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Project Name: Lenda

Project Description: enda is an AI-driven DeFi agent that maximizes stablecoin lending profits. It analyzes wallet data, predicts APY trends, and automates portfolio management, saving users time while optimizing returns. Smarter lending, effortless profits.

Project's X/Twitter: https://x.com/lendaonchain

Project's Website: https://x.com/lendaonchain

Project's GitHub: https://github.com/tinftsol/lenda

Hackathon Submission Analysis: Lenda

Overview

This hackathon submission, named Lenda, is an AI-driven DeFi agent designed to optimize stablecoin lending by analyzing wallet data, predicting APY trends, and automating portfolio management. The main functionalities include wallet analysis, APY prediction, and automated portfolio management, which aim to save users time while maximizing returns.

Main Features

  • Wallet Analysis: Evaluates user wallet positions to determine optimal lending strategies.
  • APY Prediction: Utilizes AI models to forecast interest rate trends across various DeFi protocols.
  • Automated Portfolio Management: Executes lending actions automatically to maximize returns.

Criteria Analysis

1. Correctness

Score: 7/10

Strengths:

  • The codebase appears to be well-structured, with clear interfaces and types defined for various components, such as IWalletPosition and IUserPositionHistory.
  • The use of TypeScript helps catch type-related errors at compile time.

Weaknesses:

  • There are several instances of incomplete implementations, such as the handler function in AnalyzeUserWalletPositionEvaluator which returns "TBD" without any functionality.

    handler: async (runtime: IAgentRuntime, message: Memory) => {
    console.log("Evaluating Kamino Protocol Specifics...");
    return "TBD";
}

  • The validate function in EvaluateSwitchFeasibilityEvaluator always returns false, which means it will never validate any input.

Improvements:

  • Complete the implementations of the incomplete functions to ensure they perform their intended tasks.
  • Review and fix the validation logic to ensure it correctly assesses the input.

2. Readability

Score: 6/10

Strengths:

  • The code is generally organized into modules, making it easier to navigate.
  • The use of TypeScript interfaces and enums enhances clarity regarding the data structures being used.

Weaknesses:

  • Some functions are lengthy and could benefit from breaking them down into smaller, more manageable pieces. For example, the handler function in PredictFutureAPYChangesAction is quite long and handles multiple responsibilities.

    handler: async (
    runtime: IAgentRuntime,
    message: Memory,
    state: State,
    _options: { [key: string]: unknown },
    callback?: HandlerCallback
) => {
    // Long function body
}

  • There are instances of commented-out code, such as in PredictFutureAPYChangesAction, which can clutter the code and reduce readability.

Improvements:

  • Refactor long functions into smaller helper functions to improve readability and maintainability.
  • Remove any unnecessary commented-out code to keep the codebase clean.

3. Bugginess

Score: 5/10

Strengths:

  • The codebase uses TypeScript, which helps reduce runtime errors by enforcing type checks.

Weaknesses:

  • There are several areas where error handling is either missing or insufficient. For example, in the GetWalletPositionsAction, if no valid wallet addresses are found, it simply logs a message without providing feedback to the user.

    if (walletsToCheck.length === 0 && isLogsIncluded) {
    callback?.({ text: "No valid wallet addresses were found to check positions." });
    return []
}

  • The sendTx function in LendLiquidityKaminoProtocolAction has a retry mechanism that may lead to infinite loops if the transaction fails repeatedly.

Improvements:

  • Implement comprehensive error handling throughout the codebase to ensure that users receive appropriate feedback when errors occur.
  • Review the retry logic in sendTx to prevent potential infinite loops.

4. Features

Score: 8/10

Strengths:

  • The submission includes a variety of features that align well with the hackathon's goals, such as wallet analysis, APY prediction, and automated lending actions.
  • The use of AI for predicting APY trends is a strong feature that adds significant value to the application.

Weaknesses:

  • Some features, such as the AnalyzeUserWalletPositionEvaluator, are not fully implemented, which limits the overall functionality of the application.

Improvements:

  • Complete the implementation of all planned features to ensure that the application meets its intended functionality.

Final Score

Final Score: 6.5/10

Summary

Overall, this hackathon submission demonstrates a solid foundation with a clear focus on optimizing stablecoin lending through AI. While there are strengths in the structure and planned features, there are also notable weaknesses in correctness, readability, and bugginess that need to be addressed. By completing the implementations, improving error handling, and enhancing readability, the submission could significantly improve its quality and user experience.