avelang

v0.0.0 suspicious
4.0
Medium Risk

n experimental Pythonic language for writing GPU kernels

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows no signs of immediate malicious activity such as network calls or shell execution. However, the metadata risk score of 3 out of 10, due to the package being new and maintained by a single author with limited history, raises some concern.

  • Metadata risk due to new package and single author
  • Low risk of network calls, shell execution, obfuscation, and credential harvesting
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 detected, reducing the risk of executing unauthorized commands.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious obfuscation.
  • Credentials: No credential harvesting patterns detected, indicating low risk of secret theft.
  • Metadata: The package appears to be new and maintained by a single author with limited history, raising some suspicion but not conclusive evidence of malice.

📦 Package Quality Overall: Low (2.0/10)

○ Low Test Suite 1.0

No test suite detected

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

Some documentation present

  • Brief PyPI description (260 chars)
○ Low Contributing Guide 2.0

No contributing guide or governance files found

  • No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
○ Low Type Annotations 1.0

No type annotations detected

  • No type annotations, py.typed marker, or stub files detected
○ 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

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 4.0

2 maintainer concern(s) found

  • Only one version has ever been released — brand new package
  • Author "Haohui Mai" 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 avelang 
Create a fully-functional mini-application using the 'avelang' package, which is designed for writing GPU kernels in Python. Your goal is to develop a simple yet powerful image processing tool that leverages the power of GPU acceleration through avelang. This application will allow users to apply various filters to images, such as grayscale conversion, edge detection, and color balance adjustments, all powered by GPU-accelerated computations.

### Project Steps:
1. **Setup**: Install necessary dependencies including 'avelang', any required image processing libraries (such as PIL or OpenCV), and ensure you have access to a CUDA-enabled GPU.
2. **Image Loading & Display**: Implement functionality to load images from disk and display them before and after applying filters.
3. **Filter Implementation**: Use 'avelang' to write GPU-accelerated kernels for each filter type. For example, implement a kernel for converting an image to grayscale, another for detecting edges using Sobel operators, and a third for adjusting color balance.
4. **User Interface**: Develop a basic command-line interface (CLI) that allows users to select an image file, choose a filter, and view the result. Optionally, extend this to include a graphical user interface (GUI) using a library like PyQt or Tkinter.
5. **Performance Testing**: Measure and compare the performance of your GPU-accelerated filters against CPU-only implementations to demonstrate the benefits of using 'avelang'.
6. **Documentation**: Write comprehensive documentation explaining how to install and use the application, along with details on the 'avelang' package and its role in the project.

### Suggested Features:
- Support for multiple image formats (JPEG, PNG, BMP).
- Real-time preview of filter effects when using a GUI.
- Ability to save processed images to disk.
- Detailed error handling and informative messages.
- Optional: Allow users to create custom filters by writing their own kernels in 'avelang'.

### Utilizing 'avelang':
- Define functions in 'avelang' syntax that perform specific image processing tasks.
- Compile these functions into GPU kernels using 'avelang' tools.
- Call these compiled kernels from your main application code to process images.
- Experiment with different optimization techniques provided by 'avelang' to improve performance.

💬 Discussion Feed

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