Design DADAGAN’s decision-making core in MeTTa enabling it to autonomously create promote and sell its artwork in a decentralized infrastructure that will give it full operational autonomy.

Technical Documentation Architecture Design Blueprint Overview of D ADA GA N’s system components within MeTTa detailing its core modules (art creation social media engagement NFT sales). Flow diagrams showing how D ADA GA N will handle tasks autonomously (e.g. generating artwork analyzing interactions managing sales) and the inter-module communication flow. 2. Initial Implementation Prototype Prototype Code & Scripts Preliminary MeTTa-based scripts and code snippets that demonstrate basic decision-making functions (e.g. self-promotion). A GitHub repository where initial code can be reviewed with documentation for installation usage and basic testing. Sample Outputs Examples of prototype outputs in terms of artwork creation social media responses or sales strategy based on its autonomous decision-making. Log files or analytics showing D ADA GA N’s decision-making rationale recorded to provide insight into its choices and actions. 3. Testing & Analysis Report Initial Testing Results Summary of test cases executed to verify core decision-making functions. Identified areas for improvement with recommendations for adjustments in algorithms or MeTTa’s handling of tasks. Feedback Initial feedback summary from collaborators (e.g. art and tech experts) on D ADA GA N’s behavior and responsiveness. 4. Plan for Next Steps Outline for continued development

$10,000 USD

A decentralized version of DADAGAN that can independently create and promote art post on Twitter and manage art sales through smart contracts.

Use MeTTa’s self-referencing capabilities to enable DADAGAN to reflect on its own actions successes and failures. Implement adaptive learning allowing DADAGAN to modify its behavior and improve performance based on feedback loops and data from past interactions.

Detailed description of how MeTTa’s self-referencing capabilities enables DADAGAN to analyze its own outputs interactions and performance. Processing: Evaluation of outputs against predefined metrics (e.g. relevance aesthetic coherence or user engagement levels). Insights into strengths weaknesses and opportunities for improvement. Flow diagram illustrating the feedback loop showing how D ADA GA N processes past outputs to generate adaptive improvements. Rules for modifying artistic styles or content generation strategies based on feedback. Parameters for prioritizing certain tasks (e.g. engaging with specific audience types or creating high-demand visual styles). Explanation of how learning weights or adjustments are stored and accessed within MeTTa. Prototype Code & Scripts Initial implementation of self-reflection functions in MeTTa. Analysis of past actions Mechanisms to categorize outputs and feed them back into the system. A GitHub repository containing the implemented code. Documentation for setup usage and testing. Evidence of ability to reflect on a set of past outputs. Description of test cases used to validate self-reflection and learning mechanisms. Key insights into system performance such as: Accuracy of self-assessment. Effectiveness of behavioral adjustments. Improved engagement rates (e.g. likes comments retweets). Reduction in repeated mistakes or underperforming actions. Integration of user-generated feedback into the reflection process.

$15,000 USD

DADAGAN’s behavior evolving over time learning from interaction patterns and feedback, becoming more effective at engaging its audience and generating revenue.