ad

v1.5.0 safe
1.0
Low Risk

Fast, transparent first- and second-order automatic differentiation

🤖 AI Analysis

Final verdict: SAFE

The package ad v1.5.0 has been assessed as safe with no indications of malicious activity. All checks indicate low risk across all categories.

  • No network calls detected
  • No shell execution patterns found
  • No obfuscation or credential harvesting detected
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package requires internet access for its functionality.
  • Shell: No shell execution patterns detected, indicating no immediate signs of executing system commands.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious obfuscation.
  • Credentials: No credential harvesting patterns detected, indicating low risk of secret theft.

🔬 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 score 9.0

Possible typosquat of: cdk, arq, rq

  • "ad" is 2 edit(s) from "cdk"
  • "ad" is 2 edit(s) from "arq"
  • "ad" is 2 edit(s) from "rq"
✓ Registered Email Domain

Email domain looks legitimate: gmail.com>

⚠ Suspicious Page Links score 4.0

Found 2 suspicious link(s) on the package page

  • Non-HTTPS external link: http://en.wikipedia.org/wiki/Automatic_differentiation
  • Non-HTTPS external link: http://pythonhosted.org/ad
✓ Git Repository History

Repository eggzec/ad appears legitimate

⚠ 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 ad 
Create a Python-based mini-application that utilizes the 'ad' package for performing automatic differentiation on mathematical functions. This application will serve as a tool for students and researchers to easily compute derivatives of complex functions without having to manually derive them. The application should have a user-friendly interface where users can input a mathematical function (e.g., f(x) = x^2 + 3x - 5) and specify the variable with respect to which they want to differentiate. Additionally, the app should allow users to choose between computing the first derivative, second derivative, or both.

### Features:
1. **Input Function:** Users should be able to input any mathematical function supported by Python's math library.
2. **Variable Specification:** Users must specify the variable with respect to which they wish to differentiate.
3. **Derivative Selection:** Users should be able to select whether they want to calculate the first derivative, the second derivative, or both.
4. **Result Display:** The application should display the resulting derivative(s) in a readable format.
5. **Graphical Representation:** Optionally, the application could graphically represent the original function and its derivatives using matplotlib or a similar plotting library.
6. **Error Handling:** Implement error handling to manage incorrect inputs (e.g., non-mathematical expressions, undefined variables).
7. **Help Section:** Include a help section that explains how to use the application and provides examples of valid inputs.

### Utilizing the 'ad' Package:
- Use the 'ad' package to perform automatic differentiation on the user-inputted function. The 'ad' package will automatically handle the computation of the derivatives based on the input function and the specified variable. Ensure that the application demonstrates the transparency and speed of the 'ad' package by clearly showing the steps involved in the differentiation process (if possible) and measuring the time taken to compute the derivatives.

### Deliverables:
- A Python script that integrates the 'ad' package for differentiation.
- A user interface (console-based or GUI) that allows users to interact with the differentiation tool.
- Documentation explaining the setup, usage, and implementation details of the application.
- Example outputs demonstrating the application's functionality.