arrlp

v0.1.25 suspicious
4.0
Medium Risk

A library that provides tools for arrays on CPU & GPU [any shape].

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package arrLP has a moderate risk score due to potential typosquatting targeting 'arrow'. Despite low risks in network calls, shell execution, obfuscation, and credential handling, the metadata risk raises concerns about the maintainer's effort and intent.

  • Potential typosquatting
  • Low maintainer effort
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package requires network interaction 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 intent.
  • Credentials: No credential harvesting patterns detected, indicating safe handling of secrets and credentials.
  • Metadata: The package shows signs of low maintainer effort and potential typosquatting.
  • ⚠ Typosquatting target: arrow

📦 Package Quality Overall: Low (4.4/10)

✦ High Test Suite 9.0

Test suite present — 6 test file(s) found

  • 6 test file(s) detected (e.g. test_FunctionArray.py)
◈ Medium Documentation 5.0

Some documentation present

  • Brief PyPI description (572 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

  • 4 type-annotated function signatures (partial)
○ 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 score 3.0

Possible typosquat of: arrow

  • "arrlp" is 2 edit(s) from "arrow"
✓ Registered Email Domain

No author email provided

✓ 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 arrlp 
Create a mini-application called 'ArrayMaster' which leverages the 'arrlp' library to handle large-scale array computations both on CPU and GPU. This application will serve as a versatile tool for researchers and data scientists who need to perform complex operations on multidimensional arrays efficiently.

### Features:
1. **Array Initialization**: Users can initialize arrays of any shape, including high-dimensional ones, either on CPU or GPU. The application should support various initializers like zeros, ones, random values, etc.
2. **Basic Operations**: Implement basic arithmetic operations (+, -, *, /) on these arrays. These operations should be capable of handling both single and multiple arrays simultaneously.
3. **Advanced Operations**: Include more advanced operations such as matrix multiplication, element-wise exponentiation, and reduction operations like sum, mean, max, min across any axis.
4. **Visualization**: Integrate visualization capabilities to plot the arrays in 2D or 3D (if applicable). Use libraries like Matplotlib or Plotly for this purpose.
5. **Performance Comparison**: Provide a feature that allows users to compare the performance of operations when executed on CPU vs GPU. Display results in a tabular format showing time taken and any relevant metrics.
6. **User Interface**: Develop a simple command-line interface (CLI) for easy interaction. Additionally, consider integrating a web-based UI using Flask or Django for a more user-friendly experience.
7. **Documentation and Help**: Ensure comprehensive documentation and help sections are available within the application.

### Utilization of 'arrlp':
- Use 'arrlp' for initializing arrays and performing operations on them. Explore its functionalities to understand how it simplifies working with arrays on different hardware platforms.
- Leverage 'arrlp' to demonstrate the efficiency gains from using GPU over CPU for certain types of operations.
- Consider extending 'arrlp' functionalities if necessary to better suit your application's needs.

### Deliverables:
- A fully functional 'ArrayMaster' application.
- Source code with comments explaining each part of the implementation.
- Documentation detailing the setup process, usage instructions, and examples.
- A report discussing the performance comparison between CPU and GPU operations.

This project aims to showcase the power and flexibility of 'arrlp' while providing a practical tool for users dealing with large datasets and complex array operations.

💬 Discussion Feed

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