Software can change any company.

Software is becoming essential. Everyone should consider how software affects their lives and others'.

Software on your phone, tablet, or computer offers many new options. We're experts in enough ways now.

Software Business Ideas will be popular by 2023.

ERP Programs

ERP software meets rising demand.

ERP solutions automate and monitor tasks that large organizations, businesses, and even schools would struggle to do manually.

ERP software could reach $49 billion by 2024.

CRM Program

CRM software is a must-have for any customer-focused business.

Having an open mind about your business services and products allows you to change platforms.

Another company may only want your CRM service.

Medical software

Healthcare facilities need reliable, easy-to-use software.

EHRs, MDDBs, E-Prescribing, and more are software options.

The global medical software market could reach $11 billion by 2025, and mobile medical apps may follow.

Presentation Software in the Cloud

SaaS presentation tools are great.

They're easy to use, comprehensive, and full of traditional Software features.

In today's cloud-based world, these solutions make life easier for people. We don't know about you, but we like it.

Software for Project Management

People began working remotely without signs or warnings before the 2020 COVID-19 pandemic.

Many organizations found it difficult to track projects and set deadlines.

With PMP software tools, teams can manage remote units and collaborate effectively.

App for Blockchain-Based Invoicing

This advanced billing and invoicing solution is for businesses and freelancers.

These blockchain-based apps can calculate taxes, manage debts, and manage transactions.

Intelligent contracts help blockchain track transactions more efficiently. It speeds up and improves invoice generation.

Software for Business Communications

Internal business messaging is tricky.

Top business software tools for communication can share files, collaborate on documents, host video conferences, and more.

Payroll Automation System

Software development also includes developing an automated payroll system.

These software systems reduce manual tasks for timely employee payments.

These tools help enterprise clients calculate total wages quickly, simplify tax calculations, improve record-keeping, and support better financial planning.

System for Detecting Data Leaks

Both businesses and individuals value data highly. Yahoo's data breach is dangerous because of this.

This area of software development can help people protect their data.

You can design an advanced data loss prevention system.

AI-based Retail System

AI-powered shopping systems are popular. The systems analyze customers' search and purchase patterns and store history and are equipped with a keyword database.

These systems offer many customers pre-loaded products.

AI-based shopping algorithms also help users make purchases.

Software for Detecting Plagiarism

Software can help ensure your projects are original and not plagiarized.

These tools detect plagiarized content that Google, media, and educational institutions don't like.

Software for Converting Audio to Text

Machine Learning converts speech to text automatically.

These programs can quickly transcribe cloud-based files.

Software for daily horoscopes

Daily and monthly horoscopes will continue to be popular.

Software platforms that can predict forecasts, calculate birth charts, and other astrology resources are good business ideas.

E-learning Programs

Traditional study methods are losing popularity as virtual schools proliferate and physical space shrinks.

Khan Academy online courses are the best way to keep learning.

Online education portals can boost your learning. If you want to start a tech startup, consider creating an e-learning program.

Conclusion

Software is booming. There's never been a better time to start a software development business, with so many people using computers and smartphones. This article lists eight business ideas for 2023. Consider these ideas if you're just starting out or looking to expand.

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.

During the dot-com bust, I started my internet career. People used the internet intermittently to check email, plan travel, and do research. The average internet user spent 30 minutes online a day, compared to 7 today. To use the internet, you had to "log on" (most people still used dial-up), unlike today's always-on, high-speed mobile internet. In 2001, Amazon's market cap was $2.2B, 1/500th of what it is today. A study asked Americans if they'd adopt broadband, and most said no. They didn't see a need to speed up email, the most popular internet use. The National Academy of Sciences ranked the internet 13th among the 100 greatest inventions, below radio and phones. The internet was a cool invention, but it had limited uses and wasn't a good place to build a business. 

A small but growing movement of developers and founders believed the internet could be more than a read-only medium, allowing anyone to create and publish. This is web 2. The runner up name was read-write web. (These terms were used in prominent publications and conferences.) 

Web 2 concepts included letting users publish whatever they want ("user generated content" was a buzzword), social graphs, APIs and mashups (what we call composability today), and tagging over hierarchical navigation. Technical innovations occurred. A seemingly simple but important one was dynamically updating web pages without reloading. This is now how people expect web apps to work. Mobile devices that could access the web were niche (I was an avid Sidekick user). 

The contrast between what smart founders and engineers discussed over dinner and on weekends and what the mainstream tech world took seriously during the week was striking. Enterprise security appliances, essentially preloaded servers with security software, were a popular trend. Many of the same people would talk about "serious" products at work, then talk about consumer internet products and web 2. It was tech's biggest news. Web 2 products were seen as toys, not real businesses. They were hobbies, not work-related. 

There's a strong correlation between rich product design spaces and what smart people find interesting, which took me some time to learn and led to blog posts like "The next big thing will start out looking like a toy" Web 2's novel product design possibilities sparked dinner and weekend conversations. Imagine combining these features. What if you used this pattern elsewhere? What new product ideas are next? This excited people. "Serious stuff" like security appliances seemed more limited. 

The small and passionate web 2 community also stood out. I attended the first New York Tech meetup in 2004. Everyone fit in Meetup's small conference room. Late at night, people demoed their software and chatted. I have old friends. Sometimes I get asked how I first met old friends like Fred Wilson and Alexis Ohanian. These topics didn't interest many people, especially on the east coast. We were friends. Real community. Alex Rampell, who now works with me at a16z, is someone I met in 2003 when a friend said, "Hey, I met someone else interested in consumer internet." Rare. People were focused and enthusiastic. Revolution seemed imminent. We knew a secret nobody else did. 

My web 2 startup was called SiteAdvisor. When my co-founders and I started developing the idea in 2003, web security was out of control. Phishing and spyware were common on Internet Explorer PCs. SiteAdvisor was designed to warn users about security threats like phishing and spyware, and then, using web 2 concepts like user-generated reviews, add more subjective judgments (similar to what TrustPilot seems to do today). This staged approach was common at the time; I called it "Come for the tool, stay for the network." We built APIs, encouraged mashups, and did SEO marketing. 

Yahoo's 2005 acquisitions of Flickr and Delicious boosted web 2 in 2005. By today's standards, the amounts were small, around $30M each, but it was a signal. Web 2 was assumed to be a fun hobby, a way to build cool stuff, but not a business. Yahoo was a savvy company that said it would make web 2 a priority. 

As I recall, that's when web 2 started becoming mainstream tech. Early web 2 founders transitioned successfully. Other entrepreneurs built on the early enthusiasts' work. Competition shifted from ideation to execution. You had to decide if you wanted to be an idealistic indie bar band or a pragmatic stadium band. 

Web 2 was booming in 2007 Facebook passed 10M users, Twitter grew and got VC funding, and Google bought YouTube. The 2008 financial crisis tested entrepreneurs' resolve. Smart people predicted another great depression as tech funding dried up. 

Many people struggled during the recession. 2008-2011 was a golden age for startups. By 2009, talented founders were flooding Apple's iPhone app store. Mobile apps were booming. Uber, Venmo, Snap, and Instagram were all founded between 2009 and 2011. Social media (which had replaced web 2), cloud computing (which enabled apps to scale server side), and smartphones converged. Even if social, cloud, and mobile improve linearly, the combination could improve exponentially. 

This chart shows how I view product and financial cycles. Product and financial cycles evolve separately. The Nasdaq index is a proxy for the financial sentiment. Financial sentiment wildly fluctuates. 

Next row shows iconic startup or product years. Bottom-row product cycles dictate timing. Product cycles are more predictable than financial cycles because they follow internal logic. In the incubation phase, enthusiasts build products for other enthusiasts on nights and weekends. When the right mix of technology, talent, and community knowledge arrives, products go mainstream. (I show the biggest tech cycles in the chart, but smaller ones happen, like web 2 in the 2000s and fintech and SaaS in the 2010s.) 

Tech has changed since the 2000s. Few tech giants dominate the internet, exerting economic and cultural influence. In the 2000s, web 2 was ignored or dismissed as trivial. Entrenched interests respond aggressively to new movements that could threaten them. Creative patterns from the 2000s continue today, driven by enthusiasts who see possibilities where others don't. Know where to look. Crypto and web 3 are where I'd start. 

Today's negative financial sentiment reminds me of 2008. If we face a prolonged downturn, we can learn from 2008 by preserving capital and focusing on the long term. Keep an eye on the product cycle. Smart people are interested in things with product potential. This becomes true. Toys become necessities. Hobbies become mainstream. Optimists build the future, not cynics.

Full article is available here

Quitting your job for a side gig isn't smart.

A few years ago, I would have laughed at the idea of starting a side business.

I never thought a side gig could earn more than my 9-to-5. My side gig pays more than my main job now.

You may then tell me to leave your job.  But I don't want to gamble, and my side gig is important. Programming and web development help me write better because of my job.

Yes, I share work-related knowledge. Web development, web3, programming, money, investment, and side hustles are key.

Let me now show you how to make one.

Create a side business based on your profession or your interests.

I'd be direct.

Most people don't know where to start or which side business to pursue.

You can make money by taking online surveys, starting a YouTube channel, or playing web3 games, according to several blogs.

You won't make enough money and will waste time.

Nitin directs our efforts. My friend, you've worked and have talent. Profit from your talent.

Example:

College taught me web development. I soon created websites, freelanced, and made money. First year was hardest for me financially and personally.

As I worked, I became more skilled. Soon after, I got more work, wrote about web development on Medium, and started selling products.

I've built multiple income streams from web development. It wasn't easy. Web development skills got me a 9-to-5 job.

Focus on a specific skill and earn money in many ways. Most people start with something they hate or are bad at; the rest is predictable.

Result? They give up, frustrated.

Quietly focus for a year.

I started my side business in college and never told anyone. My parents didn't know what I did for fun.

The only motivation is time constraints. So I focused.

As I've said, I focused on my strengths (learned skills) and made money. Yes, I was among Medium's top 500 authors in a year and got a bonus.

How did I succeed? Since I know success takes time, I never imagined making enough money in a month. I spent a year concentrating.

I became wealthy. Now that I have multiple income sources, some businesses pay me based on my skill.

I recommend learning skills and working quietly for a year. You can do anything with this.

The hardest part will always be the beginning.

When someone says you can make more money working four hours a week. Leave that, it's bad advice.

If someone recommends a paid course to help you succeed, think twice.

The beginning is always the hardest.

I made many mistakes learning web development. When I started my technical content side gig, it was tough. I made mistakes and changed how I create content, which helped.

And it’s applicable everywhere.

Don't worry if you face problems at first. Time and effort heal all wounds.

Quitting your job to work a side job is not a good idea.

Some honest opinions.

Most online gurus encourage side businesses. It takes time to start and grow a side business.

Suppose you quit and started a side business.

After six months, what happens? Your side business won't provide enough money to survive.

Indeed. Later, you'll become demotivated and tense and look for work.

Instead, work 9-5, and start a side business. You decide. Stop watching Netflix and focus on your side business.

I know you're busy, but do it.

Next? It'll succeed or fail in six months. You can continue your side gig for another six months because you have a job and have tried it.

You'll probably make money, but you may need to change your side gig.

That’s it.

You've created a new revenue stream.

Remember.

Starting a side business, a company, or finding work is difficult. There's no free money in a competitive world. You'll only succeed with skill.

Read it again.

Focusing silently for a year can help you succeed.

I studied web development and wrote about it. First year was tough. I went viral, hit the top 500, and other firms asked me to write for them. So, my life changed.

Yours can too. One year of silence is required.

Enjoy!

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.

I analyze the top 200 DeFi crypto projects in this article.

This isn't a study. The findings benefit crypto investors.

Let’s go!

A set of data

I analyzed data from defillama.com. In my analysis, I used the top 200 DeFis by TVL in October 2022.

Total Locked Value

The chart below shows platform-specific locked value.

14 platforms had $1B+ TVL. 65 platforms have $100M-$1B TVL. The remaining 121 platforms had TVLs below $100 million, with the lowest being $23 million.

TVLs are distributed Pareto. Top 40% of DeFis account for 80% of TVLs.

Compliant Blockchains

Ethereum's blockchain leads DeFi. 96 of the examined projects offer services on Ethereum. Behind BSC, Polygon, and Avalanche.

Five platforms used 10+ blockchains. 36 between 2-10 159 used 1 blockchain.

Use Cases for DeFi

The chart below shows platform use cases. Each platform has decentralized exchanges, liquid staking, yield farming, and lending.

These use cases are DefiLlama's main platform features.

Which use case costs the most? Chart explains. Collateralized debt, liquid staking, dexes, and lending have high TVLs.

The DeFi Industry

I compared three high-TVL platforms (Maker DAO, Balancer, AAVE). The columns show monthly TVL and token price changes. The graph shows monthly Bitcoin price changes.

Each platform's market moves similarly.

Probably because most DeFi deposits are cryptocurrencies. Since individual currencies are highly correlated with Bitcoin, it's not surprising that they move in unison.

Takeaways

This analysis shows that the most common DeFi services (decentralized exchanges, liquid staking, yield farming, and lending) also have the highest average locked value.

Some projects run on one or two blockchains, while others use 15 or 20. Our analysis shows that a project's blockchain count has no correlation with its success.

It's hard to tell if certain use cases are rising. Bitcoin's price heavily affects the entire DeFi market.

TVL seems to be a good indicator of a DeFi platform's success and quality. Higher TVL platforms are cheaper. They're a better long-term investment because they gain or lose less value than DeFis with lower TVLs.