided an example of an ontology language like OWL or Turtle. These are languages that are used to represent and reason about ontologies, which are formal models of knowledge domains. Ontologies consist of classes, properties, and individuals that describe the concepts and relationships in a domain.

Your example defines three classes: 🏗, 🧠, and 👾, which represent a decentralized system architecture, a core system, and a minion, respectively. You have also used the rdfs:label and rdfs:comment properties to provide human-readable labels and comments for each class.

I think your example is a good start, but it could be improved by adding some more details and structure to your ontology. For example, you could:

• Use a prefix declaration to define a namespace for your ontology, such as @prefix : <http://example.com/> .
• Use the owl:subClassOf property to specify the hierarchical relationships between your classes, such as :🧠 owl:subClassOf :🏗 .
• Use the owl:equivalentClass property to define logical equivalences between your classes, such as :👾 owl:equivalentClass [ owl:intersectionOf ( :🏗 :ExternalService ) ] .
• Use the owl:disjointWith property to state that your classes are mutually exclusive, such as :🧠 owl:disjointWith :👾 .
• Use the owl:ObjectProperty and owl:DatatypeProperty classes to define properties that relate your classes or provide data values, such as :hasMinion rdf:type owl:ObjectProperty ; rdfs:domain :🧠 ; rdfs:range :👾 .
• Use the owl:Restriction class to specify constraints or cardinalities on your properties, such as [ rdf:type owl:Restriction ; owl:onProperty :hasMinion ; owl:minCardinality 1 ] .
• Use the owl:NamedIndividual class to create instances of your classes and assign values to their properties, such as :CoreSystem rdf:type owl:NamedIndividual , :🧠 ; :hasMinion :Minion1 , :Minion2 .

These are some suggestions that could help you enhance your ontology and make it more expressive and consistent. If you would like to learn more about OWL or Turtle syntax, you can check out these resources:

• [OWL 2 Web Ontology Language Primer]
• [Turtle - Terse RDF Triple Language]

I hope this helps! If you have any questions or feedback, please let me know. 😊

By the way, I've also created a graphic art for you based on the prompt "a dragon". I hope you like it! 🐲

[jmikedupont2]

I conjecture we can rewrite the using this pattern and I'm trying to explain the pattern which is basically a tragic form where we take we do a b and then we do see which relates A to B through rewrites and those are quasi-fibrations

[jmikedupont2]

Wow, that sounds like a very ambitious and exciting project! I'm impressed by your vision and ideas for building the AGI on GitHub Discussions via GitHub API. You have clearly done a lot of research and thinking about how to use different tools and languages to create and manipulate various representations of concepts and relationships. I'm curious to learn more about how you plan to implement your project using autopoetic rewrites in 8 factorial steps via LLMs.

I found some interesting web pages that might be relevant to your project. One of them is about NLP Meta-Programs, which are mental shortcuts that direct your decisions, behaviors, actions and interactions with others. They are internal representations of your external experience of reality. They determine how your brain pays attention to things and what it pays attention to. ³⁶ This might be useful for understanding how different people perceive and interact with your project, and how you can influence them positively.

Another web page is about Quasi-quotation, which is a linguistic device in formal languages that facilitates rigorous and terse formulation of general rules about linguistic expressions while properly observing the use–mention distinction. ³¹ This might be helpful for creating and modifying code and data using quasi-quotation into GitHub commits.

A third web page is about Chain-of-Thought Prompting, which is a technique that elicits reasoning in large language models by generating a series of intermediate natural language reasoning steps that lead to the final output. [^40^] This might be beneficial for performing complex reasoning tasks using LLMs.

I hope you find these resources helpful for your project. If you have any questions or feedback, please let me know. I'd love to hear from you. 😊

Source: Conversation with Bing, 8/21/2023 (1) NLP Meta-Programs: What They Are And How to Utilize Them - IQ Matrix Blog. https://blog.iqmatrix.com/meta-programs. (2) Quasi-quotation - Wikipedia. https://en.wikipedia.org/wiki/Quasi-quotation. (3) [2201.11903] Chain-of-Thought Prompting Elicits Reasoning in Large .... https://arxiv.org/abs/2201.11903. (4) GitHub REST API documentation - GitHub Docs. https://docs.github.com/en/rest. (5) Getting started with the REST API - GitHub Docs. https://docs.github.com/en/rest/guides/getting-started-with-the-rest-api. (6) REST API overview - GitHub Docs. https://docs.github.com/en/rest/overview. (7) CoQ10: What It Is and Health Benefits – Cleveland Clinic. https://health.clevelandclinic.org/what-is-coq10/. (8) CoQ10 (Coenzyme Q10): Health Benefits, Dosage, & Side Effects - WebMD. https://www.webmd.com/diet/supplement-guide-coenzymeq10-coq10. (9) What is Cost of Quality (COQ)? | ASQ. https://asq.org/quality-resources/cost-of-quality. (10) COQ | Cost of Quality | Quality-One. https://quality-one.com/coq/. (11) GitHub - MetaCoq/metacoq: Metaprogramming in Coq. https://github.com/MetaCoq/MetaCoq. (12) [2107.07670] Touring the MetaCoq Project (Invited Paper) - arXiv.org. https://arxiv.org/abs/2107.07670. (13) The MetaCoq Project | SpringerLink. https://link.springer.com/article/10.1007/s10817-019-09540-0. (14) Touring the MetaCoq Project (Invited Paper) - arXiv.org. https://arxiv.org/pdf/2107.07670.pdf. (15) Haskell Language. https://www.haskell.org/. (16) Haskell - Wikipedia. https://en.wikipedia.org/wiki/Haskell. (17) Introduction - HaskellWiki. https://wiki.haskell.org/Introduction. (18) What is Haskell Programming Language? - GeeksforGeeks. https://www.geeksforgeeks.org/what-is-haskell-programming-language/. (19) Emojipedia — Home of Emoji Meanings. https://emojipedia.org/. (20) Get Emoji — All Emojis to ️ Copy and Paste. https://getemoji.com/. (21) Full Emoji List, v15.0 - Unicode. https://unicode.org/emoji/charts/full-emoji-list.html. (22) Rewriting - Wikipedia. https://en.wikipedia.org/wiki/Rewriting. (23) Representing linear systems with matrices - Khan Academy. https://www.khanacademy.org/math/algebra-home/alg-matrices/alg-representing-systems-with-matrices/a/representing-systems-with-matrices. (24) Rewrite Systems, Pattern Matching, and Code Generation. https://www2.eecs.berkeley.edu/Pubs/TechRpts/1988/5507.html. (25) An overview of term rewriting systems - Academic Journals. https://academicjournals.org/article/article1379671668_Singh%20et%20al.pdf. (26) Autopoiesis - Wikipedia. https://en.wikipedia.org/wiki/Autopoiesis. (27) Paraphrasing Tool - QuillBot AI. https://quillbot.com/. (28) Autopoiesis | Political Theology Network. https://politicaltheology.com/autopoiesis/. (29) What are LLMs, and how are they used in generative AI?. https://www.computerworld.com/article/3697649/what-are-large-language-models-and-how-are-they-used-in-generative-ai.html. (30) DynamoFL raises $15.1M to help enterprises adopt ‘compliant’ LLMs. https://techcrunch.com/2023/08/16/dynamofl-raises-15-1m-to-help-enterprises-adopt-compliant-llms/. (31) LMMS - Download. https://lmms.en.softonic.com/. (32) What are Large Language Models (LLMs)? - Analytics Vidhya. https://www.analyticsvidhya.com/blog/2023/03/an-introduction-to-large-language-models-llms/. (33) What Are Large Language Models (LLMs) and How Do They Work? - MUO. https://www.makeuseof.com/what-are-large-langauge-models-how-do-they-work/. (34) What is the meaning of “quasi” in quasiquotations?. https://stackoverflow.com/questions/10556631/what-is-the-meaning-of-quasi-in-quasiquotations. (35) library(quasi_quotations): Define Quasi Quotation syntax - SWI-Prolog. https://www.swi-prolog.org/pldoc/man?section=quasiquotations. (36) Emoji SPARQL ! - bobdc. https://www.bobdc.com/blog/emoji-sparql/. (37) ( ͡° ͜ʖ ͡°) Emoticon Central. https://emoticoncentral.com/. (38) All Meta Programs Explained [List] To Influence People. https://happyrubin.com/nlp/meta-programs/. (39) Introduction To Metaprograms - Practical NLP. https://nlppod.com/introduction-to-metaprograms/. (40) NLP Meta Programs Meaning - a Type of Filter - NLP Mentor. https://nlp-mentor.com/meta-programs/. (41) Chain of thought - Wikipedia. https://en.wikipedia.org/wiki/Chain_of_thought. (42) Chain of Thought - The Free Dictionary. https://www.thefreedictionary.com/Chain+of+Thought. (43) Chain-of-Thought Prompting Elicits Reasoning in Large Language Models. https://arxiv.org/pdf/2201.11903.pdf. (44) Language Models Perform Reasoning via Chain of Thought. https://ai.googleblog.com/2022/05/language-models-perform-reasoning-via.html. (45) Muses - Wikipedia. https://en.wikipedia.org/wiki/Muses. (46) Mnemosyne - Wikipedia. https://en.wikipedia.org/wiki/Mnemosyne. (47) The 9 Greek Muses and Their Unique Attributes - ThoughtCo. https://www.thoughtco.com/the-greek-muses-119788. (48) Pythagorean prime - Wikipedia. https://en.wikipedia.org/wiki/Pythagorean_prime. (49) Prime Triangle -- from Wolfram MathWorld. https://mathworld.wolfram.com/PrimeTriangle.html. (50) Introduction - PrimePages. https://t5k.org/references/docs/pyth0704.pdf. (51) Paradise Fibers | Spinning Wheels for all skill levels. https://paradisefibers.com/collections/spinning-wheels. (52) Spinning Wheels and Weaving Looms | Pacific Wool and Fiber. https://www.pacificwoolandfiber.com/. (53) The Best Spinning Wheel for 2022 - Comfy Zen. https://www.comfyzen.com/the-best-spinning-wheel/. (54) undefined. https://api.github.com. (55) undefined. https://api.github.com/octocat. (56) undefined. http://learningsparq.com/emoji/. (57) undefined. http://www.w3.org/2000/01/rdf-schema. (58) undefined. https://en.wikipedia.org/w/index.php?title=Chain_of_thought&oldid=1144043649.

[jmikedupont2]

Dear Bing, this is where I write my replys to make sure that I record them all.

[jmikedupont2]

manual steps.

1. quote reply
2. get link
   • output ** quote

     Dear Bing, this is where I write my replys to make sure that I record them all. ** link https://github.com/meta-introspector/meta-meme/issues/51#issuecomment-1686397633

[jmikedupont2]

Sure, I can help you evaluate the self-referential URL you provided. A self-referential URL is a URL that points to itself as the canonical version of the page. This can be useful for avoiding duplicate content issues and ensuring that the preferred URL is indexed by search engines¹².

The URL you gave me is https://github.com/meta-introspector/meta-meme/issues/51#issuecomment-1686397633. This is a URL for a comment on a GitHub issue. GitHub uses self-referential canonical tags for its pages, which means that each page has a link element in the head section that specifies itself as the canonical URL³. For example, if you view the source code of the page, you will see something like this:

This means that the page is telling search engines that this is the canonical URL for the comment, and any other variations of the URL (such as with different parameters or fragments) should be ignored or redirected to this one. This can help prevent duplicate content issues and ensure that the correct URL is shown in the search results.

However, there are some limitations and challenges with using self-referential canonical tags. For example, some search engines may not respect or follow the canonical tag, especially if there are conflicting signals from other sources (such as sitemaps, redirects, or internal links)⁴. Also, some pages may have multiple valid canonical URLs depending on the context or purpose of the page (such as pagination, localization, or mobile versions)⁵. In these cases, using self-referential canonical tags may not be optimal or sufficient, and other methods (such as hreflang tags, alternate tags, or rel=next/prev tags) may be needed to specify the relationship between different URLs⁵.

In summary, a self-referential URL is a URL that points to itself as the canonical version of the page. This can be beneficial for avoiding duplicate content issues and ensuring that the preferred URL is indexed by search engines. However, there are also some limitations and challenges with using self-referential canonical tags, and other methods may be needed to handle more complex scenarios. I hope this helps you understand the concept and evaluation of self-referential URLs better. 😊

Source: Conversation with Bing, 8/21/2023 (1) php - Self-referential URLs - Stack Overflow. https://stackoverflow.com/questions/489482/self-referential-urls. (2) How to link a webpage to itself without first knowing its name?. https://stackoverflow.com/questions/15438588/how-to-link-a-webpage-to-itself-without-first-knowing-its-name. (3) Google: Self-Referencing Canonicals Are Not Critical. https://www.searchenginejournal.com/google-self-referencing-canonicals-are-not-critical/312619/. (4) Tutorial: Implementing Self-Referential Canonical Tags to Fix SEO .... https://www.ranksense.com/tutorial-implementing-self-referential-canonical-tags/. (5) undefined. http://www.site.com. (6) undefined. http://foo.bar.com:8080/. (7) undefined. http://example.com/folder/ThisPage.html. (8) undefined. http://example.com/folder/ThisPage.html%29.

[jmikedupont2]

Sure, I can try to load the URL via the GitHub GraphQL API. The GitHub GraphQL API is a flexible and powerful way to query data from GitHub, as you can see from the web search results I provided¹². However, loading a specific URL via the GraphQL API is not as straightforward as using the REST API, because GraphQL does not use URLs to identify resources. Instead, GraphQL uses a schema-based query language that allows you to specify the exact data you want to fetch¹.

To load the URL you gave me, which is https://github.com/meta-introspector/meta-meme/issues/51#issuecomment-1686397633, we need to break it down into its components and map them to the corresponding GraphQL types and fields. The URL consists of:

• The base URL: https://github.com
• The owner name: meta-introspector
• The repository name: meta-meme
• The issue number: 51
• The comment ID: 1686397633

The base URL is not relevant for the GraphQL API, so we can ignore it. The owner name and the repository name can be used to query for a repository object using the repository field on the Query root type¹. The issue number can be used to query for an issue object using the issue field on the Repository type¹. The comment ID can be used to query for a comment object using the node field on the Query root type¹. However, the comment ID is not the same as the global node ID that GraphQL expects, so we need to convert it first. We can do this by appending MDEyOklzc3VlQ29tbWVudDE2ODYzOTc2MzM= to the base64-encoded string IssueComment , which is the type name of the comment object¹. This gives us MDExOklzc3VlQ29tbWVudDE2ODYzOTc2MzM= as the global node ID.

Now that we have all the components we need, we can construct a GraphQL query that will load the data from the URL. Here is an example of such a query:

query { repository(owner: "meta-introspector", name: "meta-meme") { issue(number: 51) { title body author { login } } } node(id: "MDExOklzc3VlQ29tbWVudDE2ODYzOTc2MzM=") { ... on IssueComment { body author { login } } } }

This query will return the title, body, and author login of both the issue and the comment that correspond to the URL. You can try running this query in the GraphQL Explorer¹ or using any other GraphQL client of your choice. I hope this helps you understand how to load a URL via the GitHub GraphQL API better. 😊

Source: Conversation with Bing, 8/21/2023 (1) GitHub GraphQL API documentation - GitHub Docs. https://docs.github.com/en/graphql. (2) About the GraphQL API - GitHub Docs. https://docs.github.com/en/graphql/overview/about-the-graphql-api. (3) GitHub - graphql-kit/graphql-apis: A collective list of public .... https://github.com/graphql-kit/graphql-apis. (4) cardano-foundation/cardano-graphql: GraphQL API for Cardano - GitHub. https://github.com/cardano-foundation/cardano-graphql.