alkaid

v0.7.1 suspicious
4.0
Medium Risk

Distributed Arithmetic for Machine Learning

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows minimal risk in terms of network usage, shell execution, obfuscation, and credential handling. However, the metadata risk score is elevated due to the maintainer's new or inactive account and lack of author information, making it suspicious.

  • Metadata risk due to maintainer's new or inactive account
  • Lack of detailed author information
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package requires network functionality.
  • Shell: No shell execution patterns detected, indicating no immediate signs of executing external commands.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The maintainer has a new or inactive account and lacks author information, raising some suspicion but not conclusive evidence of malice.

📦 Package Quality Overall: Low (3.4/10)

○ Low Test Suite 1.0

No test suite detected

  • No test files or test-runner configuration detected
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (2100 chars)
○ Low Contributing Guide 4.0

No contributing guide or governance files found

  • Development Status classifier >= Beta
○ Low Type Annotations 1.0

No type annotations detected

  • No type annotations, py.typed marker, or stub files detected
◈ Medium Multiple Contributors 6.0

Limited contributor diversity

  • 2 unique contributor(s) across 100 commits in calad0i/alkaid
  • Two distinct contributors found

🔬 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: cern.ch>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository calad0i/alkaid 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 alkaid 
Create a distributed machine learning arithmetic application using the 'alkaid' package. Your task is to develop a mini-app that showcases the capabilities of 'alkaid' in performing distributed arithmetic operations essential for machine learning tasks. The application should allow users to upload datasets, perform basic arithmetic operations on the data in a distributed manner, and visualize the results. Here are the steps and features you need to implement:

1. **Setup**: Initialize your project with necessary dependencies including 'alkaid'. Ensure that the environment supports distributed computing.
2. **Data Upload**: Implement a feature where users can upload their datasets. Support common file formats like CSV and Excel.
3. **Distributed Arithmetic Operations**: Use 'alkaid' to perform arithmetic operations such as addition, subtraction, multiplication, and division on the uploaded dataset in a distributed manner. Highlight how 'alkaid' simplifies these operations across multiple nodes.
4. **Visualization**: Provide visualizations of the dataset before and after arithmetic operations. Use libraries like Matplotlib or Seaborn to create graphs and charts.
5. **Interactive Interface**: Develop a simple web interface using Flask or Django where users can interact with the application, upload files, select operations, and view results.
6. **Documentation**: Write comprehensive documentation explaining how each part of the application works, especially focusing on how 'alkaid' is integrated into the solution.

This project aims to demonstrate the power of 'alkaid' in simplifying complex distributed arithmetic tasks for machine learning, making it accessible and understandable through practical application.

💬 Discussion Feed

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