Reconstructed Slides: Deterministic Infra for Non-Deterministic AI Agents - Nishant Gupta, Meta Superintelligence Labs

Source Video

Deterministic Infra for Non-Deterministic AI Agents - Nishant Gupta, Meta Superintelligence Labs

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, 543] score 176.52

• Source frame: slide-002.jpg
• Crop: full [0, 0, 960, 543] score 177.86

• Source frame: slide-003.jpg
• Crop: contour [0, 0, 960, 543] score 177.96

• Source frame: slide-004.jpg
• Crop: full [0, 0, 960, 543] score 176.41

• Source frame: slide-005.jpg
• Crop: contour [0, 0, 960, 543] score 176.82

• Source frame: slide-006.jpg
• Crop: full [0, 0, 960, 543] score 178.25

• Source frame: slide-007.jpg
• Crop: contour [0, 0, 960, 543] score 176.3

• Source frame: slide-008.jpg
• Crop: full [0, 0, 960, 543] score 175.92

• Source frame: slide-009.jpg
• Crop: full [0, 0, 960, 543] score 176.43

• Source frame: slide-010.jpg
• Crop: full [0, 0, 960, 543] score 174.33

• Source frame: slide-011.jpg
• Crop: contour [0, 0, 960, 543] score 175.21

• Source frame: slide-012.jpg
• Crop: contour [0, 0, 960, 543] score 172.46

• Source frame: slide-013.jpg
• Crop: full [0, 0, 960, 543] score 177.75

Dense Scene-Detected Slide Candidates

• youtube APh1Vx0oLmQ dense slides