Slides: GPU Cloud Deployment Without Leaving Your IDE — Audry Hsu, RunPod

Source Video

GPU Cloud Deployment Without Leaving Your IDE — Audry Hsu, RunPod

Relationship To World's Fair 2026

These slides are extracted from a public AI Engineer YouTube video connected to World's Fair 2026. Speaker-matched clips are supporting context unless later confirmed as exact session recordings; official livestream recordings are day-level/event-level source material.

Related Scheduled Sessions

No individual scheduled session mapping has been assigned yet; treat this as an event livestream deck.

Extracted Slides

Recreated text/layout view: open HTML recreation
AI slide classifier: title_card confidence 0.94
Text source: agent_vision.

Slide text:

Introducing Runpod

The foundational platform for building, running, and scaling custom AI systems.

Recreated text/layout view: open HTML recreation
AI slide classifier: content_slide confidence 0.97
Text source: agent_vision.

Slide text:

Why Runpod Exists

Infrastructure eats developer time

GPU access is slow and opaque

Builder primary focus should be building

Recreated text/layout view: open HTML recreation
AI slide classifier: content_slide confidence 0.92
Text source: agent_vision.
OCR decision: ready — dense UI and code screenshot is better handled by OCR than manual transcription

Slide text:

Overview

Recreated text/layout view: open HTML recreation
AI slide classifier: content_slide confidence 0.91
Text source: advanced OCR rapidocr-live/left-72/contrast.
OCR decision: ready — code editor screenshot with small text is better handled by OCR

Slide text:

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Recreated text/layout view: open HTML recreation
AI slide classifier: content_slide confidence 0.97
Text source: advanced OCR rapidocr-live/border-trim/contrast.
OCR decision: ready — Dense terminal/code screenshot plus embedded product imagery are OCR-suitable.

Slide text:

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Recreated text/layout view: open HTML recreation
AI slide classifier: content_slide confidence 0.96
Text source: advanced OCR rapidocr-live/bright-screen/contrast.
OCR decision: ready — Dense code/editor content and a rendered image preview are OCR-suitable.

Slide text:

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Recreated text/layout view: open HTML recreation
AI slide classifier: content_slide confidence 0.95
Text source: advanced OCR rapidocr-live/bright-screen/contrast.
OCR decision: ready — Multi-column UI screenshot with small text and labels is better handled by OCR.

Slide text:

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Hidden Non-Slide Evidence

`slide-001.jpg` — speaker_stage confidence 0.96; stage shot with presenter and audience; projected slide is secondary and not a clean slide capture
`slide-002.jpg` — speaker_stage confidence 0.96; stage shot with presenter movement blocking the view; not a clean slide frame
`slide-010.jpg` — title_card confidence 0.99; Standalone branding/title card, not a content slide.

Classification audit: raw/sources/slide-ai-classification/slides/zDGHt0LB-dA/audit.json

Slide-Derived Subjects To Review

Subject extraction uses video title, related session titles/descriptions, transcript context, and OCR text when available. OCR is best-effort and should be reviewed against the embedded slide images.