More on Entrepreneurship/Creators
Eitan Levy
3 years ago
The Top 8 Growth Hacking Techniques for Startups
The Top 8 Growth Hacking Techniques for Startups
These startups, and how they used growth-hack marketing to flourish, are some of the more ethical ones, while others are less so.
Before the 1970 World Cup began, Puma paid footballer Pele $120,000 to tie his shoes. The cameras naturally focused on Pele and his Pumas, causing people to realize that Puma was the top football brand in the world.
Early workers of Uber canceled over 5,000 taxi orders made on competing applications in an effort to financially hurt any of their rivals.
PayPal developed a bot that advertised cheap goods on eBay, purchased them, and paid for them with PayPal, fooling eBay into believing that customers preferred this payment option. Naturally, Paypal became eBay's primary method of payment.
Anyone renting a space on Craigslist had their emails collected by AirBnB, who then urged them to use their service instead. A one-click interface was also created to list immediately on AirBnB from Craigslist.
To entice potential single people looking for love, Tinder developed hundreds of bogus accounts of attractive people. Additionally, for at least a year, users were "accidentally" linked.
Reddit initially created a huge number of phony accounts and forced them all to communicate with one another. It eventually attracted actual users—the real meaning of "fake it 'til you make it"! Additionally, this gave Reddit control over the tone of voice they wanted for their site, which is still present today.
To disrupt the conferences of their main rival, Salesforce recruited fictitious protestors. The founder then took over all of the event's taxis and gave a 45-minute pitch for his startup. No place to hide!
When a wholesaler required a minimum purchase of 10, Amazon CEO Jeff Bezos wanted a way to purchase only one book from them. A wholesaler would deliver the one book he ordered along with an apology for the other eight books after he discovered a loophole and bought the one book before ordering nine books about lichens. On Amazon, he increased this across all of the users.
Original post available here
Davlin Knight
2 years ago
2 pitfalls to stay away from when launching a YouTube channel
You do not want to miss these
Stop! Stop it! Two things to avoid when starting a YouTube channel. Critical. Possible channel-killers Its future revenue.
I'll tell you now, so don't say "I wish I knew."
The Notorious Copyright Allegation
My YouTube channel received a copyright claim before I sold it. This claim was on a one-minute video I thought I'd changed enough to make mine, but the original owner disagreed.
It cost me thousands in ad revenue. Original owner got the profits.
Well, it wasn't your video, you say.
Touché.
I've learned. Sorta
I couldn't stop looking at the video's views. The video got 1,000,000 views without any revenue. I made 4 more similar videos.
If they didn't get copyrighted, I'd be rolling in dough.
You've spent a week editing and are uploading to YouTube. You're thrilled as you stand and stretch your back. You see the video just before publishing.
No way!
The red exclamation point on checks.
Copyright claim!
YouTube lets you publish, but you won't make money.
Sounds fair? Well, it is.
Copyright claims mean you stole someone's work. Song, image, or video clip.
We wouldn't want our content used for money.
The only problem with this is that almost everything belongs to someone else. I doubt some of the biggest creators are sitting down and making their music for their videos. That just seems really excessive when you could make a quick search on YouTube and download a song (I definitely don’t do this because that would be stealing).
So how do you defeat a copyright defense?
Even copyright-free songs on YouTube aren't guaranteed. Some copyrighted songs claim to be free.
Use YouTube's free music library or pay for a subscription to adobe stock, epidemic sound, or artlist.io.
Most of my videos have Nintendo music. Almost all game soundtracks are copyright-free and offer a variety of songs.
Restriction on age
Age restrictions are a must-avoid. A channel dies.
YouTube never suggests age-restricted videos.
Shadow banning means YouTube hides your content from subscribers and non-subscribers.
Keeping your channel family-friendly can help.
I hear you complaining that your channel isn't for kids. I agree. Not everyone has a clean mouth or creates content for minors.
YouTube has changed rapidly in recent years. Focusing on kids. Fewer big creators are using profanity or explicit content in videos. Not YouTube-worthy.
Youtube wants to be family-friendly. A family-friendly movie. It won't promote illegal content. Yes, it allows profanity.
Do I recommend avoiding no-no words in videos? Never. Okay. YouTube's policies are shaky. YouTube uses video content to determine ad suitability.
No joke. If you're serious about becoming a content creator, avoid profanity and inappropriate topics.
If your channel covers 18+ topics, like crime or commentary, censor as much as possible.
YouTube can be like walking on eggshells. You never know what is gonna upset the boss. So play it safe and try to avoid getting on their bad side.
Mr. Beast, Dream, Markplier, Faze Rug, and PewDewPie are popular creators. They maintain it family-friendly while entertaining fans.
You got this.
Aure's Notes
2 years ago
I met a man who in just 18 months scaled his startup to $100 million.
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.
He primarily works in sales.
Coding the product was done by his co-founder.
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:
His vision. He possessed a certain amount of fire.
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.
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.
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Merve Yılmaz
3 years ago
Dopamine detox
This post is for you if you can't read or study for 5 minutes.
If you clicked this post, you may be experiencing problems focusing on tasks. A few minutes of reading may tire you. Easily distracted? Using social media and video games for hours without being sidetracked may impair your dopamine system.
When we achieve a goal, the brain secretes dopamine. It might be as simple as drinking water or as crucial as college admission. Situations vary. Various events require different amounts.
Dopamine is released when we start learning but declines over time. Social media algorithms provide new material continually, making us happy. Social media use slows down the system. We can't continue without an award. We return to social media and dopamine rewards.
Mice were given a button that released dopamine into their brains to study the hormone. The mice lost their hunger, thirst, and libido and kept pressing the button. Think this is like someone who spends all day gaming or on Instagram?
When we cause our brain to release so much dopamine, the brain tries to balance it in 2 ways:
1- Decreases dopamine production
2- Dopamine cannot reach its target.
Too many quick joys aren't enough. We'll want more joys. Drugs and alcohol are similar. Initially, a beer will get you drunk. After a while, 3-4 beers will get you drunk.
Social media is continually changing. Updates to these platforms keep us interested. When social media conditions us, we can't read a book.
Same here. I used to complete a book in a day and work longer without distraction. Now I'm addicted to Instagram. Daily, I spend 2 hours on social media. This must change. My life needs improvement. So I started the 50-day challenge.
I've compiled three dopamine-related methods.
Recommendations:
Day-long dopamine detox
First, take a day off from all your favorite things. Social media, gaming, music, junk food, fast food, smoking, alcohol, friends. Take a break.
Hanging out with friends or listening to music may seem pointless. Our minds are polluted. One day away from our pleasures can refresh us.
2. One-week dopamine detox by selecting
Choose one or more things to avoid. Social media, gaming, music, junk food, fast food, smoking, alcohol, friends. Try a week without Instagram or Twitter. I use this occasionally.
One week all together
One solid detox week. It's the hardest program. First or second options are best for dopamine detox. Time will help you.
You can walk, read, or pray during a dopamine detox. Many options exist. If you want to succeed, you must avoid instant gratification. Success after hard work is priceless.
Vitalik
3 years ago
An approximate introduction to how zk-SNARKs are possible (part 1)
You can make a proof for the statement "I know a secret number such that if you take the word ‘cow', add the number to the end, and SHA256 hash it 100 million times, the output starts with 0x57d00485aa". The verifier can verify the proof far more quickly than it would take for them to run 100 million hashes themselves, and the proof would also not reveal what the secret number is.
In the context of blockchains, this has 2 very powerful applications: Perhaps the most powerful cryptographic technology to come out of the last decade is general-purpose succinct zero knowledge proofs, usually called zk-SNARKs ("zero knowledge succinct arguments of knowledge"). A zk-SNARK allows you to generate a proof that some computation has some particular output, in such a way that the proof can be verified extremely quickly even if the underlying computation takes a very long time to run. The "ZK" part adds an additional feature: the proof can keep some of the inputs to the computation hidden.
You can make a proof for the statement "I know a secret number such that if you take the word ‘cow', add the number to the end, and SHA256 hash it 100 million times, the output starts with 0x57d00485aa". The verifier can verify the proof far more quickly than it would take for them to run 100 million hashes themselves, and the proof would also not reveal what the secret number is.
In the context of blockchains, this has two very powerful applications:
- Scalability: if a block takes a long time to verify, one person can verify it and generate a proof, and everyone else can just quickly verify the proof instead
- Privacy: you can prove that you have the right to transfer some asset (you received it, and you didn't already transfer it) without revealing the link to which asset you received. This ensures security without unduly leaking information about who is transacting with whom to the public.
But zk-SNARKs are quite complex; indeed, as recently as in 2014-17 they were still frequently called "moon math". The good news is that since then, the protocols have become simpler and our understanding of them has become much better. This post will try to explain how ZK-SNARKs work, in a way that should be understandable to someone with a medium level of understanding of mathematics.
Why ZK-SNARKs "should" be hard
Let us take the example that we started with: we have a number (we can encode "cow" followed by the secret input as an integer), we take the SHA256 hash of that number, then we do that again another 99,999,999 times, we get the output, and we check what its starting digits are. This is a huge computation.
A "succinct" proof is one where both the size of the proof and the time required to verify it grow much more slowly than the computation to be verified. If we want a "succinct" proof, we cannot require the verifier to do some work per round of hashing (because then the verification time would be proportional to the computation). Instead, the verifier must somehow check the whole computation without peeking into each individual piece of the computation.
One natural technique is random sampling: how about we just have the verifier peek into the computation in 500 different places, check that those parts are correct, and if all 500 checks pass then assume that the rest of the computation must with high probability be fine, too?
Such a procedure could even be turned into a non-interactive proof using the Fiat-Shamir heuristic: the prover computes a Merkle root of the computation, uses the Merkle root to pseudorandomly choose 500 indices, and provides the 500 corresponding Merkle branches of the data. The key idea is that the prover does not know which branches they will need to reveal until they have already "committed to" the data. If a malicious prover tries to fudge the data after learning which indices are going to be checked, that would change the Merkle root, which would result in a new set of random indices, which would require fudging the data again... trapping the malicious prover in an endless cycle.
But unfortunately there is a fatal flaw in naively applying random sampling to spot-check a computation in this way: computation is inherently fragile. If a malicious prover flips one bit somewhere in the middle of a computation, they can make it give a completely different result, and a random sampling verifier would almost never find out.
It only takes one deliberately inserted error, that a random check would almost never catch, to make a computation give a completely incorrect result.
If tasked with the problem of coming up with a zk-SNARK protocol, many people would make their way to this point and then get stuck and give up. How can a verifier possibly check every single piece of the computation, without looking at each piece of the computation individually? There is a clever solution.
see part 2
Enrique Dans
2 years ago
What happens when those without morals enter the economic world?
I apologize if this sounds basic, but throughout my career, I've always been clear that a company's activities are shaped by its founder(s)' morality.
I consider Palantir, owned by PayPal founder Peter Thiel, evil. He got $5 billion tax-free by hacking a statute to help middle-class savings. That may appear clever, but I think it demonstrates a shocking lack of solidarity with society. As a result of this and other things he has said and done, I early on dismissed Peter Thiel as someone who could contribute anything positive to society, and events soon proved me right: we are talking about someone who clearly considers himself above everyone else and who does not hesitate to set up a company, Palantir, to exploit the data of the little people and sell it to the highest bidder, whoever that is and whatever the consequences.
The German courts have confirmed my warnings concerning Palantir. The problem is that politicians love its surveillance tools because they think knowing more about their constituents gives them power. These are ideal for dictatorships who want to snoop on their populace. Hence, Silicon Valley's triumphalist dialectic has seduced many governments at many levels and collected massive volumes of data to hold forever.
Dangerous company. There are many more. My analysis of the moral principles that disclose company management changed my opinion of Facebook, now Meta, and anyone with a modicum of interest might deduce when that happened, a discovery that leaves you dumbfounded. TikTok was easy because its lack of morality was revealed early when I saw the videos it encouraged minors to post and the repercussions of sharing them through its content recommendation algorithm. When you see something like this, nothing can convince you that the firm can change its morals and become good. Nothing. You know the company is awful and will fail. Speak it, announce it, and change it. It's like a fingerprint—unchangeable.
Some of you who read me frequently make its Facebook today jokes when I write about these firms, and that's fine: they're my moral standards, those of an elderly professor with thirty-five years of experience studying corporations and discussing their cases in class, but you don't have to share them. Since I'm writing this and don't have to submit to any editorial review, that's what it is: when you continuously read a person, you have to assume that they have moral standards and that sometimes you'll agree with them and sometimes you won't. Morality accepts hierarchies, nuances, and even obsessions. I know not everyone shares my opinions, but at least I can voice them. One day, one of those firms may sue me (as record companies did some years ago).
Palantir is incredibly harmful. Limit its operations. Like Meta and TikTok, its business strategy is shaped by its founders' immorality. Such a procedure can never be beneficial.