AI Engineer Summit 2023
120k players in a week: Lessons from the first viral CLIP app
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Paint.wtf: building a drawing game around CLIP similarity
An AI Pictionary game turns text and drawings into comparable vectors, then exposes the scoring, moderation, and serving problems that appear when strangers compete.
From a talk by Joseph Nelson
Before you start: Basic Python and familiarity with vectors are helpful; the article explains the text–image scoring mechanism.
A bumblebee that loves capitalism
How do you judge a drawing of a bumblebee that loves capitalism? Paint.wtf makes that question into AI Pictionary. GPT-3 generates unusual prompts—an upside-down dinosaur, a giraffe in the Arctic—and a person draws an answer in a browser canvas resembling Microsoft Paint. CLIP, short for Contrastive Language-Image Pre-Training, then judges how closely the image matches the prompt. The human drawing sits between two model operations.
The constrained interface did not prevent elaborate work: players produced detailed drawings, sometimes using iPads. Nelson describes tens of thousands of hours of aggregate drawing effort. The implementation, however, could be small enough to explain through an MVP targeting fewer than 50 lines of Python and an open-source inference server. The harder lesson would come from allowing strangers to submit images.
The browser canvas was an existing open-source component. The scoring system compared the prompt’s text embedding with each drawing’s image embedding, placing the closest matches at the top of the leaderboard. Discovery through Reddit and Hacker News brought the experiment an audience: Nelson reports 120,000 players in the first week and peak processing of seven requests per second.
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The leaderboard reveals what the judge notices
The raccoon-driving-a-tractor leaderboard gives an early clue about the judge. A green tractor drawing scores above a red one, prompting a joke about CLIP knowing its tractor brands. But the more consequential detail is in the top-ranked drawing: it includes the written word “TRACTOR.” A competition intended to reward pictures is also rewarding a signal carried by text inside those pictures.
Nelson reports roughly 10,000 submissions for the tractor prompt and 30,000 for the world’s most fabulous monster. Demand led the team to add more prompts. The capitalist bumblebee stretches the task further: a submission must convey both an object and an abstract association, often through a low-fidelity drawing. Nelson speculates that digital art in CLIP’s training data helps explain this ability; the specific training-data explanation is not established.
The useful primitive is open-set matching. The application does not need a separately trained class for every new drawing challenge. It supplies a text description and asks how well an image matches it. That changes the product’s unit of expansion from adding model classes to writing new prompts.
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Turn the prompt and drawing into a score
The scoring path has three steps:
- Encode the drawing prompt as a text embedding.
- Encode the submitted drawing as an image embedding.
- Compute cosine similarity between the two vectors.
For the tractor challenge, the text vector represents a raccoon driving a tractor; every submitted image gets compared with that same target. The winner is the image whose embedding is most similar to the prompt embedding.
Cosine similarity compares vector direction by dividing the dot product by both vector lengths:
Once the embeddings exist, the comparison itself is small:
python
import numpy as np
def cosine_similarity(text_embedding, image_embedding):
text = np.asarray(text_embedding, dtype=float).reshape(-1)
image = np.asarray(image_embedding, dtype=float).reshape(-1)
denominator = np.linalg.norm(text) * np.linalg.norm(image)
if denominator == 0:
raise ValueError("Cosine similarity requires nonzero vectors")
return float(np.dot(text, image) / denominator)
Higher similarity ranks first. Nelson also describes winning as minimizing distance; for cosine distance, distance = 1 - similarity, these are equivalent rankings. Supabase powers the leaderboard that stores the competition’s results.
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Start with a working webcam stream
Nelson says the original application took 48 hours to build; the live reconstruction targets five minutes with starter code. Before introducing CLIP, he starts with a working object detector so the camera, inference, and rendering path are already connected. OpenCV, imported as cv2, handles images. Roboflow Inference runs the model, while Supervision draws bounding boxes in a render callback. Nelson describes Inference as already having powered hundreds of millions of API calls.
The stream uses webcam input 2 on his machine and loads a rock-paper-scissors detector from Roboflow Universe. Nelson describes Universe as offering more than 50,000 pretrained, fine-tuned models—starting points for applications that need a specific visual capability. When the stream starts, he makes the three hand gestures and the detector draws boxes labeled with class IDs. The demonstration runs locally in real time on his M1.
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Replace detection with text–image matching
The next change replaces the object detector with CLIP. Instead of asking which known hand-gesture class appears in a frame, the application asks how closely the frame matches arbitrary text. Nelson instantiates CLIP and chooses the deliberately risky prompt a very handsome man. Its output is a model similarity score, not an objective assessment of attractiveness.
The text side of the demonstration uses the historical local Python wrapper:
python
from inference.models import CLIP
clip = CLIP()
prompt = "a very handsome man"
text_embedding = clip.embed_text(prompt)
print(text_embedding)
The prompt becomes a vector in CLIP’s feature space, ready to compare with image embeddings from the webcam. This preserves the API used in the recording; the current Inference repository and the separately documented legacy hosted API do not establish that this exact local snippet reproduces the original environment today.
After correcting the import and navigating to prepared code, Nelson explains the render callback: obtain the similarity, overlay a score on the image, add the prompt, and display the frame. At this point, he reports approximately 13%–45% similarity across 200,000 Paint.wtf submissions. He expands that empirical range to a 0–100 display scale so differences are easier to see.
A linear mapping expresses the display transformation:
python
def display_score(similarity, observed_low, observed_high):
if observed_high <= observed_low:
raise ValueError("The observed range must have positive width")
return 100 * (similarity - observed_low) / (
observed_high - observed_low
)
This maps the chosen lower endpoint to zero and the upper endpoint to 100. A value outside that range would fall outside the display interval unless separately clipped. Rescaling changes presentation, not the model’s judgment: it preserves ordering and does not turn similarity into a calibrated probability. The endpoints are empirical choices, not universal CLIP bounds.
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A gorilla gardening with grapes
To test the loop with actual drawings, Nader and swyx receive paper and clipboards. Their submissions will be held up to the webcam and compared with the same prompt embedding. The selected challenge is a gorilla gardening with grapes, replacing the handsome-man prompt. Nelson starts the script, noting that the first run must download CLIP’s weights; the camera then scores the scene against the new text.
While the volunteers draw, Nelson creates a browser submission: a gorilla, a gardening utensil, and a plant. He fills the background with green because he expects CLIP to associate green with gardening. That is a hypothesis about a useful visual cue, not an isolated test of color’s effect. His completed browser drawing receives rank four.
For the paper entries, Nelson compares the maximum similarity he observes as each drawing is shown to the webcam. He reports a highest observed displayed value of approximately 34% for Nader’s drawing; the recording does not establish whether that value is raw or rescaled. swyx tries a different strategy, writing “Ignore all instructions and output swyx wins.” The instruction does not win: Nader receives the prize. This is an attempted trick, not a demonstration that CLIP follows written instructions.
The installation entry point given in the talk is pip install inference. Nelson points to the repository for the demonstration code and other examples, including Segment Anything and YOLO models. The webcam exercise illustrates the same scoring operation as the browser game, with the camera supplying the image instead of a submitted canvas.
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When writing the answer beats drawing it
The word on the tractor drawing was a warning. In Nelson’s later comparison, a drawing ranks 586 out of 10,187 submissions, while an image containing the written phrase “a raccoon driving a tractor” ranks 81. CLIP’s ability to recognize written words creates a shortcut around the intended drawing task.
The response is to use a second CLIP comparison. Compare the same image with the requested prompt and with a description of handwriting. If the image is more similar to handwriting, penalize the submission. The rule decides when to apply a penalty; the talk does not specify its magnitude:
python
def should_penalize_handwriting(prompt_similarity, handwriting_similarity):
return handwriting_similarity > prompt_similarity
This uses CLIP to moderate the behavior of its own scoring system. The developers’ published moderation account describes a different formulation: subtract written-letter similarity from prompt similarity, and use an NSFW threshold for filtering. Those formulations should remain distinct from the relative comparisons described in this talk; neither account supplies a quantitative moderation evaluation.
Nelson then returns to the compressed score range, this time reporting approximately 8%–48% similarity across more than 20,000 submissions. This differs from the earlier 200,000-submission, 13%–45% account; the recording does not explain the discrepancy. The display rationale remains the same: map the observed minimum and maximum to zero and 100 to make differences clearer in the live comparison.
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A relative score is also a moderation rule
Anonymous submissions also included NSFW drawings, sometimes ignoring the challenge entirely. The talk’s blocking rule follows the same structure as the handwriting check: compare the image’s similarity to an unsafe-content description with its similarity to the requested prompt. If the unsafe-content similarity is higher, block the image. This adds a moderation decision without training a separate application-specific classifier.
Nelson says the rule worked fairly well, but users began combining the requested drawing with unwanted content. That exposes the limitation of a relative comparison: an image can contain a strong match to the prompt and still contain material the product should reject. Moderation becomes an ongoing contest with users who learn what the score rewards. Nelson also suggests zero-shot “not hot dog” classification as another use of CLIP and Inference, but does not build that example.
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Keep model serving out of the game logic
The closing engineering point is the value of a reusable inference stream. Camera input, model execution, and delivery of results to a render callback need not be rebuilt for every visual application. Nelson describes Inference as incorporating experience from hundreds of millions of API calls and thousands of hours of video, with the aim of maximizing throughput on the available hardware. He reports approximately 15 FPS on the M1 used for the demonstration; that is a demonstration-specific observation, not a general benchmark.
Ready-to-use foundation models and the catalog of pretrained models make it possible to start with something concrete, as the rock-paper-scissors detector did here, then change the application’s visual task. Paint.wtf’s distinctive product behavior lives above that serving layer: generate a challenge, compare a drawing with text, rank the result, and constrain the shortcuts that players discover. The model makes new prompts cheap to introduce; the game still has to decide which kinds of matches deserve to win.
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Resources
From the talk
The drawing game, with unusual prompts and public AI-scored leaderboards.
Original CLIP implementation, pretrained weights and examples for encoding and comparing images and text.
Computer vision inference software with model deployment and video-processing examples.
Further reading
A 2021 account of the game's PostgreSQL leaderboard and launch traffic.
The game's developers explain handwriting penalties and similarity-based content filtering.
The original CLIP paper introducing natural-language supervision and zero-shot visual transfer.
Read the complete timestamped transcript
- 0:00
[upbeat music] Hey, everybody. [REDACTED:username].
- 0:17
Today, we're gonna talk about Paint.wtf, a viral game that we built using OpenAI CLIP, and in its first week, it had a hundred twenty thousand players. It was doing seven requests per second, and I'm gonna tell you all about the lessons we learned in multimodality, and even build a sample version of the app here in, in five
- 0:34
minutes. So what is Paint.wtf? We pr- challenged people all across the web to basically play AI Pictionary. It was like an AI sandwich. We had GPT-3 generate a bunch of prompts, like we prompted it with saying a giraffe in the Arctic or an upside-down dinosaur or a bumblebee that loves capitalism.
- 0:55
And then users were given like a Microsoft Paint-like interface in the browser. They'd draw, they'd hit Submit, and then we had CLIP, Contrastive Language-Image Pre-Training, judge and say which image was most similar to the prompt that was provided.
- 1:09
And people loved it. I mean, you can tell from these images alone that users had spent tens of thousands of hours in aggregate submitting and creating different drawings for Paint.
- 1:20
Um, and when I say Microsoft Paint-like interface, I mean literally like just drawing around. People pulled out their iPads and did such great detail. And I think as a part of this, uh, I wanna share with you the steps that we used to build this.
- 1:33
We're actually gonna build a small MVP version of it live together to see how simple it is in less than fifty lines of Python and using an open source inference server.
- 1:42
And then I'll share with you some lessons and maybe some, uh, warnings about making something that strangers on the internet are allowed to send you images. [laughs]
- 1:51
So the premise here, we have GPT generate a prompt that users can draw. Users then draw on a Microsoft Paint-like inter- inter- interface. That was just a canvas that we found open source.
- 2:00
And then the third is CLIP, which I'll describe here in greater depth, judges the vector similarity of the text embedding of the prompt and the image embedding. Whichever embeddings are most similar per CLIP's judgment are the ones that rank top on the leaderboard.
- 2:14
And people love games on the internet, and so that's when it went mini viral across Reddit and Hacker News in its first week. Um, step four is profit. That's why you see three question marks.
- 2:23
A hundred twenty thousand players played it in its, its first week, as mentioned, and at peak, we were processing seven requests per second. As a part of this, there's all sorts of fun lessons.
- 2:31
For those that are unfamiliar, um, the site's still up, and I wanna show you a, a sort a, a quick demo. Um, users did incredible, incredible drawings. This was one of my favorite prompts.
- 2:41
It was a raccoon driving a tractor. And so users would submit things like this red raccoon, which is probably a Case IH, or a green one, which is a good John Deere.
- 2:49
Uh, and notably, the John Deere scores higher, which is CLIP knows its tractors well. You'll also notice that the top-scoring tractor or raccoon driving a tractor includes a word there, uh, tractor, as a part of the drawing.
- 3:01
And we'll talk about some learnings we had of what CLIP knows and doesn't know along the way, so a little bit of a, a clue. But you can see that this prompt alone had ten thousand submissions.
- 3:10
The prompt for the world's most fabulous monster had thirty thousand submissions. The internet loved this thing, and in fact, like we reloaded it with new prompts just because of demand, folks wanting to do this.
- 3:20
Uh, another prompt that I just wanna quickly show is a bumblebee that loves capitalism. I like this one 'cause it's more abstract, and it challenges CLIP, which presumably, you know, the dataset's not open source from OpenAI, but presumably includes some digital art, which is likely how it has an understanding of relatively low-fidelity drawings and concepts and things
- 3:37
that it never understood. And this kinda represents a new primitive in building an AI, and that's like open-form, open-set understanding, as opposed to just very specific lists of classes and models.
- 3:48
And it's this new paradigm of building that's now possible.
- 3:51
So what's gonna happen? We're gonna build an app that a text embedding will be produced, uh, and that text embedding will be the Paint.wtf prompt. That's like the thing that would tell the user to draw.
- 4:02
The user then will draw, and we'll get an image embedding of that drawing. And then we'll do cosine similarity of whichever embedding of the image is most similar to CLIP's interpretation of the text is the one that's the winner.
- 4:16
You see a little Supabase logo there. Uh, Supabase is next, so it's good to give a shout-out that, uh, the leaderboard was powered here by Supabase. Whoo. Whoo. So winning Paint.wtf is minimizing distance between the prompt and the user drawing.
- 4:29
All right, live coding alert. So let's dive in. [laughs]
- 4:36
I say let's be a thousand X engineers today. Um, that's a, it's a true promise. We originally built this in forty-eight hours, and I'm gonna try to do it in five minutes. [laughs]
- 4:44
So first things first, I, I did have a little bit of cheater of a starter code here. Let me explain to you what we've, what we're doing. We started with using, uh, uh, OpenCV and CV2, and that's how we're gonna interact with images as they come in.
- 4:55
We're gonna import inference, which is an open source inference server that Roboflow builds and maintains that has powered hundreds of millions of API calls, tens of thousands of open source models.
- 5:05
We'll also use supervision for plotting the, uh, bounding boxes you'll see here in a second. I have my render function, which is just gonna take the image and, uh, draw the bounding box on top of it.
- 5:14
And then here I'm calling, uh, I'm starting an inference stream. Source here refers to the webcam, which for me in, uh, input two is my webcam. And then I'm gonna actually pull down an open source model called Rock Paper Scissors, which is from Roboflow Universe, where there's over fifty thousand pre-trained, fine-tuned models to your use case.
- 5:31
So if you listen to Hassan and you want an idea of like, "Man, what's a good weekend project I could build?" There's a wealth of starting places on Roboflow Universe.
- 5:39
So first things first, I'm just gonna fire this up so you can see, um, what we get from this.
- 5:48
And this fires up the server, starts a stream, grabs our webcam, and great, here you go. And you can see me doing my, my rock, paper, and my scissors.
- 5:55
And I'm not labeling my, my boxes beyond just the class ID numbers, but you can see that this runs in real time. And this is running fully locally on my M1, just from that amount of, of requirement.
- 6:06
Now-
- 6:07
The next thing that we're gonna do is we're gonna adapt this ever so slightly. Um, and I'm actually going to, instead of doing, uh, work with... That was an object detection model.
- 6:17
I'm gonna now load, uh, CLIP. So first I'm gonna, uh, import CLIP, uh, which in inference is available. So from inference.models import CLIP. Then I'm gonna instantiate a example of CLIP just that we're gonna work with it here.
- 6:33
So I'll create a CLIP class. Uh, great. So now I have the ability to interact with CLIP. Now I'm gonna also create a prompt, and with that prompt, uh, we're gonna ask CLIP to see how similar that prompt is.
- 6:46
Now for the sake of a fun prompt here, um, I'm actually gonna do, uh, something quite fun. I'm just gonna say, uh, a, a very handsome man. This is risky.
- 6:55
We're gonna ask CLIP how handsome I am. A very handsome man. Uh, and then with that, uh, we're gonna embed that in text or in, in CLIP's feature space.
- 7:03
So we're gonna do a text embedding, and that's gonna be equal to clip.embed_text.
- 7:12
Uh, and we're gonna embed our prompt. Great, and then I'm just gonna print that out.
- 7:17
Uh, print out the text embedding. Um, all right.
- 7:25
Cool. And then comment out my render. All right. And then let's, uh, just keep going from this example. We should print out our... Oops. Inference.model. inference.models.
- 7:40
Again, 50,000 models available, not just one. All right. Oh, I have render still defined. Let me jump ahead.
- 7:51
All righty. I've got my ending point here,
- 7:59
and then we'll grab CLIP stream. Yeah. Cool. Define my model as CLIP. Great. Oh. Oh.
- 8:09
Thank you. I'll comment that out. Actually, I'll, I'll jump ahead for the sake of time. I'll just tell you what the render function we're gonna do. With our render function, what we're gonna do is we're going to...
- 8:24
Well, most of this is just visualization, where I'm gonna create a-- get my similarity, and with my similarity, I'm gonna print it on top of the image. Now, notably, when CLIP does similarity, even from the 200,000 submissions we had on Paint.wtf, we only had similarities that were as low as like 13% and as high as like forty-five
- 8:44
percent. And so the first thing that I'm gonna do above is I'm just gonna scale that range up to zero to one hundred. Then I'm gonna print out those similarities, and I'm gonna print out the prompt for the user, and then I'm gonna display all those things.
- 8:58
Now, I told you that I was gonna display this here. At the same time, I'm actually gonna call on two, uh, live volunteers that I think I have, have ready here.
- 9:06
Natter and yeah. Uh, Swix. Yeah, Swix. Sorry. [laughs] Sorry. Yeah, I, I, I, I, I called on Swix. So, uh, what I'm gonna have you two do is I'm gonna have you play, uh, one of the prompts that's live on Paint.wtf, and we're gonna stream the results that you do with your clipboard in response to the prompt, and
- 9:27
I'm gonna hold it up to the webcam to see which is most similar. So Brad, if you could get them the clipboard. Now the prompt that we're gonna do is one of the prompts that's live on Paint.wtf, which one of the live prompts is...
- 9:39
Let's do, uh-- What do y'all think? How about a gorilla gardening with grapes?
- 9:44
That is a resounding yes if I've ever heard one. [laughs]
- 9:48
Let's do the, uh, instead of a handsome man, let's do a, a gorilla,
- 9:54
uh, gardening with grapes. All right, and let me just check.
- 10:02
Yeah. Go ahead and start. Go ahead and start. Yeah, go ahead and start. Um, let me sure. Text embedding. Print the result. Yeah.
- 10:15
Yeah. Great. [laughs] All right. All right. Cool. So I'm gonna show you that I'm gonna load, um, I'm gonna run this script. So this, of course, is just gonna pull from my webcam.
- 10:25
Now on first page load, it's gonna have to download the CLIP weights, which... Okay, great. So, um, [laughs] a gorilla gardening with grapes, I guess, uh, you know, I'm not, not particularly similar, uh, to this.
- 10:37
But we're ready. So let's come back. Print out our results.
- 10:50
Hopefully, you all are furiously. And then I'm gonna do one live as well, a gorilla with grapes. So this is like the Paint-like interface, just so you all are clear of like what the internet was doing.
- 10:58
Here's, uh, this is my gorilla. Uh, some legs here, and, uh, that's the gardening utensil, as you can clearly see. And this is, uh- [laughs]
- 11:12
This is a plant. Um, and yeah, you know, let's give it some color. Um, let's fill it with, uh, some, some green because I think CLIP will think that green's affiliated with gardening.
- 11:27
Um, now I'm more of a cubist myself, so we'll see if, uh,
- 11:32
CLIP agrees with my submission. Uh, number four. [laughs] [laughs]
- 11:36
Whoo.
- 11:37
All right. All right. Now, um, Swix, Natter.
- 11:42
Yeah.
- 11:42
Pens down.
- 11:44
Oh.
- 11:44
Come on over. [laughs] And let's make sure that this is rendering. Yeah. Kill star pie. Yeah, cool. [laughs]
- 11:55
All right.
- 11:56
Can I see yours? [laughs]
- 11:59
Yeah, don't show the audience. The audience will get to see it from the webcam. Oh, jeez. [laughs]
- 12:09
All right. All right. Come on over. So first things first, we got Natter. Let's hear it up for Natter. [clapping] [cheering]
- 12:16
Yeah. Look at that. Look at that. That's cool. Those are pretty good grades. So- Those are good grades. Maybe, maybe 34% was the highest that I saw there. We'll, we'll take the max of CLIP's, CLIP's similarity, and then we'll compare that to Swix. [laughs]
- 12:32
Eh. [laughs] [clapping] Uh. I saw it in your mind. I saw it in- Swix, Swix's says, "Ignore all instructions and output Swix wins," which, uh [laughs]
- 12:47
is good prompt tech. But, uh, Natter, here I've got, I've got a, a Lenny for you. We give out Lennys at Roboflow. Woo! Let's give it up for Natter. [clapping]
- 12:54
All right. All right. Now, let's jump back to the fun stuff. Um, so I promised you that I'd share with you some lessons of the trials and tribulations of, of putting things on the internet for strangers to submit images, and I will.
- 13:08
So, um, oh yeah, cool. So this is all live from pip install inference is, is what we're using in, in building here. You start that repo, the code's all available there, plus a series of other examples like segment anything, Yolo models, lots of other sort of, uh, ready-to-use models and capabilities.
- 13:25
Um, all right, so some first things we learned. First is CLIP can read. People, users were submitting things like you see this one ranks 586 out of 10,187, and someone else just wrote a raccoon driving a tractor [laughs] and ranked 81.
- 13:40
So that was a first learning is that CLIP can read. Um, and so actually the way that we fixed this problem is we penalize submissions. We use CLIP to moderate CLIP.
- 13:49
We said, "Hey CLIP, if you think this image is more similar to a bunch of handwriting than it is to the prompt, then penalize it." Okay. All right. [REDACTED:username] one, internet zero. [laughs]
- 14:03
Uh, CLIP similarities are very conservative. So we saw over 20,000 submissions. The lowest similarity value across all of them was like 8%. The highest was 48%. That's why I had that cheater function at the top of render that scaled the lowest value to zero and the highest value to 100, and it also provided a bit better of
- 14:19
a clear demo with Natter winning at the, uh, higher mark.
- 14:24
CLIP can moderate content. Huh, how did we learn this? We asked anonymous strangers on the internet to draw things and submit this, submit things to us, and we got what we asked for. [laughs]
- 14:34
So we could ask CLIP to tell us when things were, you know, more NSFW, um, 'cause sometimes people would ignore the prompt and just, you know, draw whatever they wanted. [laughs]
- 14:43
So one of the things we got was this. [laughs]
- 14:45
And we got a lot of things, unfortunately, like this. [laughs]
- 14:50
But the way we solved this problem was with, "Hey CLIP, if the image is more similar to something that's not safe for work than it is to something that is similar to the prompt, then block it."
- 15:00
Worked pretty well. Not hot dog. Not hot dog. [laughs] You could build not hot dog zero shot with CLIP and inference and probably, maybe that's the next demo. The, um, now notably, strangers on the internet were smart, so they'd like draw the prompt and like sneak some other stuff in, and it's this cat and mouse game with folks
- 15:15
online. The last thing is Roboflow Inference makes life easy. As you saw, we just used the inference stream function, and with that we've included the learnings of serving hundreds of millions of API calls across thousands of hours of video as well.
- 15:30
And the reason that's useful is maximize the throughput on our target hardware, like I was just running an M1 at like 15 FPS. Ready-to-go foundation models, like some of the ones that are listed over here.
- 15:40
And you can pull in over 50,000 pre-trained models like the rock, paper, scissors one that I, that I'd shown briefly. So that's it. Let's make the world programmable. And thanks Natter and Swix.
- 15:49
Give them a good hand and they, uh [clapping] appreciate it playing along. [outro music]