AI Analysis
Final verdict: SUSPICIOUS
The package has low risks in terms of network, shell, obfuscation, and credential activities. However, it exhibits signs of low maintainer effort and suspicious author details, raising concerns about its legitimacy and potential for supply-chain attacks.
- Low maintainer effort
- Suspicious author details
Per-check LLM notes
- Network: No network calls detected, which is normal for a package focused on device-specific optimizations.
- Shell: No shell execution patterns detected, aligning with the expected behavior for a technical optimization library.
- Obfuscation: No obfuscation patterns detected, indicating low risk.
- Credentials: No credential harvesting patterns detected, indicating low risk.
- Metadata: The package shows signs of low maintainer effort and suspicious author details, indicating potential risk.
Package Quality Overall: Low (1.2/10)
○ Low
Test Suite
1.0
No test suite detected
No test files or test-runner configuration detected
○ Low
Documentation
1.0
No documentation detected
No documentation URL, doc files, or meaningful description found
○ 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
Email domain looks legitimate: example.com>
Suspicious Page Links
All external links appear legitimate
Git Repository History
No GitHub repository linked
No GitHub repository link found
Maintainer History
score 8.0
4 maintainer concern(s) found
Only one version has ever been released — brand new packageAuthor name is missing or very shortAuthor "" 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 amd-torch-device-gfx12-0
Create a Python-based mini-application that leverages the 'amd-torch-device-gfx12-0' package to perform real-time image processing on AMD GPUs. This application will showcase the power of AMD GPUs in handling complex image processing tasks efficiently. Here’s a detailed breakdown of what your application should achieve: 1. **Setup Environment**: Ensure you have Python installed along with necessary libraries such as PyTorch and the 'amd-torch-device-gfx12-0' package. Use virtual environments to manage dependencies. 2. **Image Loading and Preprocessing**: Implement functionality to load images from a directory or through a file dialog. Preprocess these images to fit the input requirements of the chosen model. 3. **Model Integration**: Utilize a pre-trained model (e.g., a convolutional neural network) that is compatible with AMD GPUs. The 'amd-torch-device-gfx12-0' package will be crucial here for optimizing the model’s performance on AMD hardware. 4. **Real-Time Processing**: Develop a feature that allows users to select an operation (e.g., edge detection, color enhancement) and apply it in real-time to the loaded images using the GPU-accelerated model. 5. **Output Display**: Show the processed images alongside the original ones for comparison. Additionally, provide an option to save the processed images. 6. **User Interface**: Although not mandatory, consider building a simple GUI using Tkinter or PyQt for better user interaction. **Suggested Features**: - Option to switch between different pre-processing techniques or models. - Performance metrics display (e.g., FPS) to show the benefits of using AMD GPUs. - A logging system to track operations and errors. This project aims to demonstrate the capabilities of AMD GPUs in accelerating deep learning tasks, specifically focusing on image processing, while showcasing the ease of use with the 'amd-torch-device-gfx12-0' package.
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