---
title: "Reconstructed Slides: The 1,000x AI Engineer: Swyx"
category: "slides"
video_id: "qaJXBMwUkoE"
sourceLabels: ["Cropped public YouTube video frames", "Local OpenCV slide-region detection", "Local RapidOCR"]
---

# Reconstructed Slides: The 1,000x AI Engineer: Swyx

## Source Video
[The 1,000x AI Engineer: Swyx](https://www.youtube.com/watch?v=qaJXBMwUkoE)

## 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
![[assets/reconstructed-slides/qaJXBMwUkoE/slide-001.jpg]]

- Source frame: `slide-001.jpg`
- Crop: `full` `[0, 0, 960, 540]` score `153.62`
![[assets/reconstructed-slides/qaJXBMwUkoE/slide-002.jpg]]

- Source frame: `slide-002.jpg`
- Crop: `full` `[0, 0, 960, 540]` score `159.78`
![[assets/reconstructed-slides/qaJXBMwUkoE/slide-003.jpg]]

- Source frame: `slide-003.jpg`
- Crop: `full` `[0, 0, 960, 540]` score `178.12`
![[assets/reconstructed-slides/qaJXBMwUkoE/slide-004.jpg]]

- Source frame: `slide-004.jpg`
- Crop: `full` `[0, 0, 960, 540]` score `167.04`
![[assets/reconstructed-slides/qaJXBMwUkoE/slide-005.jpg]]

- Source frame: `slide-005.jpg`
- Crop: `full` `[0, 0, 960, 540]` score `165.87`
![[assets/reconstructed-slides/qaJXBMwUkoE/slide-006.jpg]]

- Source frame: `slide-006.jpg`
- Crop: `full` `[0, 0, 960, 540]` score `162.23`
![[assets/reconstructed-slides/qaJXBMwUkoE/slide-007.jpg]]

- Source frame: `slide-007.jpg`
- Crop: `full` `[0, 0, 960, 540]` score `175.9`
![[assets/reconstructed-slides/qaJXBMwUkoE/slide-008.jpg]]

- Source frame: `slide-008.jpg`
- Crop: `full` `[0, 0, 960, 540]` score `166.18`
![[assets/reconstructed-slides/qaJXBMwUkoE/slide-009.jpg]]

- Source frame: `slide-009.jpg`
- Crop: `full` `[0, 0, 960, 540]` score `166.05`
![[assets/reconstructed-slides/qaJXBMwUkoE/slide-010.jpg]]

- Source frame: `slide-010.jpg`
- Crop: `full` `[0, 0, 960, 540]` score `166.19`
![[assets/reconstructed-slides/qaJXBMwUkoE/slide-011.jpg]]

- Source frame: `slide-011.jpg`
- Crop: `full` `[0, 0, 960, 540]` score `166.12`
![[assets/reconstructed-slides/qaJXBMwUkoE/slide-012.jpg]]

- Source frame: `slide-012.jpg`
- Crop: `full` `[0, 0, 960, 540]` score `166.19`
![[assets/reconstructed-slides/qaJXBMwUkoE/slide-013.jpg]]

- Source frame: `slide-013.jpg`
- Crop: `full` `[0, 0, 960, 540]` score `166.28`
![[assets/reconstructed-slides/qaJXBMwUkoE/slide-014.jpg]]

- Source frame: `slide-014.jpg`
- Crop: `full` `[0, 0, 960, 540]` score `164.98`
![[assets/reconstructed-slides/qaJXBMwUkoE/slide-015.jpg]]

- Source frame: `slide-015.jpg`
- Crop: `full` `[0, 0, 960, 540]` score `164.44`
![[assets/reconstructed-slides/qaJXBMwUkoE/slide-016.jpg]]

- Source frame: `slide-016.jpg`
- Crop: `full` `[0, 0, 960, 540]` score `166.49`
![[assets/reconstructed-slides/qaJXBMwUkoE/slide-017.jpg]]

- Source frame: `slide-018.jpg`
- Crop: `full` `[0, 0, 960, 540]` score `166.44`
## Dense Scene-Detected Slide Candidates
- [[youtube-qaJXBMwUkoE-dense-slides]]