A fascinating business conversation.

This week at Web Summit, I had mentor hour.

Mentor hour connects startups with experienced entrepreneurs.

The YC-selected founder who mentored me had grown his company to $100 million in 18 months.

I had 45 minutes to question him.

I've compiled this.

Context

Founder's name is Zack.

After working in private equity, Zack opted to acquire an MBA.

Surrounded by entrepreneurs at a prominent school, he decided to become one himself.

Unsure how to proceed, he bet on two horses.

On one side, he received an offer from folks who needed help running their startup owing to lack of time. On the other hand, he had an idea for a SaaS to start himself.

He just needed to validate it.

Validating

Since Zack's proposal helped companies, he contacted university entrepreneurs for comments.

He contacted university founders.

Once he knew he'd correctly identified the problem and that people were willing to pay to address it, he started developing.

He earned $100k in a university entrepreneurship competition.

His plan was evident by then.

The other startup's founders saw his potential and granted him $400k to launch his own SaaS.

Hiring

He started looking for a tech co-founder because he lacked IT skills.

He interviewed dozens and picked the finest.

As he didn't want to wait for his program to be ready, he contacted hundreds of potential clients and got 15 letters of intent promising they'd join up when it was available.

YC accepted him by then.

He had enough positive signals to raise.

Raising

He didn't say how many VCs he called, but he indicated 50 were interested.

He jammed meetings into two weeks to generate pressure and encourage them to invest.

Seed raise: $11 million.

Selling

His objective was to contact as many entrepreneurs as possible to promote his product.

He first contacted startups by scraping CrunchBase data.

Once he had more money, he started targeting companies with ZoomInfo.

His VC urged him not to hire salespeople until he closed 50 clients himself.

He closed 100 and hired a CRO through a headhunter.

Scaling

Three persons started the business.

1. He primarily works in sales.

2. Coding the product was done by his co-founder.

3. Another person performing operational duties.

He regretted recruiting the third co-founder, who was ineffective (could have hired an employee instead).

He wanted his company to be big, so he hired two young marketing people from a competing company.

After validating several marketing channels, he chose PR.

$100 Million and under

He developed a sales team and now employs 30 individuals.

He raised a $100 million Series A.

Additionally, he stated

• He’s been rejected a lot. Like, a lot.

• Two great books to read: Steve Jobs by Isaacson, and Why Startups Fail by Tom Eisenmann.

• The best skill to learn for non-tech founders is “telling stories”, which means sales. A founder’s main job is to convince: co-founders, employees, investors, and customers. Learn code, or learn sales.

Conclusion

I often read about these stories but hardly take them seriously.

Zack was amazing.

Three things about him stand out:

1. His vision. He possessed a certain amount of fire.

2. His vitality. The man had a lot of enthusiasm and spoke quickly and decisively. He takes no chances and pushes the envelope in all he does.

3. His Rolex.

He didn't do all this in 18 months.

Not really.

He couldn't launch his company without private equity experience.

These accounts disregard entrepreneurs' original knowledge.

Hormozi will tell you how he founded Gym Launch, but he won't tell you how he had a gym first, how he worked at uni to pay for his gym, or how he went to the gym and learnt about fitness, which gave him the idea to open his own.

Nobody knows nothing. If you scale quickly, it's probable because you gained information early.

Lincoln said, "Give me six hours to chop down a tree, and I'll spend four sharpening the axe."

Sharper axes cut trees faster.

Reflections on the closed company Hola! Dating app

I'll discuss my fuckups as an entrepreneur and CEO. All of them refer to the dating app Hola!, which I co-founded and starred in.

Spring 2021 was when we started. Two techies and two non-techies created a dating app. Pokemon Go and Tinder were combined.

Online dating is a business, and it takes two weeks from a like to a date. We questioned online dating app users if they met anyone offline last year.

75% replied yes, 50% sometimes, 25% usually.

Offline dating is popular, yet people have concerns.

• Men are reluctant to make mistakes in front of others.

• Women are curious about the background of everyone who approaches them.

We designed unique mechanics that let people date after a match. No endless chitchat. Women would be safe while men felt like cowboys.

I wish to emphasize three faults that lead to founders' estrangement.

This detachment ultimately led to us shutting down the company.

The wrong technology stack

Situation

Instead of generating a faster MVP and designing an app in a universal stack for iOS and Android, I argued we should pilot the app separately for iOS and Android. Technical founders' expertise made this possible.

Self-reflection

Mistaken strategy. We lost time and resources developing two apps at once. We chose iOS since it's more profitable. Apple took us out after the release, citing Guideline 4.3 Spam. After 4 months, we had nothing. We had a long way to go to get the app on Android and the Store.

I suggested creating a uniform platform for the company's growth. This makes parallel product development easier. The strategist's lack of experience and knowledge made it a piece of crap.

What would I have changed if I could?

We should have designed an Android universal stack. I expected Apple to have issues with a dating app.

Our approach should have been to launch something and subsequently improve it, but prejudice won.

The lesson

Discuss the IT stack with your CTO. It saves time and money. Choose the easiest MVP method.

2. A tardy search for investments

Situation

Though the universe and other founders encouraged me to locate investors first, I started pitching when we almost had an app.

When angels arrived, it was time to close. The app was banned, war broke out, I left the country, and the other co-founders stayed. We had no savings.

Self-reflection

I loved interviewing users. I'm proud of having done 1,000 interviews. I wanted to understand people's pain points and improve the product.

Interview results no longer affected the product. I was terrified to start pitching. I filled out accelerator applications and redid my presentation. You must go through that so you won't be terrified later.

What would I have changed if I could?

Get an external or internal mentor to help me with my first pitch as soon as possible. I'd be supported if criticized. He'd cheer with me if there was enthusiasm.

In 99% of cases, I'm comfortable jumping into the unknown, but there are exceptions. The mentor's encouragement would have prompted me to act sooner.

The lesson

Begin fundraising immediately. Months may pass. Show investors your pre-MVP project. Draw inferences from feedback.

3. Role ambiguity

Situation

My technical co-founders were also part-time lead developers, which produced communication issues. As co-founders, we communicated well and recognized the problems. Stakes, vesting, target markets, and approach were agreed upon.

We were behind schedule. Technical debt and strategic gap grew.

Bi-daily and weekly reviews didn't help. Each time, there were explanations. Inside, I was freaking out.

Self-reflection

I am a fairly easy person to talk to. I always try to stick to agreements; otherwise, my head gets stuffed with unnecessary information, interpretations, and emotions.

Sit down -> talk -> decide -> do -> evaluate the results. Repeat it.

If I don't get detailed comments, I start ruining everyone's mood. If there's a systematic violation of agreements without a good justification, I won't join the project or I'll end the collaboration.

What would I have done otherwise?

This is where it’s scariest to draw conclusions. Probably the most logical thing would have been not to start the project as we started it. But that was already a completely different project. So I would not have done anything differently and would have failed again.

But I drew conclusions for the future.

The lesson

First-time founders should find an adviser or team coach for a strategic session. It helps split the roles and responsibilities.

Follower count, earnings, and more

I created my 7th TikTok account six months ago. TikTok's great. I've developed accounts for Amazon products, content creators/brand deals education, website flipping, and more.

Introverted or shy people use faceless TikTok accounts.

Maybe they don't want millions of people to see their face online, or they want to remain anonymous so relatives and friends can't locate them.

Going faceless on TikTok can help you grow a following, communicate your message, and make money online.

Here are 6 steps I took to turn my Tik Tok account into a $60,000/year side gig.

From nothing to $60K in 6 months

It's clickbait, but it’s true. Here’s what I did to get here.

Quick context:

I've used social media before. I've spent years as a social creator and brand.

I've built Instagram, TikTok, and YouTube accounts to nearly 100K.

How I did it

First, select a niche.

If you can focus on one genre on TikTok, you'll have a better chance of success, however lifestyle creators do well too.

Niching down is easier, in my opinion.

Examples:

• Travel

• Food

• Kids

• Earning cash

• Finance

You can narrow these niches if you like.

During the pandemic, a travel blogger focused on Texas-only tourism and gained 1 million subscribers.

Couponing might be a finance specialization.

One of my finance TikTok accounts gives credit tips and grants and has 23K followers.

Tons of ways you can get more specific.

Consider how you'll monetize your TikTok account. I saw many enormous TikTok accounts that lose money.

Why?

They can't monetize their niche. Not impossible to commercialize, but tough enough to inhibit action.

First, determine your goal.

In this first step, consider what your end goal is.

Are you trying to promote your digital products or social media management services?

You want brand deals or e-commerce sales.

This will affect your TikTok specialty.

This is the first step to a TikTok side gig.

Step 2: Pick a content style

Next, you want to decide on your content style.

Do you do voiceover and screenshots?

You'll demonstrate a product?

Will you faceless vlog?

Step 3: Look at the competition

Find anonymous accounts and analyze what content works, where they thrive, what their audience wants, etc.

This can help you make better content.

Like the skyscraper method for TikTok.

Step 4: Create a content strategy.

Your content plan is where you sit down and decide:

• How many videos will you produce each day or each week?

• Which links will you highlight in your biography?

• What amount of time can you commit to this project?

You may schedule when to post videos on a calendar. Make videos.

5. Create videos.

No video gear needed.

Using a phone is OK, and I think it's preferable than posting drafts from a computer or phone.

TikTok prefers genuine material.

Use their app, tools, filters, and music to make videos.

And imperfection is preferable. Tik okers like to see videos made in a bedroom, not a film studio.

Make sense?

When making videos, remember this.

I personally use my phone and tablet.

Step 6: Monetize

Lastly, it’s time to monetize How will you make money? You decided this in step 1.

Time to act!

For brand agreements

• Include your email in the bio.

• Share several sites and use a beacons link in your bio.

• Make cold calls to your favorite companies to get them to join you in a TikTok campaign.

For e-commerce

• Include a link to your store's or a product's page in your bio.

For client work

• Include your email in the bio.

• Use a beacons link to showcase your personal website, portfolio, and other resources.

For affiliate marketing

• Include affiliate product links in your bio.

• Join the Amazon Influencer program and provide a link to your storefront in your bio.

$60,000 per year from Tik Tok?

Yes, and some creators make much more.

Tori Dunlap (herfirst100K) makes $100,000/month on TikTok.

My TikTok adventure took 6 months, but by month 2 I was making $1,000/month (or $12K/year).

By year's end, I want this account to earn $100K/year.

Imagine if my 7 TikTok accounts made $100K/year.

7 Tik Tok accounts X $100K/yr = $700,000/year

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.

What works and what’s just more toxic positivity

Just keep at it; you’ll get it.

I closed the Zoom call and immediately dropped my head. Open tabs included material on inspiration, burnout, and recovery.

I searched everywhere for ways to avoid burnout.

It wasn't that I needed to keep going, change my routine, employ 8D audio playlists, or come up with fresh ideas. I had several ideas and a schedule. I knew what to do.

I wasn't interested. I kept reading, changing my self-care and mental health routines, and writing even though it was tiring.

Since burnout became a psychiatric illness in 2019, thousands have shared their experiences. It's spreading rapidly among writers.

What is the actual key to recovering from burnout?

Every A-list burnout story emphasizes prevention. Other lists provide repackaged self-care tips. More discuss mental health.

It's like the mid-2000s, when pink quotes about bubble baths saturated social media.

The self-care mania cost us all. Self-care is crucial, but utilizing it to address everything didn't work then or now.

How can you recover from burnout?

Time

Are extended breaks actually good for you? Most people need a break every 62 days or so to avoid burnout.

Real-life burnout victims all took breaks. Perhaps not a long hiatus, but breaks nonetheless.

Burnout is slow and gradual. It takes little bits of your motivation and passion at a time. Sometimes it’s so slow that you barely notice or blame it on other things like stress and poor sleep.

Burnout doesn't come overnight; neither will recovery.

I don’t care what anyone else says the cure for burnout is. It has to be time because time is what gave us all burnout in the first place.