genieincodebottle / aiml-companion
AI-ML Companion: an interactive platform to learn AI & ML by watching it work - 22 tracks, 300+ modules, live visualizations - plus 10 end-to-end portfolio projects from classical ML to production LLM & multi-agent systems.
Code health
genieincodebottle/aiml-companion scores 8.9 out of 10 on defect risk, which Repowise rates Excellent. Maintainability scores 8.7 out of 10. Static performance risk scores 9.7 out of 10. The three are scored separately and never blended into a single number. 1 of 525 files are git hotspots, the places where changes and bug fixes concentrate.
Full health report- Files
- 525
- Symbols
- 5.7k
- Modules
- 6
- Doc pages
- 860
- Dead exports
- 68
Where the risk concentrates
All 1 hotspotsRanked by prior bug fixes and change frequency, mined from the full git history rather than from the code alone.
| File | Churn | Prior fixes | Maintainers | Commits 90d |
|---|---|---|---|---|
| app.pyprojects/ai-agents-project | 88.3th %ile | 0 | 1bus factor 1 | 3 |
Read more about genieincodebottle/aiml-companion
- OverviewFiles, symbols, languages, packages, git intelligence
- ArchitectureDependency graph, layered architecture, symbol index, and third-party dependencies
- Code healthPer-file health scores, hotspots, test coverage, dead code, and refactoring targets
- AI explanationsProModule-by-module documentation generated from source
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[](https://repowise.dev/repo/genieincodebottle/aiml-companion)Code health badge
Average health across every file, straight from the latest index.
[](https://repowise.dev/repo/genieincodebottle/aiml-companion)How genieincodebottle/aiml-companion works
AI-ML Companion: an interactive platform to learn AI & ML by watching it work - 22 tracks, 300+ modules, live visualizations - plus 10 end-to-end portfolio projects from classical ML to production LLM & multi-agent systems. This page is an auto-generated, always-fresh map of the genieincodebottle/aiml-companion repository, written primarily in Jupyter Notebook. Repowise indexes the source, parses every symbol, computes a dependency graph, scores per-file code health from complexity, duplication, test coverage and churn, mines git history for hotspots and ownership, and lifts the resulting architectural decisions into a wiki you can read or query through MCP.
The codebase has 525 source files, 5,718 symbols, and 11 languages, organised into 6 modules, led by python, javascript, markdown. Static analysis leaves 1,015 open findings across the three health dimensions.
Use the links above to open the interactive dashboards, or connect this repo to your editor via the Repowise MCP server for grounded answers inside Claude, Cursor, or VS Code.