Reconstructed Slides: How LLMs work for Web Devs: GPT in 600 lines of Vanilla JS - Ishan Anand

Source Video

How LLMs work for Web Devs: GPT in 600 lines of Vanilla JS - Ishan Anand

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 160.13

• Source frame: slide-002.jpg
• Crop: full [0, 0, 960, 540] score 176.62

• Source frame: slide-003.jpg
• Crop: full [0, 0, 960, 540] score 165.6

• Source frame: slide-004.jpg
• Crop: full [0, 0, 960, 540] score 166.68

• Source frame: slide-005.jpg
• Crop: full [0, 0, 960, 540] score 177.55

• Source frame: slide-006.jpg
• Crop: full [0, 0, 960, 540] score 176.22

• Source frame: slide-007.jpg
• Crop: full [0, 0, 960, 540] score 175.72

• Source frame: slide-008.jpg
• Crop: full [0, 0, 960, 540] score 167.11

• Source frame: slide-009.jpg
• Crop: full [0, 0, 960, 540] score 178.11

• Source frame: slide-010.jpg
• Crop: full [0, 0, 960, 540] score 167.16

• Source frame: slide-011.jpg
• Crop: full [0, 0, 960, 540] score 176.8

Dense Scene-Detected Slide Candidates

• youtube ZuiJjkbX0Og dense slides