---
title: "Slides: 120k players in a week: Lessons from the first viral CLIP app: Joseph Nelson"
category: "slides"
video_id: "OimPoLxioYg"
sourceLabels: ["Public YouTube video frames", "Public YouTube metadata"]
---

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

## Source Video
[120k players in a week: Lessons from the first viral CLIP app: Joseph Nelson](https://www.youtube.com/watch?v=OimPoLxioYg)

## 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/OimPoLxioYg/slide-001.jpg]]

OCR text:

> an 86a
> 
> a Oey ie
> 
> A Ss ee
> 
> aa a
> Mi

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

OCR text:

> AIE
> AUTOGPT
> smol ai

![[assets/slides/OimPoLxioYg/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

![[assets/slides/OimPoLxioYg/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
> : cs ™ 7
> \ fo} = ©
> 4 | Ly nneron?
> revnonn - QR revscumn GQ"

![[assets/slides/OimPoLxioYg/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
> Cadet Resharg. 216 ot of 14758 woemvom Globel Renting $51 ot of 14.7S8 wornnwons Ghebet Rentong 288 ont of 14.758 womeveons
> eo wy aR
> &
> , ¢ ww, }
> wy
> oe: ‘mm
> Tevacan - QD Tavacan - QD Tevacan - GD
> 6

![[assets/slides/OimPoLxioYg/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

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

OCR text:

> AIE
> AUTOgPt
> smoo

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

OCR text:

> SILAR.
> C
> statpy
> apy>no
> iapertc2
> ortisterence
> iaport spevisiesassv
> anotator-sv.Bcsknoteter()
> etrender(resvtt,
> Labge)t
> piet(resolt)
> ieshovl
> "ootpot".
> anotater-annetate(
> sere-iavpe.
> detectionsniv.De
> (v2.vaitKeyi1)
> AT
> iaference.Streanc
> source·2,
> modet-"rock-
> wesain_thresd-Trut,
> ovtput_chanoeorder
> osredicties.render
> LARColS

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

OCR text:

> > "
> ‘ ALENGN
> 7 MANN VY

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

OCR text:

> SEAR..
> stMLOY
> 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
> (inftalk)（base)joseghnesongJosephs-Map-3ai-engineerx
> setashe det ton instalone cfournewformaterednsion？Thiswilaso RatterferPyhon of
> Ln3,C2254UTF-UF

![[assets/slides/OimPoLxioYg/slide-011.jpg]]

OCR text:

> 000
> SEAR..
> starLpy
> 17 28 21 22 24 27 29 19 28 clip-Clp Lsport shference text_enbedding-clig.enbed_text（gro fron mdel-clip. vie_oin_thread.Trve, ieference.nodels inpert Clip source.2, Wtpit_chaneLoreer. onsredictiosarer printiresit) resderiresslt, c2.inhei c2.vaitkey(1) outpit', tacor.ao sceseninage, detectien etstel Tracebsck(most recent call lest): NameError!nane'render'isnotdefines (inftalk) (base)josephnelaongJosephs-MaP-3ai-engineer xpython atart.py File*/Uaera/josephnelson/dev/ai-engineer/start.py*,1ine 20,incnodule> -2.64115313e-01 -1.749349090-01 -1.877274346-01 -9.52770412e-02 -2.82019377e-02-1.54569581e-01 -1.05047315e-015.85544035e-02 -8.57200846e-02 -2.95410380e-02 -4.164822030-02 -1.87377974e-01 -1.40733317e-01 1.78932637e-01 6.72230273e-02 2.77497377e-02-9.39981341e-02 5.148771410-02 8.811201160-02 1.29763842e-01 1.79062380e-01 9.65760350e-02 1.21351220e-01 4.89275005e-02 on_prediction=render -1.31694108e-01 -8.48577321e-02 -9.236798240-02 -3.718575240-01 -1.46396488e-01 -5.08920840e-03 -1.746433946-01 20-+00060956'9- -4.11540449e-01 2.97449529e-02 1.09019071e-01 2.00923234e-01 3.56133282e-01 3.51662338e-01 2.70213808e 1.62222300e+00 3.54486555e-02 2.93375552e -02 -01 -5.72258607e-02 -6.013145300-02 -6.674434040-02 -3.318021000-01 -8.358273664-03 -6.964379284-02 -3.15130161e-01 -1.10945351e-01 -3.083823990-02 -1.167899820-01 -6.434806484-02 4.869020840-01-7.72674799e-01 2.009732134-017.64982233e-0411 1.981199680-01 1.83216166e+00 2.766016720-01 3.028125000-01 2.65872121e-01 2.245157064-01 7.063483734-02 2.56329387e-01 -1.13056585e-01 -2.69213855e-01 -1.81438118e-01 -4.18238580e-01 -1.99865073e-01 -6.17044978e-02 -1.91159070e-01 -1.706174460-01 -2.08871037e-01 -1.06113903e-01 4.20555323e-01 1.455015240-01 1.63303614e-01 2.29426712e-01 1.302200410-01 2.081671060-01 4.379215840-02 1.421204810-01 7.311619820-03
> of
> L29,C2

![[assets/slides/OimPoLxioYg/slide-012.jpg]]

OCR text:

> AIE winilerity · omin leirleelaiaulerit
> Co?.ntt gtt.n,yi 12,24,154.255.1.FONTWE
> O.tlsf prpr (2.1a0sm1. H 25,2,2350.110
> FINIE

![[assets/slides/OimPoLxioYg/slide-013.jpg]]

OCR text:

> ove. pu
> arrare le
> 32
> 
> cw2. im
> 
> cv2.Wa
> 
> inference,
> 
> source
> 
> model
> 
> ; use_me
> a CUR aot

![[assets/slides/OimPoLxioYg/slide-014.jpg]]

OCR text:

> oes ry fa) : a
> D paint.wtf @
> Convince an Ai thet you're the beet artiet.
> Best of All Time
> toverer’ tenons
> " wre men ly inp. evan i ethan ont tare o> Oped OF
> Sent .
> 9p} Ssimoln
> == AUTO GPL,

![[assets/slides/OimPoLxioYg/slide-015.jpg]]

OCR text:

> . |. : : as > er soe —_
> ci o oar] cr ee 2) .
> | Oraw a gorilla gardening with grapes suséIT
> oorttrtwti‘a‘SOOOOOOS
> 70
> O°.
> ; or
> +
> a
> s
> *.
> as
> QQ! y
> eT

![[assets/slides/OimPoLxioYg/slide-016.jpg]]

OCR text:

> AI Engineer
> SUMMIT

![[assets/slides/OimPoLxioYg/slide-017.jpg]]
![[assets/slides/OimPoLxioYg/slide-018.jpg]]

OCR text:

> i a
> he 47,
> » T/O oa
> iN
> AlEngineer | | |e
> ST ~
> cf wm ON
> MiG
> [xine] “ eee Fr dan “3 a lh grcpes Lo
> Aree,

![[assets/slides/OimPoLxioYg/slide-019.jpg]]

OCR text:

> LIVE CODING
> Let's be 1000x Engineers Today

![[assets/slides/OimPoLxioYg/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

![[assets/slides/OimPoLxioYg/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.
> vt J ter scam - GD ravacan - ED
> ed
> te

![[assets/slides/OimPoLxioYg/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

## 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.