Reconstructed Slides: Make your LLM app a Domain Expert: How to Build an Expert System — Christopher Lovejoy, Anterior

Source Video

Make your LLM app a Domain Expert: How to Build an Expert System — Christopher Lovejoy, Anterior

Method

This deck is reconstructed from the existing video frame captures by detecting likely slide regions with OpenCV, cropping/upscaling those regions, deduplicating similar crops, and OCRing the cropped slide images locally. It is a cleaner companion to the full-stage frame deck.

Reconstructed Slides

• Source frame: slide-001.jpg
• Crop: full [0, 0, 960, 540] score 160.12

• Source frame: slide-002.jpg
• Crop: full [0, 0, 960, 540] score 165.19

• Source frame: slide-003.jpg
• Crop: full [0, 0, 960, 540] score 163.89

• Source frame: slide-004.jpg
• Crop: full [0, 0, 960, 540] score 158.96

• Source frame: slide-005.jpg
• Crop: full [0, 0, 960, 540] score 157.95

• Source frame: slide-006.jpg
• Crop: full [0, 0, 960, 540] score 158.99

• Source frame: slide-007.jpg
• Crop: full [0, 0, 960, 540] score 157.16

• Source frame: slide-008.jpg
• Crop: full [0, 0, 960, 540] score 159.55

• Source frame: slide-009.jpg
• Crop: full [0, 0, 960, 540] score 158.75

• Source frame: slide-010.jpg
• Crop: full [0, 0, 960, 540] score 166.69

• Source frame: slide-011.jpg
• Crop: full [0, 0, 960, 540] score 158.1

• Source frame: slide-012.jpg
• Crop: full [0, 0, 960, 540] score 158.17

• Source frame: slide-013.jpg
• Crop: full [0, 0, 960, 540] score 161.17

• Source frame: slide-014.jpg
• Crop: full [0, 0, 960, 540] score 156.45

• Source frame: slide-015.jpg
• Crop: full [0, 0, 960, 540] score 161.6

• Source frame: slide-016.jpg
• Crop: full [0, 0, 960, 540] score 164.39