Log Sharing Plugin

[jmikedupont2]

Title: Proposal - Introspector Architecture for Diverse Log Sharing and Publication on evo.ninja

Introduction:
We propose the implementation of an introspector architecture on evo.ninja, aimed at streamlining the process of sharing logs using various protocols and methods and enabling users to publish logs to platforms like Archive.org, DKAN, Wikimedia, and more. This architecture will empower users to contribute valuable data and insights, fostering collaboration and innovation within our community while extending our reach to broader knowledge-sharing platforms.

Key Features:

1. Diverse Log Sharing Protocols and Methods:

• Users can share logs through a wide range of protocols and methods, ensuring flexibility and compatibility with different workflows.
• Supported methods include but are not limited to:
  • GitHub (Issues, Discussions, Commits)
  • Raw Git Repositories
  • Log Files
  • Gron Files
  • YAML Files
  • S3 Buckets
  • SSH Endpoints
  • SFTP Endpoints
  • Custom Endpoints

2. Multiple Repositories:

• We will introduce multiple repositories tailored to different purposes, accommodating the diverse log sharing methods.
  • GitHub Repositories: For seamless integration with GitHub-based workflows.
  • Custom Repositories: Tailor-made repositories for specific projects or research initiatives.

3. Publication to Knowledge-Sharing Platforms:

• Users can opt to publish their logs to external knowledge-sharing platforms such as Archive.org, DKAN, Wikimedia, and others.
• Extending our reach to these platforms enhances the visibility and accessibility of shared data.

4. Privacy and Security:

• Data privacy is paramount. Logs shared and published through the introspector architecture will be anonymized and protected.
• Users have control over the level of data they share and publish, ensuring their comfort and privacy.

5. Collaborative Research:

• Researchers and developers can access shared and published logs to gain insights into AI performance and ethics.
• Collaborative research opportunities will drive advancements in natural language processing and AI.

6. User-Friendly Interface:

• The introspector architecture will feature an intuitive and user-friendly interface, making log sharing and publication effortless across diverse protocols and platforms.

How It Works:

1. Users access the introspector interface on evo.ninja.
2. They select the log sharing method or protocol that aligns with their preferences and workflow.
3. Users specify the repository or destination for sharing, depending on the chosen method.
4. Logs are shared securely, preserving user privacy, and in the format specified by the chosen protocol.
5. Users can opt to publish logs to external knowledge-sharing platforms with a simple click.

Why It Matters:

• The introspector architecture empowers users to actively participate in evo.ninja's development and AI research using their preferred log sharing methods.
• It extends our reach to broader knowledge-sharing platforms, promoting transparency and collaboration.
• Researchers benefit from real-world usage data gathered across diverse protocols and platforms to advance AI understanding.

Conclusion:
The introspector architecture is a pivotal step toward making evo.ninja a collaborative hub for AI innovation and research, accommodating various log sharing preferences and extending our reach to external knowledge-sharing platforms. We invite your feedback and insights as we work together to implement this feature-rich log sharing and publication system.

Let's embark on this journey to enhance evo.ninja's capabilities and drive AI innovation through shared knowledge and data, whether within our community or on broader knowledge-sharing platforms.

[dOrgJelli]

Hey @jmikedupont2, this is interesting, do you have an example application that can be reviewed that implements this architecture similar to your vision above?

[jmikedupont2]

Hey @jmikedupont2, this is interesting, do you have an example application that can be reviewed that implements this architecture similar to your vision above?

yes you can see it live here https://github.com/meta-introspector/meta-meme/blob/docs/llms/llama2-7b-chat-codeCherryPop-qLoRA-GPTQ/code_review_self.md it is reviewing its own code. see systems design here https://github.com/meta-introspector/meta-meme/wiki/SystemsDesign

[jmikedupont2]

we can make a gui based on lollms-webgui to display and edit and rank them or use open-assistant.io as well

[dOrgJelli]

Hey @jmikedupont2 I'm pretty confused by the things you're sending, it seems like 3-4 ideas rolled into one. I apologize if I'm "just not getting it". Is it possible to consolidate these ideas into a more clear description?

Ideally it would be something that could be implemented within the current evo.ninja project's scope. Otherwise it should be separate. Going to close this issue for now so that open issues are very action-forward, and there's no clear action here. Happy to continue discussion in the replies below and reopen later.

[jmikedupont2]

alright, I have this scheduled leading up to the hackthon later to capture logs, i really need some logs to review for evaluation of the current benchmarks.