Here’s why that’s baloney

Mark Cuban popularized entrepreneurship.

Shark Tank (which made Mark famous) made starting a business glamorous to attract more entrepreneurs. First off

This isn't an anti-billionaire rant.

Mark Cuban has done excellent. He's a smart, principled businessman. I enjoy his Web3 work. But Mark's work and productivity theories are absurd.

You don't need to outwork everyone in this recession to live well.

You won't be able to outwork me.

Yuck! Mark's words made me gag.

Why do boys think working is a football game where the winner wins a Super Bowl trophy? To outwork you.

Hard work doesn't equal intelligence.

Highly clever professionals spend 4 hours a day in a flow state, then go home to relax with family.

If you don't put forth the effort, someone else will.

- Mark.

He'll burn out. He's delusional and doesn't understand productivity. Boredom or disconnection spark our best thoughts.

TikTok outlaws boredom.

In a spare minute, we check our phones because we can't stand stillness.

All this work p*rn makes things worse. When is it okay to feel again? Because I can’t feel anything when I’m drowning in work and haven’t had a holiday in 2 years.

Your rivals are actively attempting to undermine you.

Ohhh please Mark…seriously.

This isn't a Tom Hanks war film. Relax. Not everyone is a rival. Only yourself is your competitor. To survive the recession, be better than a year ago.

If you get rich, great. If not, there's more to life than Lambos and angel investments.

Some want to relax and enjoy life. No competition. We witness people with lives trying to endure the recession and record-high prices.

This fictitious rival worsens life and work.

If you are truly talented, you will motivate others to work more diligently and effectively.

No Mark. Soz.

If you're a good leader, you won't brag about working hard and treating others like cogs. Treat them like humans. You'll have EQ.

Silly statements like this are caused by an out-of-control ego. No longer watch Shark Tank.

Ego over humanity.

Good leaders will urge people to keep together during the recession. Good leaders support those who are laid off and need a reference.

Not harder, quicker, better. That created my mental health problems 10 years ago.

Truth: we want to work less.

The promotion of entrepreneurship is ludicrous.

Marvel superheroes. Seriously, relax Max.

I used to write about entrepreneurship, then I quit. Many WeWork Adam Neumanns. Carelessness.

I now utilize the side hustle title when writing about online company or entrepreneurship. Humanizes.

Stop glorifying. Thinking we'll all be Elon Musks who send rockets to Mars is delusional. Most of us won't create companies employing hundreds.

OK.

The true epidemic is glorification. fewer selfies Little birdy needs less bank account screenshots. Less Uber talk.

We're exhausted.

Fun, ego-free business can transform the world. Take a relax pill.

Work as if someone were attempting to take everything from you.

I've seen people lose everything.

Myself included. My 20s startup failed. I was almost bankrupt. I thought I'd never recover. Nope.

Best thing ever.

Losing everything reveals your true self. Unintelligent entrepreneur egos perish instantly. Regaining humility revitalizes relationships.

Money's significance shifts. Stop chasing it like a puppy with a bone.

Fearing loss is unfounded.

Here is a more effective approach than outworking nobody.

(You'll thrive in the recession and become wealthy.)

Smarter work

Overworking is donkey work.

You don't want to be a career-long overworker. Instead than wasting time, write down what you do. List tasks and processes.

Keep doing/outsource the list. Step-by-step each task. Continuously systematize.

Then recruit a digital employee like Zapier or a virtual assistant in the same country.

Intelligent, not difficult.

If your big break could burn in hell, diversify like it will.

People err by focusing on one chance.

Chances can vanish. All-in risky. Instead of working like a Mark Cuban groupie, diversify your income.

If you're employed, your customer is your employer.

Sell the same abilities twice and add 2-3 contract clients. Reduce your hours at your main job and take on more clients.

Leave brand loyalty behind

Mark desires his employees' worship.

That's stupid. When times are bad, layoffs multiply. The problem is the false belief that companies care. No. A business maximizes profit and pays you the least.

To care or overpay is anti-capitalist (that run the world). Be honest.

I was a banker. Then the bat virus hit and jobs disappeared faster than I urinate after a night of drinking.

Start being disloyal now since your company will cheerfully replace you with a better applicant. Meet recruiters and hiring managers on LinkedIn. Whenever something goes wrong at work, act.

Loyalty to self and family. Nobody.

Outwork this instead

Mark doesn't suggest outworking inflation instead of people.

Inflation erodes your time on earth. If you ignore inflation, you'll work harder for less pay every minute.

Financial literacy beats inflation.

Get a side job and earn money online

So you can stop outworking everyone.

Internet leverages time. Same effort today yields exponential results later. There are still whole places not online.

Instead of working forever, generate money online.

Final Words

Overworking is stupid. Don't listen to wealthy football jocks.

Work isn't everything. Prioritize diversification, internet income streams, boredom, and financial knowledge throughout the recession.

That’s how to get wealthy rather than burnout-rich.

Without self-control, you'll waste time chasing dopamine fixes.

I found a powerful quote in Tony Robbins' Awaken The Giant Within:

“Most of the challenges that we have in our personal lives come from a short-term focus” — Tony Robbins

Most people are short-term oriented, but highly successful people are long-term oriented.

Successful people act in line with their long-term goals and values, while the rest are distracted by short-term pleasures and dopamine fixes.

Instant gratification wrecks lives

Instant pleasure is fleeting. Quickly fading effects leave you craving more stimulation.

Before you know it, you're in a cycle of quick fixes. This explains binging on food, social media, and Netflix.

These things cause a dopamine spike, which is entertaining. This dopamine spike crashes quickly, leaving you craving more stimulation.

It's fine to watch TV or play video games occasionally. Problems arise when brain impulses aren't controlled. You waste hours chasing dopamine fixes.

Instant gratification becomes problematic when it interferes with long-term goals, happiness, and life fulfillment.

Most rewarding things require delay

Life's greatest rewards require patience and delayed gratification. They must be earned through patience, consistency, and effort.

Ex:

• A fit, healthy body

• A deep connection with your spouse

• A thriving career/business

• A healthy financial situation

These are some of life's most rewarding things, but they take work and patience. They all require the ability to delay gratification.

To have a healthy bank account, you must save (and invest) a large portion of your monthly income. This means no new tech or clothes.

If you want a fit, healthy body, you must eat better and exercise three times a week. So no fast food and Netflix.

It's a battle between what you want now and what you want most.

Successful people choose what they want most over what they want now. It's a major difference.

Instant vs. delayed gratification

Most people subconsciously prefer instant rewards over future rewards, even if the future rewards are more significant.

We humans aren't logical. Emotions and instincts drive us. So we act against our goals and values.

Fortunately, instant gratification bias can be overridden. This is a modern superpower. Effective methods include:

#1: Train your brain to handle overstimulation

Training your brain to function without constant stimulation is a powerful change. Boredom can lead to long-term rewards.

Unlike impulsive shopping, saving money is boring. Having lots of cash is amazing.

Compared to video games, deep work is boring. A successful online business is rewarding.

Reading books is boring compared to scrolling through funny videos on social media. Knowledge is invaluable.

You can't do these things if your brain is overstimulated. Your impulses will control you. To reduce overstimulation addiction, try:

• Daily meditation (10 minutes is enough)

• Daily study/work for 90 minutes (no distractions allowed)

• First hour of the day without phone, social media, and Netflix

• Nature walks, journaling, reading, sports, etc.

#2: Make Important Activities Less Intimidating

Instant gratification helps us cope with stress. Starting a book or business can be intimidating. Video games and social media offer a quick escape in such situations.

Make intimidating tasks less so. Break them down into small tasks. Start a new business/side-hustle by:

• Get domain name

• Design website

• Write out a business plan

• Research competition/peers

• Approach first potential client

Instead of one big mountain, divide it into smaller sub-tasks. This makes a task easier and less intimidating.

#3: Plan ahead for important activities

Distractions will invade unplanned time. Your time is dictated by your impulses, which are usually Netflix, social media, fast food, and video games. It wants quick rewards and dopamine fixes.

Plan your days and be proactive with your time. Studies show that scheduling activities makes you 3x more likely to do them.

To achieve big goals, you must plan. Don't gamble.

Want to get fit? Schedule next week's workouts. Want a side-job? Schedule your work time.

If I could tell my younger self a few rules, they would boost my career.

1. Treat design like medicine; don't get attached.

If it doesn't help, you won't be angry, but you'll try to improve it. Designers blame others if they don't like the design, but the rule is the same: we solve users' problems. You're not your design, and neither are they. Be humble with your work because your assumptions will often be wrong and users will behave differently.

2. Consider your design flawed.

Disagree with yourself, then defend your ideas. Most designers forget to dig deeper into a pattern, screen, button, or copywriting. If someone asked, "Have you considered alternatives? How does this design stack up? Here's a functional UX checklist to help you make design decisions.

3. Codeable solutions.

If your design requires more developer time, consider whether it's worth spending more money to code something with a small UX impact. Overthinking problems and designing abstract patterns is easy. Sometimes you see something on dribbble or bechance and try to recreate it, but it's not worth it. Here's my article on it.

4. Communication changes careers

Designers often talk with users, clients, companies, developers, and other designers. How you talk and present yourself can land you a job. Like driving or swimming, practice it. Success requires being outgoing and friendly. If I hadn't said "hello" to a few people, I wouldn't be where I am now.

5. Ignorance of the law is not an excuse.

Copyright, taxation How often have you used an icon without checking its license? If you use someone else's work in your project, the owner can cause you a lot of problems — paying a lot of money isn't worth it. Spend a few hours reading about copyrights, client agreements, and taxes.

6. Always test your design

If nobody has seen or used my design, it's not finished. Ask friends about prototypes. Testing reveals how wrong your assumptions were. Steve Krug, one of the authorities on this topic will tell you more about how to do testing.

7. Run workshops

A UX designer's job involves talking to people and figuring out what they need, which is difficult because they usually don't know. Organizing teamwork sessions is a powerful skill, but you must also be a good listener. Your job is to help a quiet, introverted developer express his solution and control the group. AJ Smart has more on workshops here.

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.

Do you want to build and launch your own software company? To do this, all you need is a product that solves a problem.

Coming up with profitable ideas is not that easy. But you’re in luck because you got me!

I’ll give you the idea for free. All you need to do is execute it properly.

If you’re ready, let’s jump right into it! Starting with the problem.

Problem

Youtube has many creators. Every day, they think of new ways to entertain or inform us.

They work hard to make videos. Many of their efforts go to waste. They limit their revenue and reach.

Solution

Content repurposing solves this problem.

One video can become several TikToks. Creating YouTube videos from a podcast episode.

Or, one video might become a blog entry.

By turning videos into blog entries, Youtubers may develop evergreen SEO content, attract a new audience, and reach a non-YouTube audience.

Many YouTube creators want this easy feature.

Let's build it!

Implementation

We identified the problem, and we have a solution. All that’s left to do is see how it can be done.

Monitoring new video uploads

First, watch when a friend uploads a new video. Everything should happen automatically without user input.

YouTube Webhooks make this easy. Our server listens for YouTube Webhook notifications.

After publishing a new video, we create a conversion job.

Creating a Blog Post from a Video

Next, turn a video into a blog article.

To convert, we must extract the video's audio (which can be achieved by using FFmpeg on the server).

Once we have the audio channel, we can use speech-to-text.

Services can accomplish this easily.

• Speech-to-text on Google

• Google Translate

• Deepgram

Deepgram's affordability and integration make it my pick.

After conversion, the blog post needs formatting, error checking, and proofreading.

After this, a new blog post will appear in our web app's dashboard.

Completing a blog post

After conversion, users must examine and amend their blog posts.

Our application dashboard would handle all of this. It's a dashboard-style software where users can:

• Link their Youtube account

• Check out the converted videos in the future.

• View the conversions that are ongoing.

• Edit and format converted blog articles.

It's a web-based app.

It doesn't matter how it's made but I'd choose Next.js.

Next.js is a React front-end standard. Vercel serverless functions could conduct the conversions.

This would let me host the software for free and reduce server expenditures.

Taking It One Step Further

SaaS in a nutshell. Future improvements include integrating with WordPress or Ghost.

Our app users could then publish blog posts. Streamlining the procedure.

MVPs don't need this functionality.

Final Thoughts

Repurposing content helps you post more often, reach more people, and develop faster.

Many agencies charge a fortune for this service. Handmade means pricey.

Content creators will go crazy if you automate and cheaply solve this problem.

Just execute this idea!