If asked, run.

An entrepreneur asked me about her pitch deck. A Platform as a Service (PaaS).

She told me she hadn't done her 5-year forecasts but would soon.

I said, Don't bother. I added "time-wasting."

“I've been asked”, she said.

“Who asked?”

“a VC”

“5-year forecast?”

“Yes”

“Get another VC. If he asks, it's because he doesn't understand your solution or to waste your time.”

Some VCs are lagging. They're still using steam engines.

10-years ago, 5-year forecasts were requested.

Since then, we've adopted a 3-year plan.

But It's outdated.

Max one year.

What has happened?

Revolutionary technology. NO-CODE.

Revolution's consequences?

Product viability tests are shorter. Hugely. SaaS and PaaS.

Let me explain:

• Building a minimum viable product (MVP) that works only takes a few months.

• 1 to 2 months for practical testing.

• Your company plan can be validated or rejected in 4 months as a consequence.

After validation, you can ask for VC money. Even while a prototype can generate revenue, you may not require any.

Good VCs won't ask for a 3-year business plan in that instance.

One-year, though.

If you want, establish a three-year plan, but realize that the second year will be different.

You may have changed your business model by then.

A VC isn't interested in a three-year business plan because your solution may change.

Your ability to create revenue will be key.

• But also, to pivot.

• They will be interested in your value proposition.

• They will want to know what differentiates you from other competitors and why people will buy your product over another.

• What will interest them is your resilience, your ability to bounce back.

• Not to mention your mindset. The fact that you won’t get discouraged at the slightest setback.

• The grit you have when facing adversity, as challenges will surely mark your journey.

• The authenticity of your approach. They’ll want to know that you’re not just in it for the money, let alone to show off.

• The fact that you put your guts into it and that you are passionate about it. Because entrepreneurship is a leap of faith, a leap into the void.

• They’ll want to make sure you are prepared for it because it’s not going to be a walk in the park.

• They’ll want to know your background and why you got into it.

• They’ll also want to know your family history.

• And what you’re like in real life.

So a 5-year plan…. You can bet they won’t give a damn. Like their first pair of shoes.

40 years pass. After 14 years of retirement, you die. Am I the only one who sees the problem?

I’m the Jedi master of escaping the rat race.

Not to impress. I know this works since I've tried it. Quitting a job to make money online is worse than Kim Kardashian's internet-burning advice.

Let me help you rethink the move from a career to online income to f*ck you money.

To understand why a job is a joke, do some life math.

Without a solid why, nothing makes sense.

The retirement age is 65. Our processed food consumption could shorten our 79-year average lifespan.

You spend 40 years working.

After 14 years of retirement, you die.

Am I alone in seeing the problem?

Life is too short to work a job forever, especially since most people hate theirs. After-hours skills are vital.

Money equals unrestricted power, f*ck you.

F*ck you money is the answer.

Jack Raines said it first. He says we can do anything with the money. Jack, a young rebel straight out of college, can travel and try new foods.

F*ck you money signifies not checking your bank account before buying.

F*ck you” money is pure, unadulterated freedom with no strings attached.

Jack claims you're rich when you rarely think about money.

Avoid confusion.

This doesn't imply you can buy a Lamborghini. It indicates your costs, income, lifestyle, and bank account are balanced.

Jack established an online portfolio while working for UPS in Atlanta, Georgia. So he gained boundless power.

The portion that many erroneously believe

Yes, you need internet abilities to make money, but they're not different from 9-5 talents.

Sahil Lavingia, Gumroad's creator, explains.

A job is a way to get paid to learn.

Mistreat your boss 9-5. Drain his skills. Defuse him. Love and leave him (eventually).

Find another employment if yours is hazardous. Pick an easy job. Make sure nothing sneaks into your 6-12 time slot.

The dumb game that makes you a sheep

A 9-5 job requires many job interviews throughout life.

You email your résumé to employers and apply for jobs through advertisements. This game makes you a sheep.

You're competing globally. Work-from-home makes the competition tougher. If you're not the cheapest, employers won't hire you.

After-hours online talents (say, 6 pm-12 pm) change the game. This graphic explains it better:

Online talents boost after-hours opportunities.

You go from wanting to be picked to picking yourself. More chances equal more money. Your f*ck you fund gets the extra cash.

A novel method of learning is essential.

College costs six figures and takes a lifetime to repay.

Informal learning is distinct. 6-12pm:

• Observe the carefully controlled Twitter newsfeed.

• Make use of Teachable and Gumroad's online courses.

• Watch instructional YouTube videos

• Look through the top Substack newsletters.

Informal learning is more effective because it's not obvious. It's fun to follow your curiosity and hobbies.

The majority of people lack one attitude. It's simple to learn.

One big impediment stands in the way of f*ck you money and time independence. So often.

Too many people plan after 6-12 hours. Dreaming. Big-thinkers. Strategically. They fill their calendar with meetings.

This is after-hours masturb*tion.

Sahil Bloom reminded me that a bias towards action will determine if this approach works for you.

The key isn't knowing what to do from 6-12 a.m. Trust yourself and develop abilities as you go. It's for building the parachute after you jump.

Sounds risky. We've eliminated the risk by finishing this process after hours while you work 9-5.

With no risk, you can have an I-don't-care attitude and still be successful.

When you choose to move forward, this occurs.

Once you try 9-5/6-12, you'll tell someone.

It's bad.

Few of us hang out with problem-solvers.

It's how much of society operates. So they make reasons so they can feel better about not giving you money.

Matthew Kobach told me chasing f*ck you money is easier with like-minded folks.

Without f*ck you money friends, loneliness will take over and you'll think you've messed up when you just need to keep going.

Steal this easy guideline

Let's act. No more fluffing and caressing.

1. Learn

If you detest your 9-5 talents or don't think they'll work online, get new ones. If you're skilled enough, continue.

Easlo recommends these skills:

• Designer for Figma

• Designer Canva

• bubble creators

• editor in Photoshop

• Automation consultant for Zapier

• Designer of Webflow

• video editor Adobe

• Ghostwriter for Twitter

• Idea consultant

• Artist in Blender Studio

2. Develop the ability

Every night from 6-12, apply the skill.

Practicing ghostwriting? Write someone's tweets for free. Do someone's website copy to learn copywriting. Get a website to the top of Google for a keyword to understand SEO.

Free practice is crucial. Your 9-5 pays the money, so work for free.

3. Take off stealthily like a badass

Another mistake. Sell to few. Don't be the best. Don't claim expertise.

Sell your new expertise to others behind you.

Two ways:

• Using a digital good

• By providing a service,

Point 1 also includes digital service examples. Digital products include eBooks, communities, courses, ad-supported podcasts, and templates. It's easy. Your 9-5 job involves one of these.

Take ideas from work.

Why? They'll steal your time for profit.

4. Iterate while feeling awful

First-time launches always fail. You'll feel terrible. Okay. Remember your 9-5?

Find improvements. Ask free and paying consumers what worked.

Multiple relaunches, each 1% better.

5. Discover more

Never stop learning. Improve your skill. Add a relevant skill. Learn copywriting if you write online.

After-hours students earn the most.

6. Continue

Repetition is key.

7. Make this one small change.

Consistently. The 6-12 momentum won't make you rich in 30 days; that's success p*rn.

Consistency helps wage slaves become f*ck you money. Most people can't switch between the two.

Putting everything together

It's easy. You're probably already doing some.

This formula explains why, how, and what to do. It's a 5th-grade-friendly blueprint. Good.

Reduce financial risk with your 9-to-5. Replace Netflix with 6-12 money-making talents.

Life is short; do whatever you want. Today.

5 VC investment criteria

I reviewed 200 startup business concepts last week. Brainache.

The enterprises sold various goods and services. The concepts were achingly similar: give us money, we'll produce a product, then get more to expand. No different from daily plans and pitches.

Most of those 200 plans sounded plausible. But 10% looked venture-worthy. 90% of startups need alternatives to venture finance.

With the success of VC-backed businesses and the growth of venture funds, a common misperception is that investors would fund any decent company idea. Finding investors that believe in the firm and founders is the key to funding.

Incorrect. Venture capital needs investing in certain enterprises. If your startup doesn't match the model, as most early-stage startups don't, you can revise your business plan or locate another source of capital.

Before spending six months pitching angels and VCs, make sure your startup fits these criteria.

Likely to generate $100 million in sales

First, I check the income predictions in a pitch deck. If it doesn't display $100M, don't bother.

The math doesn't work for venture financing in smaller businesses.

Say a fund invests $1 million in a startup valued at $5 million that is later acquired for $20 million. That's a win everyone should celebrate. Most VCs don't care.

Consider a $100M fund. The fund must reach $360M in 7 years with a 20% return. Only 20-30 investments are possible. 90% of the investments will fail, hence the 23 winners must return $100M-$200M apiece. $15M isn't worth the work.

Angel investors and tiny funds use the same ideas as venture funds, but their smaller scale affects the calculations. If a company can support its growth through exit on less than $2M in angel financing, it must have $25M in revenues before large companies will consider acquiring it.

Aiming for Hypergrowth

A startup's size isn't enough. It must expand fast.

Developing a great business takes time. Complex technology must be constructed and tested, a nationwide expansion must be built, or production procedures must go from lab to pilot to factories. These can be enormous, world-changing corporations, but venture investment is difficult.

The normal 10-year venture fund life. Investments are made during first 3–4 years.. 610 years pass between investment and fund dissolution. Funds need their investments to exit within 5 years, 7 at the most, therefore add a safety margin.

Longer exit times reduce ROI. A 2-fold return in a year is excellent. Loss at 2x in 7 years.

Lastly, VCs must prove success to raise their next capital. The 2nd fund is raised from 1st fund portfolio increases. Third fund is raised using 1st fund's cash return. Fund managers must raise new money quickly to keep their jobs.

Branding or technology that is protected

No big firm will buy a startup at a high price if they can produce a competing product for less. Their development teams, consumer base, and sales and marketing channels are large. Who needs you?

Patents, specialist knowledge, or brand name are the only answers. The acquirer buys this, not the thing.

I've heard of several promising startups. It's not a decent investment if there's no exit strategy.

A company that installs EV charging stations in apartments and shopping areas is an example. It's profitable, repeatable, and big. A terrific company. Not a startup.

This building company's operations aren't secret. No technology to protect, no special information competitors can't figure out, no go-to brand name. Despite the immense possibilities, a large construction company would be better off starting their own.

Most venture businesses build products, not services. Services can be profitable but hard to safeguard.

Probable purchase at high multiple

Once a software business proves its value, acquiring it is easy. Pharma and medtech firms have given up on their own research and instead acquire startups after regulatory permission. Many startups, especially in specialized areas, have this weakness.

That doesn't mean any lucrative $25M-plus business won't be acquired. In many businesses, the venture model requires a high exit premium.

A startup invents a new glue. 3M, BASF, Henkel, and others may buy them. Adding more adhesive to their catalogs won't boost commerce. They won't compete to buy the business. They'll only buy a startup at a profitable price. The acquisition price represents a moderate EBITDA multiple.

The company's $100M revenue presumably yields $10m in profits (assuming they’ve reached profitability at all). A $30M-$50M transaction is likely. Not terrible, but not what venture investors want after investing $25M to create a plant and develop the business.

Private equity buys profitable companies for a moderate profit multiple. It's a good exit for entrepreneurs, but not for investors seeking 10x or more what PE firms pay. If a startup offers private equity as an exit, the conversation is over.

Constructed for purchase

The startup wants a high-multiple exit. Unless the company targets $1B in revenue and does an IPO, exit means acquisition.

If they're constructing the business for acquisition or themselves, founders must decide.

If you want an indefinitely-running business, I applaud you. We need more long-term founders. Most successful organizations are founded around consumer demands, not venture capital's urge to grow fast and exit. Not venture funding.

if you don't match the venture model, what to do

VC funds moonshots. The 10% that succeed are extraordinary. Not every firm is a rocketship, and launching the wrong startup into space, even with money, will explode.

But just because your startup won't make $100M in 5 years doesn't mean it's a bad business. Most successful companies don't follow this model. It's not venture capital-friendly.

Although venture capital gets the most attention due to a few spectacular triumphs (and disasters), it's not the only or even most typical option to fund a firm.

Other ways to support your startup:

• Personal and family resources, such as credit cards, second mortgages, and lines of credit

• bootstrapping off of sales

• government funding and honors

• Private equity & project financing

• collaborating with a big business

• Including a business partner

Before pitching angels and VCs, be sure your startup qualifies. If so, include them in your pitch.

How to reframe irritation and go forward

“… before you can figure out where you are going, you need to know where you are, and once you know and accept where you are, you can design your way to where you want to be.” — Bill Burnett and Dave Evans

“You’ve been here before. But there are some new ingredients this time. What can tell yourself that will make you understand that now isn’t just like last year? That there’s something new in this August.”

My coach paused. I sighed, inhaled deeply, and considered her question.

What could I say? I simply needed a plan from her so everything would fall into place and I could be the happy, successful person I want to be.

Time passed. My mind was exhausted from running all morning, all summer, or the last five years, searching for what to do next and how to get there.

Calmer, I remembered that my coach's inquiry had benefited me throughout the summer. The month before our call, I read Designing Your Work Life — How to Thrive and Change and Find Happiness at Work from Standford University’s Bill Burnett and Dave Evans.

A passage in their book felt like a lifeline: “We have something important to say to you: Wherever you are in your work life, whatever job you are doing, it’s good enough. For now. Not forever. For now.”

As I remembered this book on the coaching call, I wondered if I could embrace where I am in August and say my job life is good enough for now. Only temporarily.

I've done that since. I'm getting unstuck.

Here's how you can take the first step in any area where you feel stuck.

How to acquire the perspective of "Good enough for now" for yourself

We’ve all heard the advice to just make the best of a bad situation. That´s not bad advice, but if you only make the best of a bad situation, you are still in a bad situation. It doesn’t get to the root of the problem or offer an opportunity to change the situation. You’re more cheerfully navigating lousiness, which is an improvement, but not much of one and rather hard to sustain over time.” — Bill Burnett and Dave Evans

Reframing Burnett at Evans says good enough for now is the key to being happier at work. Because, as they write, a designer always has options.

Choosing to believe things are good enough for now is liberating. It helps us feel less victimized and less judged. Accepting our situation helps us become unstuck.

Let's break down the process, which designers call constructing your way ahead, into steps you can take today.

Writing helps get started. First, write down your challenge and why it's essential to you. If pen and paper help, try this strategy:

• Make the decision to accept the circumstance as it is. Designers always begin by acknowledging the truth of the situation. You now refrain from passing judgment. Instead, you simply describe the situation as accurately as you can. This frees us from negative thought patterns that prevent us from seeing the big picture and instead keep us in a tunnel of negativity.

• Look for a reframing right now. Begin with good enough for the moment. Take note of how your body feels as a result. Tell yourself repeatedly that whatever is occurring is sufficient for the time being. Not always, but just now. If you want to, you can even put it in writing and repeatedly breathe it in, almost like a mantra.

• You can select a reframe that is more relevant to your situation once you've decided that you're good enough for now and have allowed yourself to believe it. Try to find another perspective that is possible, for instance, if you feel unappreciated at work and your perspective of I need to use and be recognized for all my new skills in my job is making you sad and making you want to resign. For instance, I can learn from others at work and occasionally put my new abilities to use.

• After that, leave your mind and act in accordance with your new perspective. Utilize the designer's bias for action to test something out and create a prototype that you can learn from. Your beginning point for creating experiences that will support the new viewpoint derived from the aforementioned point is the new perspective itself. By doing this, you recognize a circumstance at work where you can provide value to yourself or your workplace and then take appropriate action. Send two or three coworkers from whom you wish to learn anything an email, for instance, asking them to get together for coffee or a talk.

Choose tiny, doable actions. You prioritize them at work.

Let's assume you're feeling disconnected at work, so you make a list of folks you may visit each morning or invite to lunch. If you're feeling unmotivated and tired, take a daily walk and treat yourself to a decent coffee.

This may be plenty for now. If you want to take this procedure further, use Burnett and Evans' internet tools and frameworks.

Developing the daily practice of reframing

“We’re not discontented kids in the backseat of the family minivan, but how many of us live our lives, especially our work lives, as if we are?” — Bill Burnett and Dave Evans

I choose the good enough for me perspective every day, often. No quick fix. Am a failing? Maybe a little bit, but I like to think of it more as building muscle.

This way, every time I tell myself it's ok, I hear you. For now, that muscle gets stronger.

Hopefully, reframing will become so natural for us that it will become a habit, and not a technique anymore.

If you feel like you’re stuck in your career or at work, the reframe of Good enough, for now, might be valuable, so just go ahead and try it out right now.

And while you’re playing with this, why not think of other areas of your life too, like your relationships, where you live — even your writing, and see if you can feel a shift?

Most crypto is probably too.

The goals of Web 3 and the metaverse are admirable and attractive. Who doesn't want an internet owned by users? Who wouldn't want a digital realm where anything is possible? A better way to collaborate and visit pals.

Companies pursue profits endlessly. Infinite growth and revenue are expected, and if a corporation needs to sacrifice profits to safeguard users, the CEO, board of directors, and any executives will lose to the system of incentives that (1) retains workers with shares and (2) makes a company answerable to all of its shareholders. Only the government can guarantee user protections, but we know how successful that is. This is nothing new, just a problem with modern capitalism and tech platforms that a user-owned internet might remedy. Moxie, the founder of Signal, has a good articulation of some of these current Web 2 tech platform problems (but I forget the timestamp); thoughts on JRE aside, this episode is worth listening to (it’s about a bunch of other stuff too).

Moxie Marlinspike, founder of Signal, on the Joe Rogan Experience podcast.

Source: https://open.spotify.com/episode/2uVHiMqqJxy8iR2YB63aeP?si=4962b5ecb1854288

Web 3 champions are premature. There was so much spectacular growth during Web 2 that the next wave of founders want to make an even bigger impact, while investors old and new want a chance to get a piece of the moonshot action. Worse, crypto enthusiasts believe — and financially need — the fact of its success to be true, whether or not it is.

I’m doubtful that it will play out like current proponents say. Crypto has been the white-hot focus of SV’s best and brightest for a long time yet still struggles to come up any mainstream use case other than ‘buy, HODL, and believe’: a store of value for your financial goals and wishes. Some kind of the metaverse is likely, but will it be decentralized, mostly in VR, or will Meta (previously FB) play a big role? Unlikely.

METAVERSE

The metaverse exists already. Our digital lives span apps, platforms, and games. I can design a 3D house, invite people, use Discord, and hang around in an artificial environment. Millions of gamers do this in Rust, Minecraft, Valheim, and Animal Crossing, among other games. Discord's voice chat and Slack-like servers/channels are the present social anchor, but the interface, integrations, and data portability will improve. Soon you can stream YouTube videos on digital house walls. You can doodle, create art, play Jackbox, and walk through a door to play Apex Legends, Fortnite, etc. Not just gaming. Digital whiteboards and screen sharing enable real-time collaboration. They’ll review code and operate enterprises. Music is played and made. In digital living rooms, they'll watch movies, sports, comedy, and Twitch. They'll tweet, laugh, learn, and shittalk.

The metaverse is the evolution of our digital life at home, the third place. The closest analog would be Discord and the integration of Facebook, Slack, YouTube, etc. into a single, 3D, customizable hangout space.

I'm not certain this experience can be hugely decentralized and smoothly choreographed, managed, and run, or that VR — a luxury, cumbersome, and questionably relevant technology — must be part of it. Eventually, VR will be pragmatic, achievable, and superior to real life in many ways. A total sensory experience like the Matrix or Sword Art Online, where we're physically hooked into the Internet yet in our imaginations we're jumping, flying, and achieving athletic feats we never could in reality; exploring realms far grander than our own (as grand as it is). That VR is different from today's.

https://podcasts.google.com/feed/aHR0cHM6Ly9leHBvbmVudC5mbS9mZWVkLw/episode/aHR0cHM6Ly9leHBvbmVudC5mbS8_cD00MzM?hl=en&ved=2ahUKEwjH5u6r4rv2AhUjc98KHeybAP8QjrkEegQIChAF&ep=6

Ben Thompson released an episode of Exponent after Facebook changed its name to Meta. Ben was suspicious about many metaverse champion claims, but he made a good analogy between Oculus and the PC. The PC was initially far too pricey for the ordinary family to afford. It began as a business tool. It got so powerful and pervasive that it affected our personal life. Price continues to plummet and so much consumer software was produced that it's impossible to envision life without a home computer (or in our pockets). If Facebook shows product market fit with VR in business, through use cases like remote work and collaboration, maybe VR will become practical in our personal lives at home.

Before PCs, we relied on Blockbuster, the Yellow Pages, cabs to get to the airport, handwritten taxes, landline phones to schedule social events, and other archaic methods. It is impossible for me to conceive what VR, in the form of headsets and hand controllers, stands to give both professional and especially personal digital experiences that is an order of magnitude better than what we have today. Is looking around better than using a mouse to examine a 3D landscape? Do the hand controls make x10 or x100 work or gaming more fun or efficient? Will VR replace scalable Web 2 methods and applications like Web 1 and Web 2 did for analog? I don't know.

My guess is that the metaverse will arrive slowly, initially on displays we presently use, with more app interoperability. I doubt that it will be controlled by the people or by Facebook, a corporation that struggles to properly innovate internally, as practically every large digital company does. Large tech organizations are lousy at hiring product-savvy employees, and if they do, they rarely let them explore new things.

These companies act like business schools when they seek founders' results, with bureaucracy and dependency. Which company launched the last popular consumer software product that wasn't a clone or acquisition? Recent examples are scarce.

Web 3

Investors and entrepreneurs of Web 3 firms are declaring victory: 'Web 3 is here!' Web 3 is the future! Many profitable Web 2 enterprises existed when Web 2 was defined. The word was created to explain user behavior shifts, not a personal pipe dream.

Origins of Web 2: http://www.oreilly.com/pub/a/web2/archive/what-is-web-20.html

One of these Web 3 startups may provide the connecting tissue to link all these experiences or become one of the major new digital locations. Even so, successful players will likely use centralized power arrangements, as Web 2 businesses do now. Some Web 2 startups integrated our digital lives. Rockmelt (2010–2013) was a customizable browser with bespoke connectors to every program a user wanted; imagine seeing Facebook, Twitter, Discord, Netflix, YouTube, etc. all in one location. Failure. Who knows what Opera's doing?

Silicon Valley and tech Twitter in general have a history of jumping on dumb bandwagons that go nowhere. Dot-com crash in 2000? The huge deployment of capital into bad ideas and businesses is well-documented. And live video. It was the future until it became a niche sector for gamers. Live audio will play out a similar reality as CEOs with little comprehension of audio and no awareness of lasting new user behavior deceive each other into making more and bigger investments on fool's gold. Twitter trying to buy Clubhouse for $4B, Spotify buying Greenroom, Facebook exploring live audio and 'Tiktok for audio,' and now Amazon developing a live audio platform. This live audio frenzy won't be worth their time or energy. Blind guides blind. Instead of learning from prior failures like Twitter buying Periscope for $100M pre-launch and pre-product market fit, they're betting on unproven and uncompelling experiences.

NFTs

NFTs are also nonsense. Take Loot, a time-limited bag drop of "things" (text on the blockchain) for a game that didn't exist, bought by rich techies too busy to play video games and foolish enough to think they're getting in early on something with a big reward. What gaming studio is incentivized to use these items? Who's encouraged to join? No one cares besides Loot owners who don't have NFTs. Skill, merit, and effort should be rewarded with rare things for gamers. Even if a small minority of gamers can make a living playing, the average game's major appeal has never been to make actual money - that's a profession.

No game stays popular forever, so how is this objective sustainable? Once popularity and usage drop, exclusive crypto or NFTs will fall. And if NFTs are designed to have cross-game appeal, incentives apart, 30 years from now any new game will need millions of pre-existing objects to build around before they start. It doesn’t work.

Many games already feature item economies based on real in-game scarcity, generally for cosmetic things to avoid pay-to-win, which undermines scaled gaming incentives for huge player bases. Counter-Strike, Rust, etc. may be bought and sold on Steam with real money. Since the 1990s, unofficial cross-game marketplaces have sold in-game objects and currencies. NFTs aren't needed. Making a popular, enjoyable, durable game is already difficult.

With NFTs, certain JPEGs on the internet went from useless to selling for $69 million. Why? Crypto, Web 3, early Internet collectibles. NFTs are digital Beanie Babies (unlike NFTs, Beanie Babies were a popular children's toy; their destinies are the same). NFTs are worthless and scarce. They appeal to crypto enthusiasts seeking for a practical use case to support their theory and boost their own fortune. They also attract to SV insiders desperate not to miss the next big thing, not knowing what it will be. NFTs aren't about paying artists and creators who don't get credit for their work.

South Park's Underpants Gnomes

NFTs are a benign, foolish plan to earn money on par with South Park's underpants gnomes. At worst, they're the world of hucksterism and poor performers. Or those with money and enormous followings who, like everyone, don't completely grasp cryptocurrencies but are motivated by greed and status and believe Gary Vee's claim that CryptoPunks are the next Facebook. Gary's watertight logic: if NFT prices dip, they're on the same path as the most successful corporation in human history; buy the dip! NFTs aren't businesses or museum-worthy art. They're bs.

Gary Vee compares NFTs to Amazon.com. vm.tiktok.com/TTPdA9TyH2

We grew up collecting: Magic: The Gathering (MTG) cards printed in the 90s are now worth over $30,000. Imagine buying a digital Magic card with no underlying foundation. No one plays the game because it doesn't exist. An NFT is a contextless image someone conned you into buying a certificate for, but anyone may copy, paste, and use. Replace MTG with Pokemon for younger readers.

When Gary Vee strongarms 30 tech billionaires and YouTube influencers into buying CryptoPunks, they'll talk about it on Twitch, YouTube, podcasts, Twitter, etc. That will convince average folks that the product has value. These guys are smart and/or rich, so I'll get in early like them. Cryptography is similar. No solid, scaled, mainstream use case exists, and no one knows where it's headed, but since the global crypto financial bubble hasn't burst and many people have made insane fortunes, regular people are putting real money into something that is highly speculative and could be nothing because they want a piece of the action. Who doesn’t want free money? Rich techies and influencers won't be affected; normal folks will.

Imagine removing every $1 invested in Bitcoin instantly. What would happen? How far would Bitcoin fall? Over 90%, maybe even 95%, and Bitcoin would be dead. Bitcoin as an investment is the only scalable widespread use case: it's confidence that a better use case will arise and that being early pays handsomely. It's like pouring a trillion dollars into a company with no business strategy or users and a CEO who makes vague future references.

New tech and efforts may provoke a 'get off my lawn' mentality as you approach 40, but I've always prided myself on having a decent bullshit detector, and it's flying off the handle at this foolishness. If we can accomplish a functional, responsible, equitable, and ethical user-owned internet, I'm for it.

Postscript:

I wanted to summarize my opinions because I've been angry about this for a while but just sporadically tweeted about it. A friend handed me a Dan Olson YouTube video just before publication. He's more knowledgeable, articulate, and convincing about crypto. It's worth seeing:

This post is a summary. See the original one 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.