AI Analysis
Final verdict: SAFE
The package shows very low risk in terms of network, shell, and obfuscation activities. The metadata risk is slightly elevated due to sparse author information, but this alone does not indicate malicious intent.
- Low risk scores across all technical categories.
- Sparse author information warrants further investigation.
Per-check LLM notes
- Network: No network calls detected, which is normal unless the package requires external services.
- Shell: No shell execution patterns detected, indicating no immediate risk of unauthorized command execution.
- Obfuscation: No obfuscation patterns detected, indicating low risk of malicious activity.
- Credentials: No credential harvesting patterns detected, suggesting safe handling of sensitive information.
- Metadata: The author information is sparse, suggesting potential low activity or newness which may warrant further investigation.
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: users.noreply.github.com>
Suspicious Page Links
All external links appear legitimate
Git Repository History
Repository Thatgfsj/neuroweave-cortex appears legitimate
Maintainer History
score 4.0
2 maintainer concern(s) found
Author name is missing or very shortAuthor "" 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 NWcortex
Create a personalized AI assistant application using the NWcortex package. This AI assistant will utilize graph-first cognitive memory to provide context-aware responses and learn from interactions over time. Here’s a detailed breakdown of the steps and features you need to implement: 1. **Setup**: Initialize the NWcortex environment within your Python application. Configure the hippocampal-inspired architecture to manage the assistant's memory, including domain routing and spreading activation mechanisms. 2. **User Interaction**: Design an interface where users can input questions or commands. The AI assistant should respond based on its current state of knowledge, which is dynamically updated through each interaction. 3. **Learning Mechanism**: Implement a learning feature that allows the AI assistant to update its knowledge base after each interaction. Use the 4-layer compression and thermal storage features of NWcortex to efficiently store and recall information. 4. **Personality Modeling**: Develop a personality model for the AI assistant that reflects certain traits (e.g., friendly, informative, humorous). Adjust the assistant’s responses according to its personality profile. 5. **Sleep Consolidation**: Integrate the 8-phase sleep consolidation process into the AI assistant’s routine. This feature will help consolidate learned information and optimize memory efficiency during periods when the assistant is not actively interacting with users. 6. **Edge Budget Management**: Ensure that the AI assistant can handle multiple concurrent interactions without overwhelming its memory capacity. Use edge budget management techniques provided by NWcortex to manage resource allocation effectively. 7. **Testing & Evaluation**: Conduct tests to evaluate the effectiveness of the AI assistant’s learning and response capabilities. Analyze performance metrics such as accuracy, speed, and user satisfaction. By following these steps and utilizing the core features of NWcortex, your application will create a dynamic and engaging AI assistant that continuously improves through interaction.