Reconstructed Slides: Dream Machine: Scaling to 1m users in 4 days — Keegan McCallum, Luma AI

Source Video

Dream Machine: Scaling to 1m users in 4 days — Keegan McCallum, Luma AI

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 160.16

• Source frame: slide-002.jpg
• Crop: full [0, 0, 960, 540] score 153.46

• Source frame: slide-003.jpg
• Crop: full [0, 0, 960, 540] score 162.26

• Source frame: slide-004.jpg
• Crop: full [0, 0, 960, 540] score 160.42

• Source frame: slide-005.jpg
• Crop: full [0, 0, 960, 540] score 156.99

• Source frame: slide-006.jpg
• Crop: full [0, 0, 960, 540] score 159.45

• Source frame: slide-007.jpg
• Crop: full [0, 0, 960, 540] score 159.08

• Source frame: slide-008.jpg
• Crop: contour [0, 0, 960, 540] score 177.4

• Source frame: slide-009.jpg
• Crop: full [0, 0, 960, 540] score 166.26

• Source frame: slide-010.jpg
• Crop: full [0, 0, 960, 540] score 161.4

• Source frame: slide-011.jpg
• Crop: full [0, 0, 960, 540] score 157.89

• Source frame: slide-012.jpg
• Crop: full [0, 0, 960, 540] score 158.57

• Source frame: slide-013.jpg
• Crop: full [0, 0, 960, 540] score 154.85

• Source frame: slide-014.jpg
• Crop: full [0, 0, 960, 540] score 167.13

• Source frame: slide-015.jpg
• Crop: full [0, 0, 960, 540] score 158.86

• Source frame: slide-016.jpg
• Crop: full [0, 0, 960, 540] score 158.17

• Source frame: slide-017.jpg
• Crop: full [0, 0, 960, 540] score 175.74