Deep learning-based generative art is being researched. As usual, self-learning is better. Some models, like OpenAI's DALL-E 2, require registration and can only be used online, but others can be used locally, which is usually more enjoyable for curious users. I'll demonstrate the Stable Diffusion model's operation on a standard PC.

Let’s get started.

What It Does

Stable Diffusion uses numerous components:

• A generative model trained to produce images is called a diffusion model. The model is incrementally improving the starting data, which is only random noise. The model has an image, and while it is being trained, the reversed process is being used to add noise to the image. Being able to reverse this procedure and create images from noise is where the true magic is (more details and samples can be found in the paper).

• An internal compressed representation of a latent diffusion model, which may be altered to produce the desired images, is used (more details can be found in the paper). The capacity to fine-tune the generation process is essential because producing pictures at random is not very attractive (as we can see, for instance, in Generative Adversarial Networks).

• A neural network model called CLIP (Contrastive Language-Image Pre-training) is used to translate natural language prompts into vector representations. This model, which was trained on 400,000,000 image-text pairs, enables the transformation of a text prompt into a latent space for the diffusion model in the scenario of stable diffusion (more details in that paper).

This figure shows all data flow:

The weights file size for Stable Diffusion model v1 is 4 GB and v2 is 5 GB, making the model quite huge. The v1 model was trained on 256x256 and 512x512 LAION-5B pictures on a 4,000 GPU cluster using over 150.000 NVIDIA A100 GPU hours. The open-source pre-trained model is helpful for us. And we will.

Install

Before utilizing the Python sources for Stable Diffusion v1 on GitHub, we must install Miniconda (assuming Git and Python are already installed):

wget https://repo.anaconda.com/miniconda/Miniconda3-py39_4.12.0-Linux-x86_64.sh
chmod +x Miniconda3-py39_4.12.0-Linux-x86_64.sh
./Miniconda3-py39_4.12.0-Linux-x86_64.sh
conda update -n base -c defaults conda

Install the source and prepare the environment:

git clone https://github.com/CompVis/stable-diffusion
cd stable-diffusion
conda env create -f environment.yaml
conda activate ldm
pip3 install transformers --upgrade

Download the pre-trained model weights next. HiggingFace has the newest checkpoint sd-v14.ckpt (a download is free but registration is required). Put the file in the project folder and have fun:

python3 scripts/txt2img.py --prompt "hello world" --plms --ckpt sd-v1-4.ckpt --skip_grid --n_samples 1

Almost. The installation is complete for happy users of current GPUs with 12 GB or more VRAM. RuntimeError: CUDA out of memory will occur otherwise. Two solutions exist.

Running the optimized version

Try optimizing first. After cloning the repository and enabling the environment (as previously), we can run the command:

python3 optimizedSD/optimized_txt2img.py --prompt "hello world" --ckpt sd-v1-4.ckpt --skip_grid --n_samples 1

Stable Diffusion worked on my visual card with 8 GB RAM (alas, I did not behave well enough to get NVIDIA A100 for Christmas, so 8 GB GPU is the maximum I have;).

Running Stable Diffusion without GPU

If the GPU does not have enough RAM or is not CUDA-compatible, running the code on a CPU will be 20x slower but better than nothing. This unauthorized CPU-only branch from GitHub is easiest to obtain. We may easily edit the source code to use the latest version. It's strange that a pull request for that was made six months ago and still hasn't been approved, as the changes are simple. Readers can finish in 5 minutes:

• Replace if attr.device!= torch.device(cuda) with if attr.device!= torch.device(cuda) and torch.cuda.is available at line 20 of ldm/models/diffusion/ddim.py ().

• Replace if attr.device!= torch.device(cuda) with if attr.device!= torch.device(cuda) and torch.cuda.is available in line 20 of ldm/models/diffusion/plms.py ().

• Replace device=cuda in lines 38, 55, 83, and 142 of ldm/modules/encoders/modules.py with device=cuda if torch.cuda.is available(), otherwise cpu.

• Replace model.cuda() in scripts/txt2img.py line 28 and scripts/img2img.py line 43 with if torch.cuda.is available(): model.cuda ().

Run the script again.

Testing

Test the model. Text-to-image is the first choice. Test the command line example again:

python3 scripts/txt2img.py --prompt "hello world" --plms --ckpt sd-v1-4.ckpt --skip_grid --n_samples 1

The slow generation takes 10 seconds on a GPU and 10 minutes on a CPU. Final image:

Hello world is dull and abstract. Try a brush-wielding hamster. Why? Because we can, and it's not as insane as Napoleon's cat. Another image:

Generating an image from a text prompt and another image is interesting. I made this picture in two minutes using the image editor (sorry, drawing wasn't my strong suit):

I can create an image from this drawing:

python3 scripts/img2img.py --prompt "A bird is sitting on a tree branch" --ckpt sd-v1-4.ckpt --init-img bird.png --strength 0.8

It was far better than my initial drawing:

I hope readers understand and experiment.

Stable Diffusion UI

Developers love the command line, but regular users may struggle. Stable Diffusion UI projects simplify image generation and installation. Simple usage:

• Unpack the ZIP after downloading it from https://github.com/cmdr2/stable-diffusion-ui/releases. Linux and Windows are compatible with Stable Diffusion UI (sorry for Mac users, but those machines are not well-suitable for heavy machine learning tasks anyway;).

• Start the script.

Done. The web browser UI makes configuring various Stable Diffusion features (upscaling, filtering, etc.) easy:

V2.1 of Stable Diffusion

I noticed the notification about releasing version 2.1 while writing this essay, and it was intriguing to test it. First, compare version 2 to version 1:

• alternative text encoding. The Contrastive LanguageImage Pre-training (CLIP) deep learning model, which was trained on a significant number of text-image pairs, is used in Stable Diffusion 1. The open-source CLIP implementation used in Stable Diffusion 2 is called OpenCLIP. It is difficult to determine whether there have been any technical advancements or if legal concerns were the main focus. However, because the training datasets for the two text encoders were different, the output results from V1 and V2 will differ for the identical text prompts.

• a new depth model that may be used to the output of image-to-image generation.

• a revolutionary upscaling technique that can quadruple the resolution of an image.

• Generally higher resolution Stable Diffusion 2 has the ability to produce both 512x512 and 768x768 pictures.

The Hugging Face website offers a free online demo of Stable Diffusion 2.1 for code testing. The process is the same as for version 1.4. Download a fresh version and activate the environment:

conda deactivate  
conda env remove -n ldm  # Use this if version 1 was previously installed
git clone https://github.com/Stability-AI/stablediffusion
cd stablediffusion
conda env create -f environment.yaml
conda activate ldm

Hugging Face offers a new weights ckpt file.

The Out of memory error prevented me from running this version on my 8 GB GPU. Version 2.1 fails on CPUs with the slow conv2d cpu not implemented for Half error (according to this GitHub issue, the CPU support for this algorithm and data type will not be added). The model can be modified from half to full precision (float16 instead of float32), however it doesn't make sense since v1 runs up to 10 minutes on the CPU and v2.1 should be much slower. The online demo results are visible. The same hamster painting with a brush prompt yielded this result:

It looks different from v1, but it functions and has a higher resolution.

The superresolution.py script can run the 4x Stable Diffusion upscaler locally (the x4-upscaler-ema.ckpt weights file should be in the same folder):

python3 scripts/gradio/superresolution.py configs/stable-diffusion/x4-upscaling.yaml x4-upscaler-ema.ckpt

This code allows the web browser UI to select the image to upscale:

The copy-paste strategy may explain why the upscaler needs a text prompt (and the Hugging Face code snippet does not have any text input as well). I got a GPU out of memory error again, although CUDA can be disabled like v1. However, processing an image for more than two hours is unlikely:

Stable Diffusion Limitations

When we use the model, it's fun to see what it can and can't do. Generative models produce abstract visuals but not photorealistic ones. This fundamentally limits The generative neural network was trained on text and image pairs, but humans have a lot of background knowledge about the world. The neural network model knows nothing. If someone asks me to draw a Chinese text, I can draw something that looks like Chinese but is actually gibberish because I never learnt it. Generative AI does too! Humans can learn new languages, but the Stable Diffusion AI model includes only language and image decoder brain components. For instance, the Stable Diffusion model will pull NO WAR banner-bearers like this:

V1:

V2.1:

The shot shows text, although the model never learned to read or write. The model's string tokenizer automatically converts letters to lowercase before generating the image, so typing NO WAR banner or no war banner is the same.

I can also ask the model to draw a gorgeous woman:

V1:

V2.1:

The first image is gorgeous but physically incorrect. A second one is better, although it has an Uncanny valley feel. BTW, v2 has a lifehack to add a negative prompt and define what we don't want on the image. Readers might try adding horrible anatomy to the gorgeous woman request.

If we ask for a cartoon attractive woman, the results are nice, but accuracy doesn't matter:

V1:

V2.1:

Another example: I ordered a model to sketch a mouse, which looks beautiful but has too many legs, ears, and fingers:

V1:

V2.1: improved but not perfect.

V1 produces a fun cartoon flying mouse if I want something more abstract:

I tried multiple times with V2.1 but only received this:

The image is OK, but the first version is closer to the request.

Stable Diffusion struggles to draw letters, fingers, etc. However, abstract images yield interesting outcomes. A rural landscape with a modern metropolis in the background turned out well:

V1:

V2.1:

Generative models help make paintings too (at least, abstract ones). I searched Google Image Search for modern art painting to see works by real artists, and this was the first image:

I typed "abstract oil painting of people dancing" and got this:

V1:

V2.1:

It's a different style, but I don't think the AI-generated graphics are worse than the human-drawn ones.

The AI model cannot think like humans. It thinks nothing. A stable diffusion model is a billion-parameter matrix trained on millions of text-image pairs. I input "robot is creating a picture with a pen" to create an image for this post. Humans understand requests immediately. I tried Stable Diffusion multiple times and got this:

This great artwork has a pen, robot, and sketch, however it was not asked. Maybe it was because the tokenizer deleted is and a words from a statement, but I tried other requests such robot painting picture with pen without success. It's harder to prompt a model than a person.

I hope Stable Diffusion's general effects are evident. Despite its limitations, it can produce beautiful photographs in some settings. Readers who want to use Stable Diffusion results should be warned. Source code examination demonstrates that Stable Diffusion images feature a concealed watermark (text StableDiffusionV1 and SDV2) encoded using the invisible-watermark Python package. It's not a secret, because the official Stable Diffusion repository's test watermark.py file contains a decoding snippet. The put watermark line in the txt2img.py source code can be removed if desired. I didn't discover this watermark on photographs made by the online Hugging Face demo. Maybe I did something incorrectly (but maybe they are just not using the txt2img script on their backend at all).

Conclusion

The Stable Diffusion model was fascinating. As I mentioned before, trying something yourself is always better than taking someone else's word, so I encourage readers to do the same (including this article as well;).

Is Generative AI a game-changer? My humble experience tells me:

• I think that place has a lot of potential. For designers and artists, generative AI can be a truly useful and innovative tool. Unfortunately, it can also pose a threat to some of them since if users can enter a text field to obtain a picture or a website logo in a matter of clicks, why would they pay more to a different party? Is it possible right now? unquestionably not yet. Images still have a very poor quality and are erroneous in minute details. And after viewing the image of the stunning woman above, models and fashion photographers may also unwind because it is highly unlikely that AI will replace them in the upcoming years.

• Today, generative AI is still in its infancy. Even 768x768 images are considered to be of a high resolution when using neural networks, which are computationally highly expensive. There isn't an AI model that can generate high-resolution photographs natively without upscaling or other methods, at least not as of the time this article was written, but it will happen eventually.

• It is still a challenge to accurately represent knowledge in neural networks (information like how many legs a cat has or the year Napoleon was born). Consequently, AI models struggle to create photorealistic photos, at least where little details are important (on the other side, when I searched Google for modern art paintings, the results are often even worse;).

• When compared to the carefully chosen images from official web pages or YouTube reviews, the average output quality of a Stable Diffusion generation process is actually less attractive because to its high degree of randomness. When using the same technique on their own, consumers will theoretically only view those images as 1% of the results.

Anyway, it's exciting to witness this area's advancement, especially because the project is open source. Google's Imagen and DALL-E 2 can also produce remarkable findings. It will be interesting to see how they progress.

AI images are like funhouse mirrors.

Google's AI Blog introduced the puppy-slug in the summer of 2015.

Puppy-slug isn't a single image or character. "Puppy-slug" refers to Google's DeepDream's unsettling psychedelia. This tool uses convolutional neural networks to train models to recognize dataset entities. If researchers feed the model millions of dog pictures, the network will learn to recognize a dog.

DeepDream used neural networks to analyze and classify image data as well as generate its own images. DeepDream's early examples were created by training a convolutional network on dog images and asking it to add "dog-ness" to other images. The models analyzed images to find dog-like pixels and modified surrounding pixels to highlight them.

Puppy-slugs and other DeepDream images are ugly. Even when they don't trigger my trypophobia, they give me vertigo when my mind tries to reconcile familiar features and forms in unnatural, physically impossible arrangements. I feel like I've been poisoned by a forbidden mushroom or a noxious toad. I'm a Lovecraft character going mad from extradimensional exposure. They're gross!

Is this really how AIs see the world? This is possibly an even more unsettling topic that DeepDream raises than the blatant abjection of the images.

When these photographs originally circulated online, many friends were startled and scandalized. People imagined a computer's imagination would be literal, accurate, and boring. We didn't expect vivid hallucinations and organic-looking formations.

DeepDream's images didn't really show the machines' imaginations, at least not in the way that scared some people. DeepDream displays data visualizations. DeepDream reveals the "black box" of convolutional network training.

Some of these images look scary because the models don't "know" anything, at least not in the way we do.

These images are the result of advanced algorithms and calculators that compare pixel values. They can spot and reproduce trends from training data, but can't interpret it. If so, they'd know dogs have two eyes and one face per head. If machines can think creatively, they're keeping it quiet.

You could be forgiven for thinking otherwise, given OpenAI's Dall-impressive E's results. From a technological perspective, it's incredible.

Arthur C. Clarke once said, "Any sufficiently advanced technology is indistinguishable from magic." Dall-magic E's requires a lot of math, computer science, processing power, and research. OpenAI did a great job, and we should applaud them.

Dall-E and similar tools match words and phrases to image data to train generative models. Matching text to images requires sorting and defining the images. Untold millions of low-wage data entry workers, content creators optimizing images for SEO, and anyone who has used a Captcha to access a website make these decisions. These people could live and die without receiving credit for their work, even though the project wouldn't exist without them.

This technique produces images that are less like paintings and more like mirrors that reflect our own beliefs and ideals back at us, albeit via a very complex prism. Due to the limitations and biases that these models portray, we must exercise caution when viewing these images.

The issue was succinctly articulated by artist Mimi Onuoha in her piece "On Algorithmic Violence":

As we continue to see the rise of algorithms being used for civic, social, and cultural decision-making, it becomes that much more important that we name the reality that we are seeing. Not because it is exceptional, but because it is ubiquitous. Not because it creates new inequities, but because it has the power to cloak and amplify existing ones. Not because it is on the horizon, but because it is already here.

The brand-new USB Rubber Ducky is much riskier than before.

With its own programming language, the well-liked hacking tool may now pwn you.

With a vengeance, the USB Rubber Ducky is back.

This year's Def Con hacking conference saw the release of a new version of the well-liked hacking tool, and its author, Darren Kitchen, was on hand to explain it. We put a few of the new features to the test and discovered that the most recent version is riskier than ever.

WHAT IS IT?

The USB Rubber Ducky seems to the untrained eye to be an ordinary USB flash drive. However, when you connect it to a computer, the computer recognizes it as a USB keyboard and will accept keystroke commands from the device exactly like a person would type them in.

Kitchen explained to me, "It takes use of the trust model built in, where computers have been taught to trust a human, in that anything it types is trusted to the same degree as the user is trusted. And a computer is aware that clicks and keystrokes are how people generally connect with it.

Over ten years ago, the first Rubber Ducky was published, quickly becoming a hacker favorite (it was even featured in a Mr. Robot scene). Since then, there have been a number of small upgrades, but the most recent Rubber Ducky takes a giant step ahead with a number of new features that significantly increase its flexibility and capability.

WHERE IS ITS USE?

The options are nearly unlimited with the proper strategy.

The Rubber Ducky has already been used to launch attacks including making a phony Windows pop-up window to collect a user's login information or tricking Chrome into sending all saved passwords to an attacker's web server. However, these attacks lacked the adaptability to operate across platforms and had to be specifically designed for particular operating systems and software versions.

The nuances of DuckyScript 3.0 are described in a new manual. 

The most recent Rubber Ducky seeks to get around these restrictions. The DuckyScript programming language, which is used to construct the commands that the Rubber Ducky will enter into a target machine, receives a significant improvement with it. DuckyScript 3.0 is a feature-rich language that allows users to write functions, store variables, and apply logic flow controls, in contrast to earlier versions that were primarily limited to scripting keystroke sequences (i.e., if this... then that).

This implies that, for instance, the new Ducky can check to see if it is hooked into a Windows or Mac computer and then conditionally run code specific to each one, or it can disable itself if it has been attached to the incorrect target. In order to provide a more human effect, it can also generate pseudorandom numbers and utilize them to add a configurable delay between keystrokes.

The ability to steal data from a target computer by encoding it in binary code and transferring it through the signals intended to instruct a keyboard when the CapsLock or NumLock LEDs should light up is perhaps its most astounding feature. By using this technique, a hacker may plug it in for a brief period of time, excuse themselves by saying, "Sorry, I think that USB drive is faulty," and then take it away with all the credentials stored on it.

HOW SERIOUS IS THE RISK?

In other words, it may be a significant one, but because physical device access is required, the majority of people aren't at risk of being a target.

The 500 or so new Rubber Duckies that Hak5 brought to Def Con, according to Kitchen, were his company's most popular item at the convention, and they were all gone on the first day. It's safe to suppose that hundreds of hackers already possess one, and demand is likely to persist for some time.

Additionally, it has an online development toolkit that can be used to create attack payloads, compile them, and then load them onto the target device. A "payload hub" part of the website makes it simple for hackers to share what they've generated, and the Hak5 Discord is also busy with conversation and helpful advice. This makes it simple for users of the product to connect with a larger community.

It's too expensive for most individuals to distribute in volume, so unless your favorite cafe is renowned for being a hangout among vulnerable targets, it's doubtful that someone will leave a few of them there. To that end, if you intend to plug in a USB device that you discovered outside in a public area, pause to consider your decision.

WOULD IT WORK FOR ME?

Although the device is quite straightforward to use, there are a few things that could cause you trouble if you have no prior expertise writing or debugging code. For a while, during testing on a Mac, I was unable to get the Ducky to press the F4 key to activate the launchpad, but after forcing it to identify itself using an alternative Apple keyboard device ID, the problem was resolved.

From there, I was able to create a script that, when the Ducky was plugged in, would instantly run Chrome, open a new browser tab, and then immediately close it once more without requiring any action from the laptop user. Not bad for only a few hours of testing, and something that could be readily changed to perform duties other than reading technology news.

Failures inspire young developers

Jason citron created many products.

These products flopped.

Microsoft offered $10 billion for one of these products.

He rejected the offer since he was so confident in his success.

Let’s find out how he built a product that is currently valued at $15 billion.

Early in his youth, Jason began learning to code.

Jason's father taught him programming and IT.

His father wanted to help him earn money when he needed it.

Jason created video games and websites in high school.

Jason realized early on that his IT and programming skills could make him money.

Jason's parents misjudged his aptitude for programming.

Jason frequented online programming communities.

He looked for web developers. He created websites for those people.

His parents suspected Jason sold drugs online. When he said he used programming to make money, they were shocked.

They helped him set up a PayPal account.

Florida higher education to study video game creation

Jason never attended an expensive university.

He studied game design in Florida.

“Higher Education is an interesting part of society… When I work with people, the school they went to never comes up… only thing that matters is what can you do…At the end of the day, the beauty of silicon valley is that if you have a great idea and you can bring it to the life, you can convince a total stranger to give you money and join your project… This notion that you have to go to a great school didn’t end up being a thing for me.”

Jason's life was altered by Steve Jobs' keynote address.

After graduating, Jason joined an incubator.

Jason created a video-dating site first.

Bad idea.

Nobody wanted to use it when it was released, so they shut it down.

He made a multiplayer game.

It was released on Bebo. 10,000 people played it.

When Steve Jobs unveiled the Apple app store, he stopped playing.

The introduction of the app store resembled that of a new gaming console.

Jason's life altered after Steve Jobs' 2008 address.

“Whenever a new video game console is launched, that’s the opportunity for a new video game studio to get started, it’s because there aren’t too many games available…When a new PlayStation comes out, since it’s a new system, there’s only a handful of titles available… If you can be a launch title you can get a lot of distribution.”

Apple's app store provided a chance to start a video game company.

They released an app after 5 months of work.

Aurora Feint is the game.

Jason believed 1000 players in a week would be wonderful. A thousand players joined in the first hour.

Over time, Aurora Feints' game didn't gain traction. They don't make enough money to keep playing.

They could only make enough for one month.

Instead of buying video games, buy technology

Jason saw that they established a leaderboard, chat rooms, and multiplayer capabilities and believed other developers would want to use these.

They opted to sell the prior game's technology.

OpenFeint.

Assisting other game developers

They had no money in the bank to create everything needed to make the technology user-friendly.

Jason and Daniel designed a website saying:

“If you’re making a video game and want to have a drop in multiplayer support, you can use our system”

TechCrunch covered their website launch, and they gained a few hundred mailing list subscribers.

They raised seed funding with the mailing list.

Nearly all iPhone game developers started adopting the Open Feint logo.

“It was pretty wild… It was really like a whole social platform for people to play with their friends.”

What kind of a business model was it?

OpenFeint originally planned to make the software free for all games. As the game gained popularity, they demanded payment.

They later concluded it wasn't a good business concept.

It became free eventually.

Acquired for $104 million

Open Feint's users and employees grew tremendously.

GREE bought OpenFeint for $104 million in April 2011.

GREE initially committed to helping Jason and his team build a fantastic company.

Three or four months after the acquisition, Jason recognized they had a different vision.

He quit.

Jason's Original Vision for the iPad

Jason focused on distribution in 2012 to help businesses stand out.

The iPad market and user base were growing tremendously.

Jason said the iPad may replace mobile gadgets.

iPad gamers behaved differently than mobile gamers.

People sat longer and experienced more using an iPad.

“The idea I had was what if we built a gaming business that was more like traditional video games but played on tablets as opposed to some kind of mobile game that I’ve been doing before.”

Unexpected insight after researching the video game industry

Jason learned from studying the gaming industry that long-standing companies had advantages beyond a single release.

Previously, long-standing video game firms had their own distribution system. This distribution strategy could buffer time between successful titles.

Sony, Microsoft, and Valve all have gaming consoles and online stores.

So he built a distribution system.

He created a group chat app for gamers.

He envisioned a team-based multiplayer game with text and voice interaction.

His objective was to develop a communication network, release more games, and start a game distribution business.

Remaking the video game League of Legends

Jason and his crew reimagined a League of Legends game mode for 12-inch glass.

They adapted the game for tablets.

League of Legends was PC-only.

So they rebuilt it.

They overhauled the game and included native mobile experiences to stand out.

Hammer and Chisel was the company's name.

18 people worked on the game.

The game was funded. The game took 2.5 years to make.

Was the game a success?

July 2014 marked the game's release. The team's hopes were dashed.

Critics initially praised the game.

Initial installation was widespread.

The game failed.

As time passed, the team realized iPad gaming wouldn't increase much and mobile would win.

Jason was given a fresh idea by Stan Vishnevskiy.

Stan Vishnevskiy was a corporate engineer.

He told Jason about his plan to design a communication app without a game.

This concept seeded modern strife.

“The insight that he really had was to put a couple of dots together… we’re seeing our customers communicating around our own game with all these different apps and also ourselves when we’re playing on PC… We should solve that problem directly rather than needing to build a new game…we should start making it on PC.”

So began Discord.

Online socializing with pals was the newest trend.

Jason grew up playing video games with his friends.

He never played outside.

Jason had many great moments playing video games with his closest buddy, wife, and brother.

Discord was about providing a location for you and your group to speak and hang out.

Like a private cafe, bedroom, or living room.

Discord was developed for you and your friends on computers and phones.

You can quickly call your buddies during a game to conduct a conference call. Put the call on speaker and talk while playing.

Discord wanted to give every player a unique experience. Because coordinating across apps was a headache.

The entire team started concentrating on Discord.

Jason decided Hammer and Chisel would focus on their chat app.

Jason didn't want to make a video game.

How Discord attracted the appropriate attention

During the first five months, the entire team worked on the game and got feedback from friends.

This ensures product improvement. As a result, some teammates' buddies started utilizing Discord.

The team knew it would become something, but the result was buggy. App occasionally crashed.

Jason persuaded a gamer friend to write on Reddit about the software.

New people would find Discord. Why not?

Reddit users discovered Discord and 50 started using it frequently.

Discord was launched.

Rejecting the $10 billion acquisition proposal

Discord has increased in recent years.

It sends billions of messages.

Discord's users aren't tracked. They're privacy-focused.

Purchase offer

Covid boosted Discord's user base.

Weekly, billions of messages were transmitted.

Microsoft offered $10 billion for Discord in 2021.

Jason sold Open Feint for $104m in 2011.

This time, he believed in the product so much that he rejected Microsoft's offer.

“I was talking to some people in the team about which way we could go… The good thing was that most of the team wanted to continue building.”

Last time, Discord was valued at $15 billion.

Discord raised money on March 12, 2022.

The $15 billion corporation raised $500 million in 2021.

Who is AMTD Digital?

An unknown Hong Kong corporation joined the global megacaps worth over $500 billion on Tuesday.

The American Depository Share (ADS) with the ticker code HKD gapped at the open, soaring 25% over the previous closing price as trading began, before hitting an intraday high of $2,555.

At its peak, its market cap was almost $450 billion, more than Facebook parent Meta or Alibaba.

Yahoo Finance reported a daily volume of 350,500 shares, the lowest since the ADS began trading and much below the average of 1.2 million.

Despite losing a fifth of its value on Wednesday, it's still worth more than Toyota, Nike, McDonald's, or Walt Disney.

The company sold 16 million shares at $7.80 each in mid-July, giving it a $1 billion market valuation.

Why the boom?

That market cap seems unjustified.

According to SEC reports, its income-generating assets barely topped $400 million in March. Fortune's emails and calls went unanswered.

Website discloses little about company model. Its one-minute business presentation film uses a Star Wars–like design to sell the company as a "one-stop digital solutions platform in Asia"

The SEC prospectus explains.

AMTD Digital sells a "SpiderNet Ecosystems Solutions" kind of club membership that connects enterprises. This is the bulk of its $25 million annual revenue in April 2021.

Pretax profits have been higher than top line over the past three years due to fair value accounting gains on Appier, DayDayCook, WeDoctor, and five Asian fintechs.

AMTD Group, the company's parent, specializes in investment banking, hotel services, luxury education, and media and entertainment. AMTD IDEA, a $14 billion subsidiary, is also traded on the NYSE.

“Significant volatility”

Why AMTD Digital listed in the U.S. is unknown, as it informed investors in its share offering prospectus that could delist under SEC guidelines.

Beijing's red tape prevents the Sarbanes-Oxley Board from inspecting its Chinese auditor.

This frustrates Chinese stock investors. If the U.S. and China can't achieve a deal, 261 Chinese companies worth $1.3 trillion might be delisted.

Calvin Choi left UBS to become AMTD Group's CEO.

His capitalist background and status as a Young Global Leader with the World Economic Forum don't stop him from praising China's Communist party or celebrating the "glory and dream of the Great Rejuvenation of the Chinese nation" a century after its creation.

Despite having an executive vice chairman with a record of battling corruption and ties to Carrie Lam, Beijing's previous proconsul in Hong Kong, Choi is apparently being targeted for a two-year industry ban by the city's securities regulator after an investor accused Choi of malfeasance.

Some CMIG-funded initiatives produced money, but he didn't give us the proceeds, a corporate official told China's Caixin in October 2020. We don't know if he misappropriated or lost some money.

A seismic anomaly

In fundamental analysis, where companies are valued based on future cash flows, AMTD Digital's mind-boggling market cap is a statistical aberration that should occur once every hundred years.

AMTD Digital doesn't know why it's so valuable. In a thank-you letter to new shareholders, it said it was confused by the stock's performance.

Since its IPO, the company has seen significant ADS price volatility and active trading volume, it said Tuesday. "To our knowledge, there have been no important circumstances, events, or other matters since the IPO date."

Permabears awoke after the jump. Jim Chanos asked if "we're all going to ignore the $400 billion meme stock in the room," while Nate Anderson called AMTD Group "sketchy."

It happened the same day SEC Chair Gary Gensler praised the 20th anniversary of the Sarbanes-Oxley Act, aimed to restore trust in America's financial markets after the Enron and WorldCom accounting fraud scandals.

The run-up revived unpleasant memories of Robinhood's decision to limit retail investors' ability to buy GameStop, regarded as a measure to protect hedge funds invested in the meme company.

Why wasn't HKD's buy button removed? Because retail wasn't behind it?" tweeted Gensler on Tuesday. "Real stock fraud. "You're worthless."

In simple terms, you probably don’t.

But try. Grandma didn't grow up with Facebook, but she eventually joined.

Perhaps the fear of being isolated outweighed the discomfort of learning the technology.

Grandmas are Facebook likers, sharers, and commenters.

There’s no stopping her.

Not even NFTs. Web3 is currently very complex.

It's difficult to explain what NFTs are, how they work, and why we might use them.

Three explanations.

1. Everything will be ours to own, both physically and digitally.

Why own something you can't touch? What's the point?

Blockchain technology proves digital ownership.

Untouchables need ownership proof. What?

Digital assets reduce friction, save time, and are better for the environment than physical goods.

Many valuable things are intangible. Feeling like your favorite brands. You'll pay obscene prices for clothing that costs pennies.

Secondly, NFTs Are Contracts. Agreements Have Value.

Blockchain technology will replace all contracts and intermediaries.

Every insurance contract, deed, marriage certificate, work contract, plane ticket, concert ticket, or sports event is likely an NFT.

We all have public wallets, like Grandma's Facebook page.

3. Your NFT Purchases Will Be Visible To Everyone.

Everyone can see your public wallet. What you buy says more about you than what you post online.

NFTs issued double as marketing collateral when seen on social media.

While I doubt Grandma knows who Snoop Dog is, imagine him or another famous person holding your NFT in his public wallet and the attention that could bring to you, your company, or brand.

This Technical Section Is For You

The NFT is a contract; its founders can add value through access, events, tuition, and possibly royalties.

Imagine Elon Musk releasing an NFT to his network. Or yearly business consultations for three years.

Christ-alive.

It's worth millions.

These determine their value.

No unsuspecting schmuck willing to buy your hot potato at zero. That's the trend, though.

Overpriced NFTs for low-effort projects created a bubble that has burst.

During a market bubble, you can make money by buying overvalued assets and selling them later for a profit, according to the Greater Fool Theory.

People are struggling. Some are ruined by collateralized loans and the gold rush.

Finances are ruined.

It's uncomfortable.

The same happened in 2018, during the ICO crash or in 1999/2000 when the dot com bubble burst. But the underlying technology hasn’t gone away.