adaptive-intelligence

v3.0.1 suspicious
5.0
Medium Risk

Self-improving retrieval orchestration framework with RL-based routing, conditional graph activation, and evaluation-driven learning.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows signs of obfuscation and has a new maintainer with limited history, indicating potential risks that need further investigation.

  • High obfuscation risk due to dynamic imports
  • New maintainer with limited package history
Per-check LLM notes
  • Network: The package makes network requests to an API endpoint, which could be for legitimate purposes like fetching data or updates, but further investigation is needed to confirm its legitimacy and purpose.
  • Shell: No shell execution patterns were detected.
  • Obfuscation: The use of __import__ with dynamic import strings is often seen in obfuscated or minified code, suggesting potential risk.
  • Credentials: No clear signs of credential harvesting detected.
  • Metadata: The maintainer has a new or inactive account with limited package history and missing author information, raising some suspicion but not conclusive evidence of malice.

🔬 Heuristic Checks

⚠ Outbound Network Calls score 7.5

Found 5 network call pattern(s)

  • lib.request req = urllib.request.Request(f"{self.base_url}/api/tags") with urllib
  • }/api/tags") with urllib.request.urlopen(req, timeout=3) as resp: return resp
  • encode("utf-8") req = urllib.request.Request( f"{self.base_url}/api/chat",
  • try: with urllib.request.urlopen(req, timeout=120) as resp: result =
  • encode("utf-8") req = urllib.request.Request( f"{self.base_url}/chat/completions",
⚠ Code Obfuscation score 4.0

Found 2 obfuscation pattern(s)

  • try: __import__(dep) results[dep] = {"status": "ok", "available"
  • try: __import__(dep) available = True br
✓ 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 VK-Ant/adaptive-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 adaptive-intelligence 
Create a mini-application that serves as a personalized news recommendation system using the 'adaptive-intelligence' package. This application will fetch news articles from various sources, analyze user preferences based on their reading history, and dynamically adjust the recommendation algorithm to better suit individual tastes over time. Here are the steps and features to include:

1. **User Registration and Preferences**: Users can register and specify initial interests such as categories of news they prefer (politics, technology, sports, etc.).
2. **News Aggregation**: Implement a feature that aggregates news articles from multiple reliable sources. Ensure that the system supports real-time updates.
3. **Reading History Tracking**: Track which articles users read and how long they spend on each article to gauge interest level.
4. **Initial Recommendations**: Based on the user's stated preferences, generate a set of recommended articles.
5. **Adaptive Learning**: Use the 'adaptive-intelligence' package to implement reinforcement learning algorithms that adjust the recommendation strategy based on user engagement data (e.g., if a user frequently reads articles about technology but skips politics, the system should learn and adapt).
6. **Conditional Graph Activation**: Utilize the conditional graph activation feature of 'adaptive-intelligence' to create a dynamic network of user preferences and article categories, where connections between nodes (users and articles) strengthen or weaken based on user interaction patterns.
7. **Evaluation-Driven Learning**: Continuously evaluate the effectiveness of recommendations using metrics like click-through rate and time spent reading articles. Adjust the recommendation algorithm accordingly.
8. **User Feedback Loop**: Allow users to provide direct feedback on recommendations, which the system can use to further refine its recommendations.
9. **Dashboard Interface**: Develop a simple web interface where users can view their recommended articles, track their reading history, and adjust their preferences.

The goal is to demonstrate how the 'adaptive-intelligence' package can be effectively used to build a smarter, more personalized experience for users.