aiondemand

v0.3.1 suspicious
4.0
Medium Risk

Obtain and share AI resources with ease

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package is rated suspicious due to the maintainer's new or inactive account and lack of author information, indicating potential low activity or lack of transparency.

  • The maintainer has a new or inactive account.
  • Lack of author information.
Per-check LLM notes
  • Metadata: The maintainer has a new or inactive account and lacks author information, which could indicate low activity or lack of transparency.

📦 Package Quality Overall: Medium (7.0/10)

✦ High Test Suite 9.0

Test suite present — 3 test file(s) found

  • Test runner config found: pyproject.toml
  • 3 test file(s) detected (e.g. __init__.py)
✦ High Documentation 9.0

Well-documented package

  • Documentation URL: "Documentation" -> https://aiondemand.github.io/aiondemand/
  • 1 documentation file(s) (e.g. asset_type_replacer.py)
  • Detailed PyPI description (6267 chars)
○ Low Contributing Guide 2.0

No contributing guide or governance files found

  • No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
◈ Medium Type Annotations 5.0

Partial type annotation coverage

  • 49 type-annotated function signatures detected in source
✦ High Multiple Contributors 10.0

Active multi-contributor project

  • 16 unique contributor(s) across 100 commits in aiondemand/aiondemand
  • Active community — 5 or more distinct contributors

🔬 Heuristic Checks

⚠ Outbound Network Calls score 9.0

Found 6 network call pattern(s)

  • ror occurs. """ res = requests.post( _bookmarks_url(), params={"resource_identif
  • ror occurs. """ res = requests.delete( _bookmarks_url(), params={"resource_identif
  • bookmarks. """ res = requests.get( _bookmarks_url(), headers=get_token().heade
  • en = aiod.get_token() requests.post(url, headers=token.headers, json=metadata) ```
  • val) token_response = requests.post( token_endpoint, data=token_data,
  • cated. """ response = requests.get( f"{config.api_server}authorization_test", h
⚠ Code Obfuscation score 8.0

Found 4 obfuscation pattern(s)

  • ress_method}") return eval(f"{compress_method}.compress(cls_str)") def _has_source(ob
  • rom e try: obj = eval(spec, globals(), register) except Exception: fro
  • ster, register) obj = eval("build_obj()", register, register) return obj def dep
  • ) >>> deserialized_dict = eval(serialized_dict) >>> assert deserialized_dict == my_dict
✓ 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: tue.nl>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository aiondemand/aiondemand appears legitimate

⚠ Maintainer History score 4.0

2 maintainer concern(s) found

  • Author name is missing or very short
  • Author "" 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 aiondemand 
Create a fully-functional mini-application named 'AIResourceHub' that utilizes the Python package 'aiondemand' to manage and distribute AI resources efficiently. This application should allow users to easily obtain AI models from various sources and share them with others, ensuring that all resources are properly managed and updated. Here are the steps and features to implement:

1. **Setup**: Begin by installing the required packages, including 'aiondemand'. Ensure that your application has a clean and user-friendly interface.
2. **User Authentication**: Implement a simple authentication system allowing users to sign up, log in, and log out. Users should have unique profiles where they can manage their shared resources.
3. **Resource Management**: Utilize 'aiondemand' to fetch AI models from different providers and store them locally or in a cloud storage service like AWS S3. The application should support various types of AI models such as image recognition, natural language processing, etc.
4. **Sharing Mechanism**: Allow users to share their AI models with others by generating unique links or embedding codes. Ensure that sharing options include permissions settings (public/private).
5. **Notifications System**: Implement a basic notification system using email or in-app messages to inform users about updates or new resources available.
6. **Search & Discovery**: Integrate a search feature that allows users to find specific AI models based on tags, descriptions, or other metadata.
7. **Feedback & Ratings**: Enable users to rate and provide feedback on shared resources, which will help improve the quality and relevance of the content in the hub.
8. **Documentation**: Provide comprehensive documentation explaining how to use the application, including API usage and best practices for managing AI resources.
9. **Testing & Deployment**: Thoroughly test the application for bugs and performance issues before deploying it to a production environment. Use Docker containers for easy deployment and scalability.

By following these steps and implementing the suggested features, you'll create a valuable tool for the AI community that leverages the capabilities of 'aiondemand' to streamline resource management and distribution.

💬 Discussion Feed

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