docs: add crisis learning examples to ai-provider-agnosticism principle#936
docs: add crisis learning examples to ai-provider-agnosticism principle#936atxtechbro wants to merge 2 commits intomainfrom
Conversation
…cism - Add concrete Claude 500 error → Amazon Q resilience story - Include ROI math: 2880x productivity preservation (4 hours vs 5 seconds) - Document crisis-to-solution timeline (crisis to working system in 30 minutes) - Add template for future crises and principle validation section - Show real-world proof of provider agnosticism value under pressure Principle: systems-stewardship Principle: ai-provider-agnosticism
|
⏳ I'm reviewing this pull request for security vulnerabilities and code quality issues. I'll provide an update when I'm done |
![amazon-q-developer[bot]](https://avatars.githubusercontent.com/in/1220912?s=60&v=4){kind=small}
| raise ValueError(f"Invalid file path: {file}") | ||
|
|
||
| # Check for company-notes patterns (security: prevent accidental leaks) | ||
| import fnmatch |
There was a problem hiding this comment.
Description: The code uses broad library imports instead of importing specific required class, which consumes unnecessary memory and makes code maintenance harder by obscuring actual library usage. To optimize performance and improve code clarity, use targeted imports with 'from library import specific_class' syntax. Learn More https://docs.python.org/3/tutorial/modules.html.
Severity: Medium
There was a problem hiding this comment.
The remediation is made by replacing the broad import statement with a specific import of the required function from the fnmatch module. This optimizes performance and improves code clarity by explicitly showing which part of the module is being used.
| import fnmatch | |
| raise ValueError(f"Invalid file path: {file}") | |
| # Check for company-notes patterns (security: prevent accidental leaks) | |
| # from fnmatch import fnmatch # Used for pattern matching against filenames | |
| blocked_patterns = [] | |
| for file in files: | |
| # Normalize path separators |
|
✅ I finished the code review, and left comments with the issues I found. I will now generate code fix suggestions. |
1 participant
Summary
Enhances the ai-provider-agnosticism principle with real crisis learning examples from Claude 500 errors and Amazon Q resilience testing.
Changes
✅ Crisis Learning Examples
✅ ROI Mathematics
✅ Implementation Timeline
✅ Template for Future Crises
✅ Principle Validation
Why This Matters
Closes #933
Test plan