ai9414

v0.1.2 suspicious
3.0
Low Risk

Interactive teaching demos for AI search, logic, planning, CSP, uncertainty, and tokenisation.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package has low individual risks but raises suspicion due to incomplete author information and potentially inactive account.

  • Incomplete author metadata
  • New or inactive account
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package's functionality requires external API interactions.
  • Shell: No shell executions detected, indicating the package does not attempt to execute system commands.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious obfuscation.
  • Credentials: No credential harvesting patterns detected, indicating low risk of malicious activity.
  • Metadata: The author's information is incomplete and the account seems new or inactive, raising some suspicion but not conclusive evidence of malice.

📦 Package Quality Overall: Low (4.6/10)

◈ Medium Test Suite 6.0

Partial test coverage signals detected

  • Test runner config found: pyproject.toml
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (13126 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

  • 161 type-annotated function signatures detected in source
◈ Medium Multiple Contributors 5.0

Limited contributor diversity

  • 1 unique contributor(s) across 28 commits in OliverObst/artificial-intelligence
  • Single author but highly active (28 commits)

🔬 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: unsw.edu.au>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository OliverObst/artificial-intelligence 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 ai9414 
Create a Python-based educational mini-app named 'AI Explorer' that leverages the 'ai9414' package to teach fundamental concepts of Artificial Intelligence through interactive demonstrations. This app should allow users to explore topics such as AI search algorithms, logic reasoning, planning, constraint satisfaction problems (CSP), uncertainty management, and tokenization techniques. Here are the steps and features to include:

1. **Setup and Introduction**: Start with a user-friendly interface that introduces the app and explains its purpose. Allow users to select which topic they wish to explore.

2. **AI Search Algorithms Demonstration**: Implement interactive visualizations of common search algorithms like Depth-First Search (DFS), Breadth-First Search (BFS), A* Search, and others. Users should be able to set up different graph structures and see the algorithm in action.

3. **Logic Reasoning Section**: Use 'ai9414' to demonstrate logical reasoning using propositional and predicate logic. Include examples where users can input simple logical expressions and see how these expressions are evaluated and simplified.

4. **Planning Module**: Showcase planning problems by allowing users to define initial states, goal states, and actions. The app should then generate a plan to reach the goal state from the initial state, illustrating the use of heuristic functions and search strategies.

5. **Constraint Satisfaction Problems (CSP)**: Provide an interactive section where users can define CSPs, such as Sudoku puzzles or map coloring problems, and see the solution process step-by-step.

6. **Uncertainty Management**: Introduce concepts of uncertainty in AI, such as probability theory and Bayesian networks, with interactive examples where users can manipulate probabilities and observe outcomes.

7. **Tokenization Techniques**: Utilize 'ai9414' to demonstrate tokenization methods used in natural language processing (NLP). Users should be able to input text and see it broken down into tokens, with explanations of each step in the process.

8. **Conclusion and Resources**: At the end of the exploration, provide a summary of key learnings and direct users to additional resources for further study.

Throughout the development, ensure that 'ai9414' is integrated effectively to provide the underlying functionality for each module. The app should be designed to enhance understanding and engagement with AI concepts through hands-on interaction.