AI Analysis
Final verdict: SUSPICIOUS
The package shows signs of potential obfuscation and has low maintainer activity, which raises concerns about its legitimacy and potential risks.
- Obfuscation risk due to use of tup methods
- Poor metadata quality and low maintainer activity
Per-check LLM notes
- Network: No network calls detected, which is normal if the package does not require internet access.
- Shell: No shell execution detected, indicating the package does not perform system commands that could be exploited.
- Obfuscation: The use of tup methods and avoidance of eval() could indicate an attempt to obfuscate code, but it's not conclusive without further context.
- Credentials: No clear patterns indicative of credential harvesting were detected.
- Metadata: The package shows low maintainer activity and poor metadata quality, raising concerns about its legitimacy and potential risks.
Package Quality Overall: Medium (5.4/10)
✦ High
Test Suite
9.0
Test suite present — 3 test file(s) found
3 test file(s) detected (e.g. test_multilevel.py)
◈ Medium
Documentation
7.0
Some documentation present
Documentation URL: "Documentation" -> https://github.com/vboussange/AMJax#readmeDetailed PyPI description (3356 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
✦ High
Multiple Contributors
8.0
Active multi-contributor project
3 unique contributor(s) across 34 commits in vboussange/AMJaxSmall but multi-author team (3–4 contributors)
Heuristic Checks
Outbound Network Calls
No suspicious network call patterns found
Code Obfuscation
score 2.0
Found 1 obfuscation pattern(s)
tup methods and avoids use of eval(). """ _REGISTRY = { "jacobi": setup_jacob
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: epfl.ch>
Suspicious Page Links
All external links appear legitimate
Git Repository History
score 2.5
Git history flags: Repository has zero stars and zero forks
Repository has zero stars and zero forks
Maintainer History
score 6.0
3 maintainer concern(s) found
Author 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 amjax
Develop a mini-application that solves complex linear systems using the 'amjax' package, which provides algebraic multigrid solvers built on top of JAX. Your application should allow users to input their own sparse matrices and vectors to solve Ax=b problems, where A is a large sparse matrix and x and b are vectors. The app will demonstrate the efficiency and scalability of AMG methods over traditional iterative solvers like Conjugate Gradient for certain types of matrices. Key Features: 1. User Interface: Create a simple command-line interface (CLI) or a basic web UI where users can input their matrix and vector data in various formats (e.g., CSV, JSON). 2. Problem Setup: Allow users to choose between solving a predefined set of problems or uploading their own matrix-vector pairs. 3. Solver Selection: Implement a feature where users can select between different AMG solvers provided by 'amjax'. Include at least two options: a standard AMG solver and a smoothed aggregation AMG solver. 4. Performance Comparison: For each problem solved, compare the performance of the chosen AMG solver against a basic iterative solver like Conjugate Gradient implemented in JAX. Display metrics such as time taken to converge, number of iterations required, and the final error norm. 5. Visualization: Provide visual outputs of the solution process, such as convergence plots and residual histories, to help users understand the solver behavior. 6. Documentation: Ensure comprehensive documentation is available both within the codebase and as part of the user interface, explaining how AMG works, why it might be preferred over other methods, and how to use the application effectively. Utilizing 'amjax': - Use 'amjax' to initialize and configure the AMG solvers. This includes setting up the solver parameters, specifying the type of AMG method to use, and applying it to the problem matrix. - Integrate JAX functionalities to handle matrix operations efficiently and to implement the comparison solver. - Leverage JAX's automatic differentiation capabilities if needed for any advanced features or optimizations.
💬 Discussion Feed
No discussion yet. Be the first to share your thoughts!
Report Abuse / Security Issue