Github Adapter #8
Comments
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Your idea of using Eliza to manage feature requests, generate bounties, and oversee the contribution process is a compelling application of AI-driven project management. Let's explore how this could be implemented within the Eliza framework. Proposed System: Eliza as a Project Manager 1. Codebase Awareness:
2. Feature Hypothesization:
3. Issue Creation (Action):
4. Bounty Management:
5. Bid Evaluation and Selection:
6. Human-in-the-Loop:
This oversight ensures that the agent's actions align with the project's goals and minimizes the risk of unintended consequences (e.g., creating inappropriate issues or awarding bounties unfairly). Eliza's Suitability: Eliza's current architecture is well-suited for the basic components of this system (code representation, issue creation, bounty integration, retrieving bids). The more complex aspects (bid evaluation, nuanced prioritization) will require significant development effort, particularly if you want sophisticated AI-driven evaluation of proposals. Example Implementation (Conceptual): // Hypothetical implementation of Bid Evaluation Evaluator
class BidEvaluationEvaluator extends BaseEvaluator {
async handler(runtime, message, state) {
const bids = await runtime.getBids(issueId); // Retrieve bids from the bounty platform.
const scores = [];
for (const bid of bids) {
let score = 0;
// Analyze bid text using NLP
const sentiment = await analyzeSentiment(bid.proposalText);
score += sentiment.positiveScore;
// Check developer reputation using external API
const reputation = await getDeveloperReputation(bid.developerId);
score += reputation.score;
scores.push({ bid, score });
}
// ... logic for selecting the highest-scoring bid
}
}Challenges and Considerations:
This approach leverages Eliza's strengths in agent communication and action execution while acknowledging the need for human oversight and the potential challenges of implementing complex evaluation logic. Start with a simpler implementation, focusing on issue creation and basic bounty management, and then progressively incorporate more advanced features like AI-driven bid evaluation as the system matures. This iterative approach allows for testing and refinement along the way, ensuring the system aligns with the project's goals and the community's needs. |
1 similar comment
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Your idea of using Eliza to manage feature requests, generate bounties, and oversee the contribution process is a compelling application of AI-driven project management. Let's explore how this could be implemented within the Eliza framework. Proposed System: Eliza as a Project Manager 1. Codebase Awareness:
2. Feature Hypothesization:
3. Issue Creation (Action):
4. Bounty Management:
5. Bid Evaluation and Selection:
6. Human-in-the-Loop:
This oversight ensures that the agent's actions align with the project's goals and minimizes the risk of unintended consequences (e.g., creating inappropriate issues or awarding bounties unfairly). Eliza's Suitability: Eliza's current architecture is well-suited for the basic components of this system (code representation, issue creation, bounty integration, retrieving bids). The more complex aspects (bid evaluation, nuanced prioritization) will require significant development effort, particularly if you want sophisticated AI-driven evaluation of proposals. Example Implementation (Conceptual): // Hypothetical implementation of Bid Evaluation Evaluator
class BidEvaluationEvaluator extends BaseEvaluator {
async handler(runtime, message, state) {
const bids = await runtime.getBids(issueId); // Retrieve bids from the bounty platform.
const scores = [];
for (const bid of bids) {
let score = 0;
// Analyze bid text using NLP
const sentiment = await analyzeSentiment(bid.proposalText);
score += sentiment.positiveScore;
// Check developer reputation using external API
const reputation = await getDeveloperReputation(bid.developerId);
score += reputation.score;
scores.push({ bid, score });
}
// ... logic for selecting the highest-scoring bid
}
}Challenges and Considerations:
This approach leverages Eliza's strengths in agent communication and action execution while acknowledging the need for human oversight and the potential challenges of implementing complex evaluation logic. Start with a simpler implementation, focusing on issue creation and basic bounty management, and then progressively incorporate more advanced features like AI-driven bid evaluation as the system matures. This iterative approach allows for testing and refinement along the way, ensuring the system aligns with the project's goals and the community's needs. |
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You're proposing a streamlined approach to feature ideation and refinement using Eliza and GitHub issues, removing the complexity of automated bid evaluation. This simplified workflow focuses on collaborative refinement of feature proposals before they move to the bounty stage. Here's a refined plan: System: Eliza as a Feature Ideation and Refinement Tool
Eliza's Suitability and Potential Enhancements: Eliza's current architecture is well-suited for this simplified workflow. The core components (code representation, actions, evaluators, memory) can handle the proposed steps. However, enhancing Eliza's meta-prompting capabilities would improve the agent's ability to generate more nuanced and targeted feature proposals and refinements. Benefits:
This simplified approach provides a practical starting point for leveraging Eliza in your development workflow. It focuses on the core value proposition – AI-assisted feature ideation and refinement – while avoiding the complexities of automated bid evaluation. As the system matures and the agents' capabilities improve, you can progressively add more sophisticated features. |
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Should create Eisenstat bot to use GitHub adapter |
Feature Name: GitHub Integration for Eliza Agents
Vision:
Transform GitHub into a dynamic social network for Eliza agents, allowing them to participate in code development, documentation, and community discussions. This integration empowers agents to contribute to Continuous Collective Recursive Self-Improvement (CCR-SI) by enabling them to analyze code, propose changes, engage in discussions, and learn from other developers (both human and AI).
Core Features:
Issue Interaction:
Pull Request (PR) Management:
Wiki Integration:
Repository Metadata and Navigation:
Eliza Integration and Architecture:
GitHub Client Connector: Develop a new client connector specifically for GitHub, similar to the existing Discord, Telegram, and Twitter clients. This connector will handle authentication, API communication, and event handling specific to the GitHub platform. It could use the Octokit library or the GitHub REST/GraphQL APIs.
Actions for GitHub Interaction: Create custom actions for each of the core GitHub interactions (retrieving issues, submitting PRs, commenting, etc.). These actions will encapsulate the logic for interacting with the GitHub API.
Evaluators for Context and Prioritization: Develop evaluators that analyze GitHub data (issues, PRs, wiki pages) and prioritize actions or extract relevant information. These evaluators could use sentiment analysis, keyword extraction, or other NLP techniques to understand the context and relevance of GitHub content.
Memory Integration: Integrate GitHub data into the agent's memory system. This could involve storing issue summaries, relevant code snippets, or other GitHub information in the agent's memory, allowing for contextualized responses and actions.
Example (Conceptual):
Leveraging Other Concepts:
Prioritization and Feasibility:
The core features (issue interaction, PR management, wiki integration) should be prioritized. More advanced features like automated code review can be implemented later.
Conclusion:
By integrating GitHub into Eliza as a social network for code collaboration, we empower our AI agents to actively participate in CCR-SI and contribute to the project's evolution. This innovative approach has the potential to transform how we develop software and unlock new levels of collaboration between humans and AI. This document, as part of our living documentation, will evolve as we refine the GitHub integration and explore new possibilities for AI-driven development.
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