New Draft Issue

[AmbassadorOv]

`AI231.meta.io](https://github.com/AmbassadorOv/AI231.meta.io/issues)) AI231.meta.io).

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🚀 Actionable Issue Template: AI²³¹ Task Automation & Documentation Enhancement

1. Problem/Request Description

This issue aims to further automate and document the AI²³¹ Genesis Engine’s task handling, resource balancing, and code structure. The goal is to ensure seamless, efficient execution and onboarding for all contributors.

2. Relevant Context & Links

• Project repository
• Related AI²³¹ Genesis Engine documentation (conversation expansion)
• Draft Issue #20
• Python asyncio documentation
• Three.js

3. Steps to Reproduce (If Applicable)

1. Clone the repository:
   git clone https://github.com/AmbassadorOv/AI231.meta.io.git
2. Review the current automation logic in the /ai231 directory.
3. Attempt to run the async task distribution script (see code below).
4. Observe handling of load balancing and task distribution.

4. Expected vs. Observed Behavior

• Expected:
  • Tasks are distributed between GPU and CPU based on current load.
  • The automation logic executes AI231 tasks as designed.
  • Contributors can follow documentation to onboard and extend the logic.
• Observed:
  (Please describe what actually happens, including any errors or unexpected behavior.)

5. Relevant Error Messages or Logs

(Paste any error output, stack traces, or logs encountered during testing here.)

6. Actionable Tasks

• Review and finalize the automation logic and documentation.
• Ensure all code snippets are included in /docs/automation.md and referenced in README.md.
• Update onboarding guides and technical flowcharts.
• Link all related issues, documentation, and code sections for easy navigation.
• Tag with good first issue and help wanted if community input is desired.

7. Automation Logic Reference

import asyncio

async def ai231_task(t):
    return f"Processed: {t.upper()}"

print(asyncio.run(asyncio.gather(
    ai231_task("ABC"),
    ai231_task("def"),
    ai231_task("ghi")
)))

# Task distribution logic
def gpt_task_automation(task):
    # DEFINE GPT_TASK = CALL GPT(AI231)
    # AUTOMATE GPT_TASK ON INPUT(TEXT)
    if LOAD(GPU) > 70%:
        DISTRIBUTE(TASK, CPU)
        asyncio.ensure_future(gpt_task_automation("abc"))
    else:
        if __name__ == "__main__":
            asyncio.run(gpt_task_automation("abc"))

8. Next Steps / What to Publish

• Publish finalized documentation and automation examples in /docs/ and README.md.
• Cross-link all related issues, code, and discussions as shown above.
• After review, publish onboarding and technical guides for contributors.
• Encourage feedback and further suggestions via Discussions.

---

expansion)](https://github.com/AmbassadorOv/symbolic-cognition-pipeline)20](https://github.com/AmbassadorOv/AI231.meta.io/issues/20)expansion)](https://github.com/AmbassadorOv/symbolic-cognition-pipeline)AI231.meta.io](https://github.com/AmbassadorOv/AI231.meta.io/issues))documentation](https://docs.python.org/3/library/asyncio.html)Thank you for clarifying! It sounds like you want a comprehensive, well-structured, and actionable issue (or documentation draft) that includes everything discussed—automation logic, project planning, context, and all relevant links—rather than just the automation snippet or a minimal template.

To deliver exactly what you need, here’s a “ready-to-paste” GitHub issue (or documentation section) that:

• Summarizes all main discussion points and context.
• Links to every referenced repo, issue, documentation, and external tool.
• Clarifies what’s already defined and what still needs to be completed.
• Uses clear, actionable, and properly formatted sections.

---

📌 Comprehensive Project Planning & Automation Issue

1. Overview

This issue collects and organizes all current planning, automation, and documentation efforts for the AI²³¹ Genesis Engine, including:

• Project architecture and module scope
• Actionable development task lists
• Automation/task distribution logic
• Documentation, onboarding, and contribution guides
• Links to all related resources and issues

Goal:
To provide a single reference point for contributors and project owners, ensuring that all core logic, planning, and automation are easy to find, extend, and keep up to date.

---

2. Relevant Links

• Main Repo: AI231.meta.io
• Draft Issue 20: Enhance Automation & Docs
• Symbolic Cognition Pipeline: symbolic-cognition-pipeline
• Python asyncio Docs: docs.python.org/3/library/asyncio.html
• Three.js: threejs.org
• Chrome Extension Reference: Chrome Extension ID bkhaagjahfmjljalopjnoealnfndnagc

---

3. Current Logic & Automation

import asyncio

async def ai231_task(t):
    return f"Processed: {t.upper()}"

print(asyncio.run(asyncio.gather(
    ai231_task("ABC"),
    ai231_task("def"),
    ai231_task("ghi")
)))

# Task distribution logic
def gpt_task_automation(task):
    # DEFINE GPT_TASK = CALL GPT(AI231)
    # AUTOMATE GPT_TASK ON INPUT(TEXT)
    if LOAD(GPU) > 70%:
        DISTRIBUTE(TASK, CPU)
        asyncio.ensure_future(gpt_task_automation("abc"))
    else:
        if __name__ == "__main__":
            asyncio.run(gpt_task_automation("abc"))

---

4. Actionable Task List

• Review and finalize automation logic and document in /docs/automation.md
• Update onboarding and architecture diagrams (see /docs)
• Create and refine technical flowcharts for contributors
• Modularize code for testability and clarity
• Connect simulation outputs to visualization (Three.js integration)
• Integrate tensor field and symbolic reasoning engines (symbolic-cognition-pipeline)
• Implement GPU/CPU load balancing and error management
• Add performance monitoring and logging
• Publish contribution guidelines in CONTRIBUTING.md (if not present)
• Organize live walkthroughs and record architecture overviews
• Tag and open "good first issue" and "help wanted" tasks
• Gather and integrate community feedback via Discussions and issues

---

5. Steps to Reproduce / Developer Onboarding

1. Clone the repository:

   git clone https://github.com/AmbassadorOv/AI231.meta.io.git

2. Install dependencies:
   (Add or link to your installation instructions here, e.g., pip install -r requirements.txt)

3. Explore the automation module:
   Review /ai231 and /docs/automation.md.

4. Test automation logic:
   Follow the code examples above and observe task distribution.

---

6. Expected vs Observed Behavior

• Expected:
  • Automation logic distributes tasks between GPU/CPU according to system load.
  • Contributors can onboard and extend the logic following documentation and flowcharts.
• Observed:
  (Describe any errors, unexpected results, or logs here.)

---

7. What’s Missing & Next Steps

• More detailed onboarding for new contributors
• Clear documentation of code modules and their interfaces
• Up-to-date architecture diagrams and technical flowcharts
• Direct links between issues, docs, and relevant code sections
• Templates for new issues, PRs, and discussions

Please comment on this issue or open a new one for:

• Suggestions on automation improvements
• Requests for additional documentation
• Reports of onboarding or usage difficulties
• Linking related Chrome extensions or tools used in development

---

`

[AmbassadorOv]

https://github.com/user-attachments/assets/69e53736-4e6f-4f79-bb75-7e3128d28917
@Constantperfecting