algebraic-arrays

v1.3.7 safe
3.0
Low Risk

Abstract algebric structures for GPU-efficient computation

🤖 AI Analysis

Final verdict: SAFE

The package exhibits minimal risks across all categories, with no network or shell activity detected. While metadata suggests low maintenance, this alone does not indicate malicious intent.

  • Low network and shell risk
  • No signs of obfuscation or credential harvesting
  • Metadata suggests low maintenance but no evidence of malicious activities
Per-check LLM notes
  • Network: No network calls detected, which is normal for a math-oriented library.
  • Shell: No shell execution patterns detected, aligning with expectations for an algebraic arrays package.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The package shows signs of low maintenance and effort, but there's no clear indication of malicious intent.

📦 Package Quality Overall: Low (4.8/10)

✦ High Test Suite 9.0

Test suite present — 20 test file(s) found

  • Test runner config found: conftest.py
  • Test runner config found: conftest.py
  • 20 test file(s) detected (e.g. conftest.py)
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "Documentation" -> https://docs.anandb.dev/algebraic
  • Detailed PyPI description (8897 chars)
○ Low Contributing Guide 2.0

No contributing guide or governance files found

  • No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
◈ Medium Type Annotations 5.0

Partial type annotation coverage

  • 585 type-annotated function signatures detected in source
○ Low Multiple Contributors 1.0

Unable to verify contributor count: no GitHub repository found

  • No GitHub repository linked — contributor count unavailable

🔬 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 6.0

3 maintainer concern(s) found

  • Author name is missing or very short
  • Author "" appears to have only 1 package on PyPI (new or inactive account)
  • Package has no PyPI classifiers (low effort / metadata quality)
✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with algebraic-arrays 
Create a Python-based mini-application that leverages the 'algebraic-arrays' package to perform efficient GPU-accelerated linear algebra operations. This application will serve as a simple yet powerful tool for researchers and students who need to handle large datasets with complex mathematical operations. Here’s a detailed breakdown of what your application should include:

1. **User Interface**: Develop a user-friendly command-line interface (CLI) that allows users to input matrices and vectors for various operations.
2. **Core Operations**: Implement basic linear algebra operations such as matrix multiplication, vector addition, scalar multiplication, and determinant calculation using the 'algebraic-arrays' package.
3. **Advanced Features**: Extend functionality to include more advanced operations like Singular Value Decomposition (SVD), Eigenvalue decomposition, and QR factorization.
4. **Performance Benchmarking**: Include a feature to compare the performance of these operations when executed on CPU versus GPU, highlighting the efficiency gains from GPU acceleration.
5. **Visualization**: Integrate a basic plotting module (using matplotlib) to visualize the results of operations like SVD and Eigenvalue decomposition.
6. **Documentation and Help**: Provide comprehensive documentation and a help section within the CLI to guide users through the available commands and functionalities.

**Utilizing 'algebraic-arrays':** 
- Use the package's abstract algebraic structures to represent matrices and vectors efficiently for GPU computations.
- Leverage the package's optimization for parallel processing tasks, ensuring that the application can handle large datasets swiftly and accurately.
- Ensure that all operations are designed to take full advantage of the GPU capabilities provided by 'algebraic-arrays', optimizing for speed and resource management.

Your task is to create a fully functional mini-application that not only showcases the power of 'algebraic-arrays' but also serves as a practical tool for anyone needing to perform linear algebra operations on large datasets.

💬 Discussion Feed

Leave a comment

No discussion yet. Be the first to share your thoughts!