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.

Starting a passive income stream with data science and programming

My website is fresh. But how do I monetize it?

Creating a passive-income website is difficult. Advertise first. But what useful are ads without traffic?

Let’s Generate Traffic And Put Our Programming Skills To Use

SEO boosts traffic (Search Engine Optimisation). Traffic generation is complex. Keywords matter more than text, URL, photos, etc.

My Python skills helped here. I wanted to find relevant, Google-trending keywords (tags) for my topic.

First The Code

I wrote the script below here.

import re
from string import punctuation

import nltk
from nltk import TreebankWordTokenizer, sent_tokenize
from nltk.corpus import stopwords


class KeywordsGenerator:
    def __init__(self, pytrends):
        self._pytrends = pytrends

    def generate_tags(self, file_path, top_words=30):
        file_text = self._get_file_contents(file_path)
        clean_text = self._remove_noise(file_text)
        top_words = self._get_top_words(clean_text, top_words)
        suggestions = []
        for top_word in top_words:
            suggestions.extend(self.get_suggestions(top_word))
        suggestions.extend(top_words)
        tags = self._clean_tokens(suggestions)
        return ",".join(list(set(tags)))

    def _remove_noise(self, text):
        #1. Convert Text To Lowercase and remove numbers
        lower_case_text = str.lower(text)
        just_text = re.sub(r'\d+', '', lower_case_text)
        #2. Tokenise Paragraphs To words
        list = sent_tokenize(just_text)
        tokenizer = TreebankWordTokenizer()
        tokens = tokenizer.tokenize(just_text)
        #3. Clean text
        clean = self._clean_tokens(tokens)
        return clean

    def _clean_tokens(self, tokens):
        clean_words = [w for w in tokens if w not in punctuation]
        stopwords_to_remove = stopwords.words('english')
        clean = [w for w in clean_words if w not in stopwords_to_remove and not w.isnumeric()]
        return clean

    def get_suggestions(self, keyword):
        print(f'Searching pytrends for {keyword}')
        result = []
        self._pytrends.build_payload([keyword], cat=0, timeframe='today 12-m')
        data = self._pytrends.related_queries()[keyword]['top']
        if data is None or data.values is None:
            return result
        result.extend([x[0] for x in data.values.tolist()][:2])
        return result

    def _get_file_contents(self, file_path):
        return open(file_path, "r", encoding='utf-8',errors='ignore').read()

    def _get_top_words(self, words, top):
        counts = dict()

        for word in words:
            if word in counts:
                counts[word] += 1
            else:
                counts[word] = 1

        return list({k: v for k, v in sorted(counts.items(), key=lambda item: item[1])}.keys())[:top]


if __name__ == "1__main__":
    from pytrends.request import TrendReq

    nltk.download('punkt')
    nltk.download('stopwords')
    pytrends = TrendReq(hl='en-GB', tz=360)
    tags = KeywordsGenerator(pytrends)\
              .generate_tags('text_file.txt')
    print(tags)

Then The Dependencies

This script requires:

nltk==3.7
pytrends==4.8.0

Analysis of the Script

I copy and paste my article into text file.txt, and the code returns the keywords as a comma-separated string.

To achieve this:

1. A class I made is called KeywordsGenerator.

2. This class has a function: generate_tags

3. The function generate_tags performs the following tasks:

• retrieves text file contents

• uses NLP to clean the text by tokenizing sentences into words, removing punctuation, and other elements.

• identifies the most frequent words that are relevant.

• The pytrends API is then used to retrieve related phrases that are trending for each word from Google.

• finally adds a comma to the end of the word list.

4. I then use the keywords and paste them into the SEO area of my website.

These terms are trending on Google and relevant to my topic. My site's rankings and traffic have improved since I added new keywords. This little script puts our knowledge to work. I shared the script in case anyone faces similar issues.

I hope it helps readers sell their work.

A completely operating application requires many processes and technical challenges. Implementing coding standards can make apps right, work, and faster.

With years of experience working in software houses. Many client apps are scarcely maintained.

Why are these programs "barely maintainable"? If we're used to coding concepts, we can probably tell if an app is awful or good from its codebase.

This is how I coded much of my app.

Make It Work

Before adopting any concept, make sure the apps are completely functional. Why have a fully maintained codebase if the app can't be used?

The user doesn't care if the app is created on a super server or uses the greatest coding practices. The user just cares if the program helps them.

After the application is working, we may implement coding principles.

You Aren’t Gonna Need It

As a junior software engineer, I kept unneeded code, components, comments, etc., thinking I'd need them later.

In reality, I never use that code for weeks or months.

First, we must remove useless code from our primary codebase. If you insist on keeping it because "you'll need it later," employ version control.

If we remove code from our codebase, we can quickly roll back or copy-paste the previous code without preserving it permanently.

The larger the codebase, the more maintenance required.

Keep It Simple Stupid

Indeed. Keep things simple.

Why complicate something if we can make it simpler?

Our code improvements should lessen the server load and be manageable by others.

If our code didn't pass those benchmarks, it's too convoluted and needs restructuring. Using an open-source code critic or code smell library, we can quickly rewrite the code.

Simpler codebases and processes utilize fewer server resources.

Don't Repeat Yourself

Have you ever needed an action or process before every action, such as ensuring the user is logged in before accessing user pages?

As you can see from the above code, I try to call is user login? in every controller action, and it should be optimized, because if we need to rename the method or change the logic, etc. We can improve this method's efficiency.

We can write a constructor/middleware/before action that calls is_user_login?

The code is more maintainable and readable after refactoring.

Each programming language or framework handles this issue differently, so be adaptable.

Clean Code

Clean code is a broad notion that you've probably heard of before.

When creating a function, method, module, or variable name, the first rule of clean code is to be precise and simple.

The name should express its value or logic as a whole, and follow code rules because every programming language is distinct.

If you want to learn more about this topic, I recommend reading https://www.amazon.com/Clean-Code-Handbook-Software-Craftsmanship/dp/0132350882.

Standing On The Shoulder of Giants

Use industry standards and mature technologies, not your own(s).

There are several resources that explain how to build boilerplate code with tools, how to code with best practices, etc.

I propose following current conventions, best practices, and standardization since we shouldn't innovate on top of them until it gives us a competitive edge.

Boy Scout Rule

What reduces programmers' productivity?

When we have to maintain or build a project with messy code, our productivity decreases.

Having to cope with sloppy code will slow us down (shame of us).

How to cope? Uncle Bob's book says, "Always leave the campground cleaner than you found it."

When developing new features or maintaining current ones, we must improve our codebase. We can fix minor issues too. Renaming variables, deleting whitespace, standardizing indentation, etc.

Make It Fast

After making our code more maintainable, efficient, and understandable, we can speed up our app.

Whether it's database indexing, architecture, caching, etc.

A smart craftsman understands that refactoring takes time and it's preferable to balance all the principles simultaneously. Don't YAGNI phase 1.

Using these ideas in each iteration/milestone, while giving the bottom items less time/care.

You can check one of my articles for further information. https://medium.com/life-at-mekari/why-does-my-website-run-very-slowly-and-how-do-i-optimize-it-for-free-b21f8a2f0162

https://medium.com/life-at-mekari/what-you-need-to-make-your-app-a-high-availability-system-tackling-the-technical-challenges-8896abec363f

German stellarator revamped to run longer, hotter, compete with tokamaks

Tokamaks have dominated the search for fusion energy for decades. Just as ITER, the world's largest and most expensive tokamak, nears completion in southern France, a smaller, twistier testbed will start up in Germany.

If the 16-meter-wide stellarator can match or outperform similar-size tokamaks, fusion experts may rethink their future. Stellarators can keep their superhot gases stable enough to fuse nuclei and produce energy. They can theoretically run forever, but tokamaks must pause to reset their magnet coils.

The €1 billion German machine, Wendelstein 7-X (W7-X), is already getting "tokamak-like performance" in short runs, claims plasma physicist David Gates, preventing particles and heat from escaping the superhot gas. If W7-X can go long, "it will be ahead," he says. "Stellarators excel" Eindhoven University of Technology theorist Josefine Proll says, "Stellarators are back in the game." A few of startup companies, including one that Gates is leaving Princeton Plasma Physics Laboratory, are developing their own stellarators.

W7-X has been running at the Max Planck Institute for Plasma Physics (IPP) in Greifswald, Germany, since 2015, albeit only at low power and for brief runs. W7-X's developers took it down and replaced all inner walls and fittings with water-cooled equivalents, allowing for longer, hotter runs. The team reported at a W7-X board meeting last week that the revised plasma vessel has no leaks. It's expected to restart later this month to show if it can get plasma to fusion-igniting conditions.

Wendelstein 7-X's water-cooled inner surface allows for longer runs.

HOSAN/IPP

Both stellarators and tokamaks create magnetic gas cages hot enough to melt metal. Microwaves or particle beams heat. Extreme temperatures create a plasma, a seething mix of separated nuclei and electrons, and cause the nuclei to fuse, releasing energy. A fusion power plant would use deuterium and tritium, which react quickly. Non-energy-generating research machines like W7-X avoid tritium and use hydrogen or deuterium instead.

Tokamaks and stellarators use electromagnetic coils to create plasma-confining magnetic fields. A greater field near the hole causes plasma to drift to the reactor's wall.

Tokamaks control drift by circulating plasma around a ring. Streaming creates a magnetic field that twists and stabilizes ionized plasma. Stellarators employ magnetic coils to twist, not plasma. Once plasma physicists got powerful enough supercomputers, they could optimize stellarator magnets to improve plasma confinement.

W7-X is the first large, optimized stellarator with 50 6- ton superconducting coils. Its construction began in the mid-1990s and cost roughly twice the €550 million originally budgeted.

The wait hasn't disappointed researchers. W7-X director Thomas Klinger: "The machine operated immediately." "It's a friendly machine." It did everything we asked." Tokamaks are prone to "instabilities" (plasma bulging or wobbling) or strong "disruptions," sometimes associated to halted plasma flow. IPP theorist Sophia Henneberg believes stellarators don't employ plasma current, which "removes an entire branch" of instabilities.

In early stellarators, the magnetic field geometry drove slower particles to follow banana-shaped orbits until they collided with other particles and leaked energy. Gates believes W7-X's ability to suppress this effect implies its optimization works.

W7-X loses heat through different forms of turbulence, which push particles toward the wall. Theorists have only lately mastered simulating turbulence. W7-X's forthcoming campaign will test simulations and turbulence-fighting techniques.

A stellarator can run constantly, unlike a tokamak, which pulses. W7-X has run 100 seconds—long by tokamak standards—at low power. The device's uncooled microwave and particle heating systems only produced 11.5 megawatts. The update doubles heating power. High temperature, high plasma density, and extensive runs will test stellarators' fusion power potential. Klinger wants to heat ions to 50 million degrees Celsius for 100 seconds. That would make W7-X "a world-class machine," he argues. The team will push for 30 minutes. "We'll move step-by-step," he says.

W7-X's success has inspired VCs to finance entrepreneurs creating commercial stellarators. Startups must simplify magnet production.

Princeton Stellarators, created by Gates and colleagues this year, has $3 million to build a prototype reactor without W7-X's twisted magnet coils. Instead, it will use a mosaic of 1000 HTS square coils on the plasma vessel's outside. By adjusting each coil's magnetic field, operators can change the applied field's form. Gates: "It moves coil complexity to the control system." The company intends to construct a reactor that can fuse cheap, abundant deuterium to produce neutrons for radioisotopes. If successful, the company will build a reactor.

Renaissance Fusion, situated in Grenoble, France, raised €16 million and wants to coat plasma vessel segments in HTS. Using a laser, engineers will burn off superconductor tracks to carve magnet coils. They want to build a meter-long test segment in 2 years and a full prototype by 2027.

Type One Energy in Madison, Wisconsin, won DOE money to bend HTS cables for stellarator magnets. The business carved twisting grooves in metal with computer-controlled etching equipment to coil cables. David Anderson of the University of Wisconsin, Madison, claims advanced manufacturing technology enables the stellarator.

Anderson said W7-X's next phase will boost stellarator work. “Half-hour discharges are steady-state,” he says. “This is a big deal.”

After a crazy second half of 2021 and significant trade volumes into 2022, the market for NFT artworks like Bored Ape Yacht Club, CryptoPunks, and Pudgy Penguins has begun a sharp collapse as market downturns hit token values.

DappRadar data shows NFT monthly sales have fallen below $1 billion since June 2021. OpenSea, the world's largest NFT exchange, has seen sales volume decline 75% since May and is trading like July 2021.

Prices of popular non-fungible tokens have also decreased. Bored Ape Yacht Club (BAYC) has witnessed volume and sales drop 63% and 15%, respectively, in the past month.

BeInCrypto analysis shows market decline. May 2022 cryptocurrency marketplace volume was $4 billion, according to a news platform. This is a sharp drop from April's $7.18 billion.

OpenSea, a big marketplace, contributed $2.6 billion, while LooksRare, Magic Eden, and Solanart also contributed.

NFT markets are digital platforms for buying and selling tokens, similar stock trading platforms. Although some of the world's largest exchanges offer NFT wallets, most users store their NFTs on their favorite marketplaces.

In January 2022, overall NFT sales volume was $16.57 billion, with LooksRare contributing $11.1 billion. May 2022's volume was $12.57 less than January, a 75% drop, and June's is expected to be considerably smaller.

A World Based on Utility

Despite declines in NFT trading volumes, not all investors are negative on NFTs. Although there are uncertainties about the sustainability of NFT-based art collections, there are fewer reservations about utility-based tokens and their significance in technology's future.

In June, business CEO Christof Straub said NFTs may help artists monetize unreleased content, resuscitate catalogs, establish deeper fan connections, and make processes more efficient through technology.

We all know NFTs can't be JPEGs. Straub noted that NFT music rights can offer more equitable rewards to musicians.

Music NFTs are here to stay if they have real value, solve real problems, are trusted and lawful, and have fair and sustainable business models.

NFTs can transform numerous industries, including music. Market opinion is shifting towards tokens with more utility than the social media artworks we're used to seeing.

While the major NFT names remain dominant in terms of volume, new utility-based initiatives are emerging as top 20 collections.

Otherdeed, Sorare, and NBA Top Shot are NFT-based games that rank above Bored Ape Yacht Club and Cryptopunks.

Users can switch video NFTs of basketball players in NBA Top Shot. Similar efforts are emerging in the non-fungible landscape.

Sorare shows how NFTs can support a new way of playing fantasy football, where participants buy and swap trading cards to create a 5-player team that wins rewards based on real-life performances.

Sorare raised 579.7 million in one of Europe's largest Series B financing deals in September 2021. Recently, the platform revealed plans to expand into Major League Baseball.

Strong growth indications suggest a promising future for NFTs. The value of art-based collections like BAYC and CryptoPunks may be questioned as markets become diluted by new limited collections, but the potential for NFTs to become intrinsically linked to tangible utility like online gaming, music and art, and even corporate reward schemes shows the industry has a bright future.

I joined the 1000 Digital Startups Movement (Gerakan 1000 Startup Digital) in 2017 and learned a lot about the startup sector. My previous essay outlined what a startup is and what must be prepared. Here I'll offer raw ideas for better products.

Intro

A good startup solves a problem. These can include environmental, economic, energy, transportation, logistics, maritime, forestry, livestock, education, tourism, legal, arts and culture, communication, and information challenges. Everything I wrote is simply a basic idea (as inspiration) and requires more mapping and validation. Learn how to construct a startup to maximize launch success.

Adrian Gunadi (Investree Co-Founder) taught me that a Founder or Co-Founder must be willing to be CEO (Chief Everything Officer). Everything is independent, including drafting a proposal, managing finances, and scheduling appointments. The best individuals will come to you if you're the best. It's easier than consulting Andy Zain (Kejora Capital Founder).

Description

To help better understanding from your idea, try to answer this following questions:

- Describe your idea/application
Maximum 1000 characters.

- Background
Explain the reasons that prompted you to realize the idea/application.

- Objective
Explain the expected goals of the creation of the idea/application.

- Solution
A solution that tells your idea can be the right solution for the problem at hand.

- Uniqueness
What makes your idea/app unique?

- Market share
Who are the people who need and are looking for your idea?

- Marketing Ways and Business Models
What is the best way to sell your idea and what is the business model?

Not everything here is a startup idea. It's meant to inspire creativity and new perspectives.

Ideas

#Application

1. Medical students can operate on patients or not. Applications that train prospective doctors to distinguish body organs and their placement are useful. In the advanced stage, the app can be built with numerous approaches so future doctors can practice operating on patients based on their ailments. If they made a mistake, they'd start over. Future doctors will be more assured and make fewer mistakes this way.

2. VR (virtual reality) technology lets people see 3D space from afar. Later, similar technology was utilized to digitally sell properties, so buyers could see the inside and room contents. Every gadget has flaws. It's like a gold mine for robbers. VR can let prospective students see a campus's facilities. This facility can also help hotels promote their products.

3. How can retail entrepreneurs maximize sales? Most popular goods' sales data. By using product and brand/type sales figures, entrepreneurs can avoid overstocking. Walmart computerized their procedures to track products from the manufacturer to the store. As Retail Link products sell out, suppliers can immediately step in.

4. Failing to marry is something to be avoided. But if it had to happen, the loss would be like the proverb “rub salt into the wound”.  On the I do Now I dont website, Americans who don't marry can resell their jewelry to other brides-to-be. If some want to cancel the wedding and receive their down money and dress back, others want a wedding with particular criteria, such as a quick date and the expected building. Create a DP takeover marketplace for both sides.

#Games

1. Like in the movie, players must exit the maze they enter within 3 minutes or the shape will change, requiring them to change their strategy. The maze's transformation time will shorten after a few stages.

2. Treasure hunts involve following clues to uncover hidden goods. Here, numerous sponsors are combined in one boat, and participants can choose a game based on the prizes. Let's say X-mart is a sponsor and provides riddles or puzzles to uncover the prize in their store. After gathering enough points, the player can trade them for a gift utilizing GPS and AR (augmented reality). Players can collaborate to increase their chances of success.

3. Where's Wally? Where’s Wally displays a thick image with several things and various Wally-like characters. We must find the actual Wally, his companions, and the desired object. Make a game with a map where players must find objects for the next level. The player must find 5 artifacts randomly placed in an Egyptian-style mansion, for example. In the room, there are standard tickets, pass tickets, and gold tickets that can be removed for safekeeping, as well as a wall-mounted carpet that can be stored but not searched and turns out to be a flying rug that can be used to cross/jump to a different place. Regular tickets are spread out since they can buy life or stuff. At a higher level, a black ticket can lower your ordinary ticket. Objects can explode, scattering previously acquired stuff. If a player runs out of time, they can exchange a ticket for more.

#TVprogram

1. At the airport there are various visitors who come with different purposes. Asking tourists to live for 1 or 2 days in the city will be intriguing to witness.

2. Many professions exist. Carpenters, cooks, and lawyers must have known about job desks. Does HRD (Human Resource Development) only recruit new employees? Many don't know how to become a CEO, CMO, COO, CFO, or CTO. Showing young people what a Program Officer in an NGO does can help them choose a career.

#StampsCreations

Philatelists know that only the government can issue stamps. I hope stamps are creative so they have more worth.

1. Thermochromic pigments (leuco dyes) are well-known for their distinctive properties. By putting pigments to black and white batik stamps, for example, the black color will be translucent and display the basic color when touched (at a hot temperature).

2. In 2012, Liechtenstein Post published a laser-art Chinese zodiac stamp. Belgium (Bruges Market Square 2012), Taiwan (Swallow Tail Butterfly 2009), etc. Why not make a stencil of the president or king/queen?

3. Each country needs its unique identity, like Taiwan's silk and bamboo stamps. Create from your country's history. Using traditional paper like washi (Japan), hanji (Korea), and daluang/saeh (Indonesia) can introduce a country's culture.

4. Garbage has long been a problem. Bagasse, banana fronds, or corn husks can be used as stamp material.

5. Austria Post published a stamp containing meteor dust in 2006. 2004 meteorite found in Morocco produced the dust. Gibraltar's Rock of Gilbraltar appeared on stamps in 2002. What's so great about your country? East Java is muddy (Lapindo mud). Lapindo mud stamps will be popular. Red sand at Pink Beach, East Nusa Tenggara, could replace the mud.

#PostcardCreations

1. Map postcards are popular because they make searching easier. Combining laser-cut road map patterns with perforated 200-gram paper glued on 400-gram paper as a writing medium. Vision-impaired people can use laser-cut maps.

2. Regional art can be promoted by tucking traditional textiles into postcards.

3. A thin canvas or plain paper on the card's front allows the giver to be creative.

4. What is local crop residue? Cork lids, maize husks, and rice husks can be recycled into postcard materials.

5. Have you seen a dried-flower bookmark? Cover the postcard with mica and add dried flowers. If you're worried about losing the flowers, you can glue them or make a postcard envelope.

6. Wood may be ubiquitous; try a 0.2-mm copper plate engraved with an image and connected to a postcard as a writing medium.

7. Utilized paper pulp can be used to hold eggs, smartphones, and food. Form a smooth paper pulp on the plate with the desired image, the Golden Gate bridge, and paste it on your card.

8. Postcards can promote perfume. When customers rub their hands on the card with the perfume image, they'll smell the aroma.

#Tour #Travel

Tourism activities can be tailored to tourists' interests or needs. Each tourist benefits from tourism's distinct aim.

Let's define tourism's objective and purpose.

• Holiday Tour is a tour that its participants plan and do in order to relax, have fun, and amuse themselves.

• A familiarization tour is a journey designed to help travelers learn more about (survey) locales connected to their line of work.

• An educational tour is one that aims to give visitors knowledge of the field of work they are visiting or an overview of it.

• A scientific field is investigated and knowledge gained as the major goal of a scientific tour.

• A pilgrimage tour is one designed to engage in acts of worship.

• A special mission tour is one that has a specific goal, such a commerce mission or an artistic endeavor.

• A hunting tour is a destination for tourists that plans organized animal hunting that is only allowed by local authorities for entertainment purposes.

Every part of life has tourism potential. Activities include:

1. Those who desire to volunteer can benefit from the humanitarian theme and collaboration with NGOs. This activity's profit isn't huge but consider the environmental impact.

2. Want to escape the city? Meditation travel can help. Beautiful spots around the globe can help people forget their concerns. A certified yoga/meditation teacher can help travelers release bad energy.

3. Any prison visitors? Some prisons, like those for minors under 17, are open to visitors. This type of tourism helps mental convicts reach a brighter future.

4. Who has taken a factory tour/study tour? Outside-of-school study tour (for ordinary people who have finished their studies). Not everyone in school could tour industries, workplaces, or embassies to learn and be inspired. Shoyeido (an incense maker) and Royce (a chocolate maker) offer factory tours in Japan.

5. Develop educational tourism like astronomy and archaeology. Until now, only a few astronomy enthusiasts have promoted astronomy tourism. In Indonesia, archaeology activities focus on site preservation, and to participate, office staff must undertake a series of training (not everyone can take a sabbatical from their routine). Archaeological tourist activities are limited, whether held by history and culture enthusiasts or in regional tours.

6. Have you ever longed to observe a film being made or your favorite musician rehearsing? Such tours can motivate young people to pursue entertainment careers.

7. Pamper your pets to reduce stress. Many pet owners don't have time for walks or treats. These premium services target the wealthy.

8. A quirky idea to provide tours for imaginary couples or things. Some people marry inanimate objects or animals and seek to make their lover happy; others cherish their ashes after death.

#MISCideas

1. Fashion is a lifestyle, thus people often seek fresh materials. Chicken claws, geckos, snake skin casings, mice, bats, and fish skins are also used. Needs some improvement, definitely.

2. As fuel supplies become scarcer, people hunt for other energy sources. Sound is an underutilized renewable energy. The Batechsant technology converts environmental noise into electrical energy, according to study (Battery Technology Of Sound Power Plant). South Korean researchers use Sound-Driven Piezoelectric Nanowire based on Nanogenerators to recharge cell phone batteries. The Batechsant system uses existing noise levels to provide electricity for street lamp lights, aviation, and ships. Using waterfall sound can also energize hard-to-reach locations.

3. A New York Times reporter said IQ doesn't ensure success. Our school system prioritizes IQ above EQ (Emotional Quotient). EQ is a sort of human intelligence that allows a person to perceive and analyze the dynamics of his emotions when interacting with others (and with himself). EQ is suspected of being a bigger source of success than IQ. EQ training can gain greater attention to help people succeed. Prioritize role models from school stakeholders, teachers, and parents to improve children' EQ.

4. Teaching focuses more on theory than practice, so students are less eager to explore and easily forget if they don't pay attention. Has an engineer ever made bricks from arid red soil? Morocco's non-college-educated builders can create weatherproof bricks from red soil without equipment. Can mechanical engineering grads create a water pump to solve water shortages in remote areas? Art graduates can innovate beyond only painting. Artists may create kinetic sculpture by experimenting so much. Young people should understand these sciences so they can be more creative with their potential. These might be extracurricular activities in high school and university.

5. People have been trying to recycle agricultural waste for a long time. Mycelium helps replace light, easily crushed tiles and bricks (a collection of hyphae like in the manufacture of tempe). Waste must contain lignocellulose. In this vein, anti-mainstream painting canvases can be made. The goal is to create the canvas uneven like an amoeba outline, not square or spherical. The resulting canvas is lightweight and needs no frame. Then what? Open source your idea like Precious Plastic to establish a community. By propagating this notion, many knowledgeable people will help improve your product's quality and impact.

6. As technology and humans adapt, fraud increases. Making phony doctor's letters to fool superiors, fake credentials to get hired, fraudulent land certificates to make money, and fake news (hoax). The existence of a Wikimedia can aid the community by comparing bogus and original information.

7. Do you often hit a problem-solving impasse? Since the Doraemon bag hasn't been made, construct an Idea Bank. Everyone can contribute to solving problems here. How do you recruit volunteers? Obviously, a reward is needed. Contributors can become moderators or gain complimentary tickets to TIA (Tech in Asia) conferences. Idea Bank-related concepts: the rise of startups without a solid foundation generates an age as old as corn that does not continue. Those with startup ideas should describe them here so they can be validated by other users. Other users can contribute input if a comparable notion is produced to improve the product or integrate it. Similar-minded users can become Co-Founders.

8. Why not invest in fruit/vegetables, inspired by digital farming? The landowner obtains free fruit without spending much money on maintenance. Investors can get fruits/vegetables in larger quantities, fresher, and cheaper during harvest. Fruits and vegetables are often harmed if delivered too slowly. Rich investors with limited land can invest in teak, agarwood, and other trees. When harvesting, investors might choose raw results or direct wood sales earnings. Teak takes at least 7 years to harvest, therefore long-term wood investments carry the risk of crop failure.

9. Teenagers in distant locations can't count, read, or write. Many factors hinder locals' success. Life's demands force them to work instead of study. Creating a learning playground may attract young people to learning. Make a skatepark at school. Skateboarders must learn in school. Donations buy skateboards.

10. Globally, online taxi-bike is known. By hiring a motorcycle/car online, people no longer bother traveling without a vehicle. What if you wish to cross the island or visit remote areas? Is online boat or helicopter rental possible like online taxi-bike? Such a renting process has been done independently thus far and cannot be done quickly.

11. What do startups need now? A startup or investor consultant. How many startups fail to become Unicorns? Many founders don't know how to manage investor money, therefore they waste it on promotions and other things. Many investors only know how to invest and can't guide a struggling firm.

“In times of crisis, the wise build bridges, while the foolish build barriers.” — T’Challa [Black Panther]

Don't chase cash. Money is a byproduct. Profit-seeking is stressful. Market requirements are opportunities. If you have something to say, please comment.

This is only informational. Before implementing ideas, do further study.