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Slides: 120k players in a week: Lessons from the first viral CLIP app: Joseph Nelson

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120k players in a week: Lessons from the first viral CLIP app: Joseph Nelson

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

Extracted Slides

slide-001.jpg

OCR text:

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slide-002.jpg

OCR text:

AIE

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smol ai

slide-003.jpg

OCR text:

The Premise: Convince an Al you’re the best artist

1) GPT: Generate a prompt for users to draw

eo User: Draws prompt in MS Paint interface

6 CLIP: Judge vector similarity of text

prompt and user image

@ ???: 120k players in one week, 7 requests

per second

slide-004.jpg

OCR text:

Prompt: A Raccoon Driving a Tractor

Draw a raccoon driving a tractor Draw araccoon driving atractor | Oraw araccoon driving a tractor

Global Ranking: 462 out of 10.187 wubewssions Gtobel Ranking 335 out of 10.187 tudmesont Gobel Ranking: 3 out of 10.187 submessons

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slide-005.jpg

OCR text:

Prompt: A Bumblebee that Loves Capitalism

Draw a bumblebee that loves capitaliam Draw a bumblebee that loves capitalism Draw a bumblebee that loves capitalism

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slide-006.jpg

OCR text:

CLIP: Trained on 400M image/text pairs, OpenAl, 2021

text_embedding: how CLIP Paint.wtf Scoring

maps the paint.wtf prompt into

its feature space CLIP’s Interpretation

x, of the Paint.wtf Prompt

Image_embedding: how CLIP cuPtissarpraention of

maps the user submission into saiaaeteiaiiaaiienaiiniias-

its feature space /e

Winning paint.wtf: minimizing Cosine Similarity S|

distance between the prompt i

and the user drawing

7

slide-007.jpg

OCR text:

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slide-008.jpg

OCR text:

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OCR text:

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slide-010.jpg

OCR text:

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12 "ovtpun", a50. sceserisaor, desectist ages/inference/core/interfaces/stream/strean.py,line 157,in._init.. ages/inference/core/interfaces/strean/strean.py,line 3e4,in run_thread File*/Users/josephnelson/dev/ai-engineer/inftalk/1ib/python3.1o/site-gack self.inference_requeat_thread() self.run_thread()

1.9

19 20 Z1 22 2x 24 27 aference.Streanl ovtput_channeLarder. source.2, mel-"rock uie_sain_threed-True, 292.4 -50wr11 nda3/1ib/python3.1e/threading.py'> ages/inference/core/interfaces/strean/strean.py,line 282,in inference_reg uest_thread CException ignoredSn!<module'threading'from Traceback(eost recent calllast)1 File/Users/sosephnelson/dev/ai-engineer/start.py", cb(predictions,self.frane_cv) cv2.eaitxeyt1) Jsers/josephnelson/ninico 1ine 16,inrender

23 msredieties 67,in_shutdon Keyboardtnterrvot: leck.acquire()

(inftaik)(base)josephnelsongJosephs-map-3ai-engineer%python start.py Traceback(most recent call last): File/Usera/josephnelson/dev/ai-engineer/start.py,line 3,in<module> fron inference.modelingort clip

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slide-011.jpg

OCR text:

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slide-012.jpg

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slide-014.jpg

OCR text:

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Convince an Ai thet you're the beet artiet.

Best of All Time

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== AUTO GPL,

slide-015.jpg

OCR text:

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slide-016.jpg

OCR text:

AI Engineer

SUMMIT

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slide-019.jpg

OCR text:

LIVE CODING

Let's be 1000x Engineers Today

slide-020.jpg

OCR text:

Lessons from Building Paint.wtf with CLIP

We're all learning here

CLIP Can Read

Draw a raccoon driving a tractor

Global Ranking: 586 out of 10,187 submissions

TRY AGAIN

choose another prompt

Draw a raccoon driving a tractor

Global Ranking: 81 out of 10,187 submissions

TRY AGAIN

choose another prompt

10

slide-021.jpg

OCR text:

Lessons from Building Paint.wtf with CLIP ; Se

CLIP Can Read

ee teawr teen tenene sheila lala

ae — Reo A facc Ooh

eae ARivivry Xs

= #

. TRACTOR.

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slide-022.jpg

OCR text:

Lessons from Building Paint.wtf with CLIP

Roboflow Inference Makes Life Easy

Serving done right. an aad

Built on the lessons of serving 100M of API ; interop - . -

calls and thousands of hours of video. ee

e Maximize throughput on your target ee

hardware (GPU, CPU, edge) ie ao

e Use ready-to-go foundation models ini

e Pullin over 50k pretrained models from a aed

Roboflow Universe community are eas

14

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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.