---
title: "Slides: From MCP to Scale: Pipelines That Build Themselves — Rafael Levi, Bright Data"
category: "slides"
video_id: "zTZ0qunQXnM"
sourceLabels: ["Public YouTube video frames", "Public YouTube metadata"]
---

# Slides: From MCP to Scale: Pipelines That Build Themselves — Rafael Levi, Bright Data

## Source Video
[From MCP to Scale: Pipelines That Build Themselves — Rafael Levi, Bright Data](https://www.youtube.com/watch?v=zTZ0qunQXnM)

## 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
![[assets/slides/zTZ0qunQXnM/slide-001.jpg]]

OCR text:

> PLATINUM SPONSORS
> Braintrust WorkOS OpenAI

![[assets/slides/zTZ0qunQXnM/slide-002.jpg]]

OCR text:

> a - at Sete eel |
> mn . = aren to Scale:
> an Je Some :
> | BUILD THEMSELVES
> a Pe ae :
> a
> | Pb tcnmet iene
> = ns La] Ce yr e : a ;
> i Engineering the future of Al
> | — i ; a

![[assets/slides/zTZ0qunQXnM/slide-003.jpg]]

OCR text:

> AIE
> Engineering the future of AI

![[assets/slides/zTZ0qunQXnM/slide-004.jpg]]

OCR text:

> Build Walmart scraper with keyword search
> AI Engineer
> EUROPE

![[assets/slides/zTZ0qunQXnM/slide-005.jpg]]

OCR text:

> AIE
> Engineering the future of AI

![[assets/slides/zTZ0qunQXnM/slide-006.jpg]]

OCR text:

> AIE
> Engineering the future of AI

![[assets/slides/zTZ0qunQXnM/slide-007.jpg]]

OCR text:

> B
> rtlogging
> etreqoests
> oetBeavtLfulsoop
> Coefigoratiea
> AIE
> 20.s.vn.gt（*ATA_，ocker)
> OTRT_-.ah.（o.path.abst（_f1l_）)
> logging.scConfig（1ev）-eggig.DFo,fort-s（ncte
> log-logging-getlegger（_ae_)
> ffetpae：tr，reriet3）t
> .Fetor....
> "fetchpage throupBrigt Detawoonlocker.
> foattept inrage[retries）:
> try:
> response.reqsts.postc
> "https1//ags.brightdata.com/regest",
> ders(*aethorLationrerer（aP_r）*),
> tlseout-50,
> response.raise_fer_status()
> escept reqoests.heqoestExceptlon ss )
> 1og.rng（fattpt（attmgt·1）falled for （or1)：[0）²)
> fattt（etrses-）
> （.lg（2ttt）
> raise lontlaetrrer(fFailed te fetch [or1)aftee (retries)atteapts°)
> AIEngineer
> EUROPE

![[assets/slides/zTZ0qunQXnM/slide-008.jpg]]

OCR text:

> bd * ae
> * * an seen
> ae ee
> od os *
> }
> 7 " feo. mec: Pad é , aoe
> Bei: | AlEngineer |
> ( EUROPE
> ial

![[assets/slides/zTZ0qunQXnM/slide-009.jpg]]

OCR text:

> Keep
> blicdat
> ublic
> AlEngir
> EUROP

![[assets/slides/zTZ0qunQXnM/slide-010.jpg]]

OCR text:

> AI Engineer
> EUROPE
> HTTPS://AI.ENGINEER

## 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.