AI Analysis
The package shows minimal signs of potential risks, with no evidence of obfuscation or credential harvesting. The metadata suggests it's from a less active or newer account, but this alone is not sufficient to raise suspicion.
- Low obfuscation risk
- No credential harvesting detected
- Single package from author
Per-check LLM notes
- Obfuscation: No obfuscation patterns detected, indicating low risk.
- Credentials: No credential harvesting patterns detected, indicating low risk.
- Metadata: The author has only one package, which might indicate a new or less active account, but no other red flags are present.
Package Quality Overall: Low (1.6/10)
No test suite detected
No test files or test-runner configuration detected
No documentation detected
No documentation URL, doc files, or meaningful description found
No contributing guide or governance files found
Development Status classifier >= Beta
No type annotations detected
No type annotations, py.typed marker, or stub files detected
Unable to verify contributor count: no GitHub repository found
No GitHub repository linked — contributor count unavailable
Heuristic Checks
Found 1 network call pattern(s)
ball_url}...') try: urllib.request.urlretrieve(tarball_url, archive_name_local) except Exce
No obfuscation patterns detected
No shell execution patterns detected
No credential harvesting patterns detected
No typosquatting candidates detected
Email domain looks legitimate: tensorflow.org
All external links appear legitimate
No GitHub repository linked
No GitHub repository link found
1 maintainer concern(s) found
Author "Google AI Edge Authors" appears to have only 1 package on PyPI (new or inactive account)
No known vulnerabilities found in OSV database.
AI App Starter Prompt
Create a real-time object detection app using the 'ai-edge-litert-sdk-qualcomm' package. Your app will run on a Qualcomm-based device and leverage the LiteRT framework to perform efficient on-device inference. The app should capture video from a camera and continuously process frames to detect objects such as people, cars, and animals in real-time. Upon detection, the app should annotate the frame with bounding boxes and labels, then display the annotated video stream on the screen. Additionally, implement a feature to log detected objects into a local database for further analysis. Use the SDK's optimization capabilities to ensure smooth performance even on less powerful devices. Detail the setup process, including installing dependencies and setting up the environment, and provide instructions for running and testing the app.