Callout Component Debug Tests
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Callout Component Debug Tests

Michael Brenndoerfer•1 min read•113 words•Interactive

Debug notebook for testing all callout component types and list rendering configurations

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Callout Component Debug Notebook

This notebook tests all callout types with various list configurations to identify rendering issues.

Test 1: Hint with Auto-Detected Ordered List

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Test 2: Warning with Auto-Detected Unordered List

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Test 3: Thought with Explicit Ordered List

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Test 4: Note with Explicit Unordered List

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Test 5: Hint with No List (Single Paragraph)

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Test 6: Warning with Mixed Content

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Test 7: Thought with Bold Prefixed Items (MDX-stripped numbers)

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Test 8: Note with KaTeX and List

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Test 9: Hint with Multiple Lists

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Test 10: Warning with Long List Items

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Test 11: Thought with Empty Lines in List

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Test 12: Note with Numbered Items Without Periods

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Test 13: All Four Types Side by Side

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Reference

BIBTEXAcademic
@misc{calloutcomponentdebugtests, author = {Michael Brenndoerfer}, title = {Callout Component Debug Tests}, year = {2025}, url = {https://mbrenndoerfer.com/writing/callout-debug-meta}, organization = {mbrenndoerfer.com}, note = {Accessed: 2025-10-05} }
APAAcademic
Michael Brenndoerfer (2025). Callout Component Debug Tests. Retrieved from https://mbrenndoerfer.com/writing/callout-debug-meta
MLAAcademic
Michael Brenndoerfer. "Callout Component Debug Tests." 2025. Web. 10/5/2025. <https://mbrenndoerfer.com/writing/callout-debug-meta>.
CHICAGOAcademic
Michael Brenndoerfer. "Callout Component Debug Tests." Accessed 10/5/2025. https://mbrenndoerfer.com/writing/callout-debug-meta.
HARVARDAcademic
Michael Brenndoerfer (2025) 'Callout Component Debug Tests'. Available at: https://mbrenndoerfer.com/writing/callout-debug-meta (Accessed: 10/5/2025).
SimpleBasic
Michael Brenndoerfer (2025). Callout Component Debug Tests. https://mbrenndoerfer.com/writing/callout-debug-meta
Michael Brenndoerfer

About the author: Michael Brenndoerfer

All opinions expressed here are my own and do not reflect the views of my employer.

Michael currently works as an Associate Director of Data Science at EQT Partners in Singapore, where he drives AI and data initiatives across private capital investments.

With over a decade of experience spanning private equity, management consulting, and software engineering, he specializes in building and scaling analytics capabilities from the ground up. He has published research in leading AI conferences and holds expertise in machine learning, natural language processing, and value creation through data.

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