🤖 AI Analysis

Final verdict: SAFE

The package shows low risks across multiple categories, with no detected network calls, shell executions, obfuscations, or credential harvesting attempts. The metadata suggests a potentially new maintainer, but this alone does not indicate malicious intent.

No network calls
No shell execution patterns
No obfuscation
No credential harvesting patterns
Single package from maintainer

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 patterns detected, indicating no immediate risk of command injection or unauthorized system access.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The maintainer has only one package, suggesting a new or less active account which could be suspicious but not conclusive.

🔬 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

Repository Koratahiu/Advanced_Optimizers appears legitimate

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

Author "Koratahiu" 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 adv-optm

Create a mini-application named 'OptiBench' that serves as a benchmarking tool for different optimization algorithms provided by the 'adv-optm' package. OptiBench should allow users to compare the performance of various optimizers on a set of predefined optimization problems. Here's a detailed breakdown of the application's requirements and features:

1. **User Interface**: Develop a simple command-line interface (CLI) using Python that allows users to select from a list of available optimizers provided by 'adv-optm'. Each optimizer should have a unique identifier and a brief description.
2. **Optimization Problems**: Include a selection of common optimization problems such as linear regression, logistic regression, and neural network training. These problems should be implemented using popular machine learning libraries like Scikit-learn or TensorFlow.
3. **Benchmarking Process**: For each selected problem and optimizer combination, run multiple trials with varying hyperparameters (e.g., learning rate, batch size). Collect metrics such as convergence time, number of iterations to reach a solution, and final loss/error value.
4. **Visualization**: Implement basic plotting capabilities to visualize the results. Users should be able to see how different optimizers perform across different problems in terms of speed and accuracy.
5. **Report Generation**: After completing the benchmarking process, generate a report summarizing the findings. This report should include tables and charts comparing the performance of the optimizers.
6. **Utilization of 'adv-optm' Package**: Ensure that the 'adv-optm' package is properly installed and imported into your project. Use its core functionalities to define the optimization processes and integrate them seamlessly with the chosen machine learning models.
7. **Documentation**: Provide clear documentation explaining how to install and use OptiBench, including any dependencies and setup instructions. Also, document the structure of the project and how 'adv-optm' is integrated within it.