🤖 AI Analysis

Final verdict: SAFE

The package shows minimal risk indicators with no signs of obfuscation or credential harvesting. The metadata suggests new or inactive authors, but this alone does not indicate malicious intent.

Low obfuscation risk
No credential harvesting detected
Authors may be new or inactive

Per-check LLM notes

Obfuscation: No obfuscation patterns detected, suggesting low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The authors appear new or inactive but no other suspicious elements were found.

🔬 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 deep-spin/adasplash appears legitimate

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

Author "Nuno Gonçalves, Marcos Treviso" 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 adasplash

Create a real-time sentiment analysis web application using Flask and the 'adasplash' package for efficient adaptive sparse attention in Triton. Your application should allow users to input text snippets and receive a sentiment score indicating whether the text is positive, negative, or neutral. Utilize 'adasplash' to optimize the attention mechanism in your model, ensuring it can handle large volumes of text data efficiently.

Step 1: Set up your development environment with Python, Flask, and the necessary libraries including 'adasplash'.
Step 2: Integrate 'adasplash' into your sentiment analysis model to enable efficient processing of textual data through its adaptive sparse attention mechanism.
Step 3: Design a simple yet user-friendly web interface using HTML/CSS/JavaScript to accept text inputs from users and display sentiment scores.
Step 4: Implement backend logic in Flask to process the text inputs, use the sentiment analysis model to determine sentiment scores, and return these scores to the frontend.
Step 5: Test the application thoroughly to ensure it accurately processes and displays sentiment scores for various types of text inputs.
Step 6: Deploy the application to a cloud service provider like AWS or Heroku for public access.

Suggested Features:
- Display a histogram of sentiment scores over time.
- Allow users to view recent analyses and their sentiment scores.
- Provide a brief explanation of the sentiment score based on key phrases identified in the text.

How to Utilize 'adasplash':
- Use 'adasplash' to preprocess and encode the text data before passing it through the sentiment analysis model.
- Leverage 'adasplash's adaptive sparse attention capabilities to dynamically adjust the model's focus based on the complexity and length of the input text.