Its not a fuc*ing fad.

It's not about boring monkeys or photos as nfts; that's just what's been pushed up and made a lot of money. The technology underlying those ridiculous nft photos will one day prove your house and automobile ownership and tell you where your banana came from. Are you ready for web3? Soar!

People don't realize that absolutely anything can and will be part of the blockchain and smart contracts, making them even better. I'll tell you a secret: it will and is happening.

Why?

Why is something blockchain-based a good idea? So let’s speak about cars!

So a new Tesla car is manufactured, and when you buy it, it is bound to an NFT on the blockchain that proves current ownership. The NFT in the smart contract can contain some data about the current owner of the car and some data about the car's status, such as the number of miles driven, the car's overall quality, and so on, as well as a reference to a digital document bound to the NFT that has more information.

Now, 40 years from now, if you want to buy a used automobile, you can scan the car's serial number to view its NFT and see all of its history, each owner, how long they owned it, if it had damages, and more. Since it's on the blockchain, it can't be tampered with.

When you're ready to buy it, the owner posts it for sale, you buy it, and it's sent to your wallet. 5 seconds to change owner, 100% safe and verifiable.

Incorporate insurance logic into the car contract. If you crashed, your car's smart contract would take money from your insurance contract and deposit it in an insurance company wallet.

It's limitless. Your funds may be used by investors to provide insurance as they profit from everyone's investments.

Or suppose all car owners in a country deposit a fixed amount of money into an insurance smart contract that promises if something happens, we'll take care of it. It could be as little as $100-$500 per year, and in a country with 10 million people, maybe 3 million would do that, which would be $500 000 000 in that smart contract and it would be used by the insurance company to invest in assets or take a cut, literally endless possibilities.

Instead of $300 per month, you may pay $300 per year to be covered if something goes wrong, and that may include multiple insurances.

What about your grocery store banana, though?

Yes that too.

You can scan a banana to learn its complete history. You'll be able to see where it was cultivated, every middleman in the supply chain, and hopefully the banana's quality, farm, and ingredients used.

If you want locally decent bananas, you can only buy them, offering you transparency and options. I believe it will be an online marketplace where farmers publish their farms and products for trust and transparency. You might also buy bananas from the farmer.

And? Food security to finish the article. If an order of bananas included a toxin, you could easily track down every banana from the same origin and supply chain and uncover the root cause. This is a tremendous thing that will save lives and have a big impact; did you realize that 1 in 6 Americans gets poisoned by food every year? This could lower the number.

To summarize:

Smart contracts can issue nfts as proof of ownership and include functionality.

The same way Amazon killed bookstores. You just can’t see it yet.

Shultz globalized coffee. Before Starbucks, coffee sucked.

All accounts say 1970s coffee was awful.

Starbucks had three stores selling ground Indonesian coffee in the 1980s.

What a show!

A year after joining the company at 29, Shultz traveled to Italy for R&D.

He noticed the coffee shops' sense of theater and community and realized Starbucks was in the wrong business.

Integrating coffee and destination created a sense of community in the store.

Brilliant!

He told Starbucks' founders about his experience.

They disapproved.

For two years.

Shultz left and opened an Italian coffee shop chain like any good entrepreneur.

Starbucks ran into financial trouble, so the founders offered to sell to Shultz.

Shultz bought Starbucks in 1987 for $3.8 million, including six stores and a payment plan.

Starbucks is worth $100.79Billion, per Google Finance.

26,500 times Shultz's initial investment

Starbucks is releasing its own NFT Platform under Shultz and his early Vision.

This year, Starbucks Odyssey launches. The new digital experience combines a Loyalty Rewards program with NFT.

The side chain Polygon-based platform doesn't require a Crypto Wallet. Customers can earn and buy digital assets to unlock incentives and experiences.

They've removed all friction, making it more immersive and convenient than a coffee shop.

Brilliant!

NFTs are the access coupon to their digital community, but they don't highlight the technology.

They prioritize consumer experience by adding non-technical users to Web3. Their collectables are called journey stamps, not NFTs.

No mention of bundled gas fees.

Brady Brewer, Starbucks' CMO, said;

“It happens to be built on blockchain and web3 technologies, but the customer — to be honest — may very well not even know that what they’re doing is interacting with blockchain technology. It’s just the enabler,”

Rewards members will log into a web app using their loyalty program credentials to access Starbucks Odyssey. They won't know about blockchain transactions.

Join the waitlist here

Starbucks has just dealt its rivals a devastating blow.

It generates more than ten times the revenue of its closest competitor Costa Coffee.

The coffee giant is booming.

Starbucks is ahead of its competitors. No wonder.

They have an innovative, adaptable leadership team.

Starbucks' DNA challenges the narrative, especially when others reject their ideas.

I’m off for a cappuccino.

After 10 years of working in scale-ups, I've embraced a few concepts for scaling Tech and Product teams.

First, cross-functionalize teams. Product Managers represent the business, Product Designers the consumer, and Engineers build.

I organize teams of 5-10 individuals, following AWS's two pizza teams guidelines, with a Product Trio guiding each.

If more individuals are needed to reach a goal, I group teams under a Product Trio.

With Engineering being the biggest group, Staff/Principal Engineers often support the Trio on cross-team technical decisions.

Product Managers, Engineering Managers, or Engineers in the team may manage projects (depending on the project or aim), but the trio is collectively responsible for the team's output and outcome.

Once the Product Trio model is created, roles, duties, team ceremonies, and cooperation models must be clarified.

Keep reporting lines by discipline. Line managers are accountable for each individual's advancement, thus it's crucial that they know the work in detail.

Cross-team collaboration becomes more important after 3 teams (15-30 people). Teams can easily diverge in how they write code, run ceremonies, and build products.

Establishing groups of people that are cross-team, but grouped by discipline and skills, sharing and agreeing on working practices becomes critical.

The “Spotify Guild” model has been where I’ve taken a lot of my inspiration from.

Last, establish a taxonomy for communication channels.

In Slack, I create one channel per team and one per guild (and one for me to have discussions with the team leads).

These are just some of the basic principles I follow to organize teams.

A book I particularly like about team types and how they interact with each other is https://teamtopologies.com/.

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.

It's free now.

Most content writers—including me—write about side hustles because they do well.

I wrote several articles on a profitable programming side hustle.

Later, I learnt about AI content-generating platforms like Jasper.ai and Copy.ai, which I thought were terrible because they required subscriptions and had bad content. Even I dislike automatic online posting.

Since ChatGPT is becoming more popular, I tried it. It's free now.

Note: I wasn’t compensated to write this piece by this tool. In other words, I don’t receive any payment from their founders. There is no affiliate link, and right now it is free to use. Also, ChatGPT can do a lot more than what I ever did.

Begin.

OpenAI account registration requires visiting the website (if you don’t have one). Just write anything into the chatbot and get a response.

First, I wrote about the top 10 2023 side businesses.

This came in seconds.

2023 is shaping up to be a year of growth and opportunity for entrepreneurs and side hustlers. With the rise of remote work and the growing gig economy, there are many income streams to consider launching in the coming year. Here are the top 10 income streams to launch in 2023:

1. Virtual Assistant — The demand for virtual assistants is on the rise as more businesses move online. As a virtual assistant, you can offer administrative, technical, or creative services to clients remotely.

2. Online Tutoring — If you have expertise in a particular subject, consider becoming an online tutor. There are many platforms that connect tutors with students looking for help with their studies.

3. Freelance Writing — If you have a knack for writing, consider becoming a freelance writer. You can offer your services to businesses, bloggers, and individuals looking for content for their websites or social media platforms.

4. Graphic Design — If you have a talent for design, consider starting a graphic design side hustle. You can create logos, banners, and other visual content for businesses and individuals.

5. Social Media Management — Many businesses are looking for help with managing their social media accounts. As a social media manager, you can help businesses create and execute social media strategies to reach their target audience.

6. Dropshipping — Dropshipping is a business model where you sell products without actually holding any inventory. You can partner with suppliers and ship the products directly to the customer on behalf of the business.

7. Pet Sitting — If you love animals, consider starting a pet-sitting side hustle. You can offer your services to pet owners who need someone to take care of their pets while they are away.

8. Home Cleaning — With busy schedules, many people are looking for help with cleaning their homes. As a home cleaning service provider, you can offer your services to help keep homes tidy and organized.

9. Event Planning — If you have a talent for organization and planning, consider starting an event planning side hustle. You can offer your services to help plan and execute events for businesses and individuals.

10. Online Teaching — With the rise of online education, there is a growing demand for online teachers. If you have expertise in a particular subject, consider becoming an online teacher and offer your services to students looking for personalized instruction.

In conclusion, 2023 is shaping up to be a great year for launching new income streams. Whether you are looking to start a virtual assistant business, become an online tutor, or offer home cleaning services, there are many opportunities to turn your skills and expertise into a profitable side hustle.

Better than most online side hustle posts, right?

Write about the top 10 programming side hustles to start in 2023.

I got this.

If you are a programmer looking to start a side hustle, 2023 is the perfect time to launch your own business. With the growing demand for software and app development, there are many opportunities for programmers to turn their skills into a profitable side hustle. Here are the top 10 programming side hustles to start in 2023:

1. App Development — With the rise of smartphones and tablets, there is a growing demand for app development. You can offer your services to businesses and individuals looking to create custom apps for their needs.

2. Website Development — Many businesses and individuals are looking for help with creating and maintaining their websites. As a website developer, you can offer your services to help design, build, and manage websites for your clients.

3. Software Development — If you have expertise in a particular programming language, consider starting a software development side hustle. You can offer your services to businesses and individuals looking to create custom software solutions for their needs.

4. Game Development — The gaming industry is thriving, and there is a growing demand for game development. If you have experience in game development, you can offer your services to businesses and individuals looking to create custom games.

5. Data Analysis — With the rise of big data, there is a growing demand for data analysts. As a data analyst, you can offer your services to businesses and individuals looking to make sense of their data and gain insights from it.

6. Machine Learning — Machine learning is a rapidly growing field, and there is a growing demand for machine learning experts. If you have experience in machine learning, you can offer your services to businesses and individuals looking to develop custom machine learning solutions.

7. Cloud Computing — With the rise of cloud computing, there is a growing demand for cloud experts. If you have experience in cloud computing, you can offer your services to businesses and individuals looking to migrate their applications and data to the cloud.

8. Blockchain Development — Blockchain technology is gaining popularity, and there is a growing demand for blockchain developers. If you have experience in blockchain development, you can offer your services to businesses and individuals looking to develop custom blockchain solutions.

9. Artificial Intelligence — Artificial intelligence is a rapidly growing field, and there is a growing demand for AI experts. If you have experience in artificial intelligence, you can offer your services to businesses and individuals looking to develop custom AI solutions.

10. Virtual Reality — Virtual reality is an exciting and emerging technology, and there is a growing demand for VR developers. If you have experience in VR development, you can offer your services to businesses and individuals looking to create custom VR experiences.

In conclusion, 2023 is a great time to start a programming side hustle. Whether you are interested in app development, website development, or machine learning, there are many opportunities for programmers to turn their skills into a profitable business.

These two blogs show that AI can simplify blog authoring.

I asked some tough programming questions, and most were suitable.

The content may occasionally not be what we want, but it will almost always be very helpful to you.

Enjoy.