🤖 AI Analysis

Final verdict: SAFE

The package FluSimpleGD v1.0.4 exhibits minimal risks across all assessed categories with no network calls, shell executions, obfuscations, or credential harvesting attempts. The metadata risk is slightly elevated due to missing author details and being a single-package project on PyPI.

No network calls
No shell executions
No obfuscation patterns
No credential harvesting patterns
Metadata contains red flags

Per-check LLM notes

Network: No network calls detected, which is normal if the package does not require external communications.
Shell: No shell executions detected, indicating the package does not perform system-level commands.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The package has some red flags including lack of author details and a single package on PyPI, but no clear evidence of typosquatting or malicious intent.

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

✓ Code Obfuscation

No obfuscation patterns detected

✓ Shell / Subprocess Execution

No shell execution patterns detected

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: gmail.com>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

No GitHub repository linked

No GitHub repository link found

⚠ Maintainer History score 4.0

2 maintainer concern(s) found

Author name is missing or very short
Author "" appears to have only 1 package on PyPI (new or inactive account)

✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with FluSimpleGD

Your task is to develop a simple yet engaging utility application using the Python package 'FluSimpleGD'. This application will serve as a basic educational tool designed to help users understand and visualize the process of gradient descent, a fundamental concept in machine learning and optimization algorithms.

The application should allow users to input a simple quadratic function (y = ax^2 + bx + c) and visually demonstrate how gradient descent finds the minimum point of this function. Here are the key steps and features your application should include:

1. **User Interface**: Design a clean, user-friendly interface where users can input the coefficients (a, b, c) of the quadratic function. Ensure the UI is responsive and intuitive, providing immediate feedback on valid inputs.

2. **Function Visualization**: Use FluSimpleGD's plotting capabilities to graphically represent the quadratic function. The plot should dynamically update based on the user's input values for a, b, and c.

3. **Gradient Descent Simulation**: Implement a feature that simulates gradient descent on the given quadratic function. This simulation should visually track the movement towards the minimum value of the function, highlighting each step of the descent process. Users should be able to control parameters such as learning rate and number of iterations.

4. **Educational Insights**: Provide explanations or tooltips that appear during the simulation to help users understand the mathematical principles behind gradient descent, such as the importance of the learning rate and how the algorithm converges to a solution.

5. **Save & Share**: Allow users to save their custom functions and simulations as images or share them directly via social media platforms like Twitter or LinkedIn.

To achieve these goals, you'll need to leverage FluSimpleGD's core functionalities, including its plotting tools and optimization algorithms. Your implementation should not only demonstrate the technical aspects of using FluSimpleGD but also showcase how it can be applied to create interactive and educational software applications.