An AI observability system that watches how models behave, detects when they fail, and explains why it happened. It tracks uncertainty and inconsistencies, groups similar mistakes, and turns them into preventive “auto-vaccines” to reduce overconfident errors and make AI systems more reliable.

4 stars 0 forks 4 watchers Jupyter Notebook Other
10 Open Issues Need Help Last updated: Aug 10, 2026

Open Issues Need Help

View All on GitHub
enhancement good first issue

An AI observability system that watches how models behave, detects when they fail, and explains why it happened. It tracks uncertainty and inconsistencies, groups similar mistakes, and turns them into preventive “auto-vaccines” to reduce overconfident errors and make AI systems more reliable.

Jupyter Notebook
enhancement good first issue

An AI observability system that watches how models behave, detects when they fail, and explains why it happened. It tracks uncertainty and inconsistencies, groups similar mistakes, and turns them into preventive “auto-vaccines” to reduce overconfident errors and make AI systems more reliable.

Jupyter Notebook
enhancement help wanted

An AI observability system that watches how models behave, detects when they fail, and explains why it happened. It tracks uncertainty and inconsistencies, groups similar mistakes, and turns them into preventive “auto-vaccines” to reduce overconfident errors and make AI systems more reliable.

Jupyter Notebook
enhancement help wanted

An AI observability system that watches how models behave, detects when they fail, and explains why it happened. It tracks uncertainty and inconsistencies, groups similar mistakes, and turns them into preventive “auto-vaccines” to reduce overconfident errors and make AI systems more reliable.

Jupyter Notebook
enhancement good first issue

An AI observability system that watches how models behave, detects when they fail, and explains why it happened. It tracks uncertainty and inconsistencies, groups similar mistakes, and turns them into preventive “auto-vaccines” to reduce overconfident errors and make AI systems more reliable.

Jupyter Notebook
enhancement good first issue

An AI observability system that watches how models behave, detects when they fail, and explains why it happened. It tracks uncertainty and inconsistencies, groups similar mistakes, and turns them into preventive “auto-vaccines” to reduce overconfident errors and make AI systems more reliable.

Jupyter Notebook
help wanted good first issue

An AI observability system that watches how models behave, detects when they fail, and explains why it happened. It tracks uncertainty and inconsistencies, groups similar mistakes, and turns them into preventive “auto-vaccines” to reduce overconfident errors and make AI systems more reliable.

Jupyter Notebook
help wanted good first issue

An AI observability system that watches how models behave, detects when they fail, and explains why it happened. It tracks uncertainty and inconsistencies, groups similar mistakes, and turns them into preventive “auto-vaccines” to reduce overconfident errors and make AI systems more reliable.

Jupyter Notebook
enhancement good first issue

An AI observability system that watches how models behave, detects when they fail, and explains why it happened. It tracks uncertainty and inconsistencies, groups similar mistakes, and turns them into preventive “auto-vaccines” to reduce overconfident errors and make AI systems more reliable.

Jupyter Notebook
enhancement good first issue

An AI observability system that watches how models behave, detects when they fail, and explains why it happened. It tracks uncertainty and inconsistencies, groups similar mistakes, and turns them into preventive “auto-vaccines” to reduce overconfident errors and make AI systems more reliable.

Jupyter Notebook