More on Entrepreneurship/Creators

Jayden Levitt
2 years ago
Billionaire who was disgraced lost his wealth more quickly than anyone in history
If you're not genuine, you'll be revealed.
Sam Bankman-Fried (SBF) was called the Cryptocurrency Warren Buffet.
No wonder.
SBF's trading expertise, Blockchain knowledge, and ability to construct FTX attracted mainstream investors.
He had a fantastic worldview, donating much of his riches to charity.
As the onion layers peel back, it's clear he wasn't the altruistic media figure he portrayed.
SBF's mistakes were disastrous.
Customer deposits were traded and borrowed by him.
With ten other employees, he shared a $40 million mansion where they all had polyamorous relationships.
Tone-deaf and wasteful marketing expenditures, such as the $200 million spent to change the name of the Miami Heat stadium to the FTX Arena
Democrats received a $40 million campaign gift.
And now there seems to be no regret.
FTX was a 32-billion-dollar cryptocurrency exchange.
It went bankrupt practically overnight.
SBF, FTX's creator, exploited client funds to leverage trade.
FTX had $1 billion in customer withdrawal reserves against $9 billion in liabilities in sister business Alameda Research.
Bloomberg Billionaire Index says it's the largest and fastest net worth loss in history.
It gets worse.
SBF's net worth is $900 Million, however he must still finalize FTX's bankruptcy.
SBF's arrest in the Bahamas and SEC inquiry followed news that his cryptocurrency exchange had crashed, losing billions in customer deposits.
A journalist contacted him on Twitter D.M., and their exchange is telling.
His ideas are revealed.
Kelsey Piper says they didn't expect him to answer because people under investigation don't comment.
Bankman-Fried wanted to communicate, and the interaction shows he has little remorse.
SBF talks honestly about FTX gaming customers' money and insults his competition.
Reporter Kelsey Piper was outraged by what he said and felt the mistakes SBF says plague him didn't evident in the messages.
Before FTX's crash, SBF was a poster child for Cryptocurrency regulation and avoided criticizing U.S. regulators.
He tells Piper that his lobbying is just excellent PR.
It shows his genuine views and supports cynics' opinions that his attempts to win over U.S. authorities were good for his image rather than Crypto.
SBF’s responses are in Grey, and Pipers are in Blue.
It's unclear if SBF cut corners for his gain. In their Twitter exchange, Piper revisits an interview question about ethics.
SBF says, "All the foolish sh*t I said"
SBF claims FTX has never invested customer monies.
Piper challenged him on Twitter.
While he insisted FTX didn't use customer deposits, he said sibling business Alameda borrowed too much from FTX's balance sheet.
He did, basically.
When consumers tried to withdraw money, FTX was short.
SBF thought Alameda had enough money to cover FTX customers' withdrawals, but life sneaks up on you.
SBF believes most exchanges have done something similar to FTX, but they haven't had a bank run (a bunch of people all wanting to get their deposits out at the same time).
SBF believes he shouldn't have consented to the bankruptcy and kept attempting to raise more money because withdrawals would be open in a month with clients whole.
If additional money came in, he needed $8 billion to bridge the creditors' deficit, and there aren't many corporations with $8 billion to spare.
Once clients feel protected, they will continue to leave their assets on the exchange, according to one idea.
Kevin OLeary, a world-renowned hedge fund manager, says not all investors will walk through the open gate once the company is safe, therefore the $8 Billion wasn't needed immediately.
SBF claims the bankruptcy was his biggest error because he could have accumulated more capital.
Final Reflections
Sam Bankman-Fried, 30, became the world's youngest billionaire in four years.
Never listen to what people say about investing; watch what they do.
SBF is a trader who gets wrecked occasionally.
Ten first-time entrepreneurs ran FTX, screwing each other with no risk management.
It prevents opposing or challenging perspectives and echo chamber highs.
Twitter D.M. conversation with a journalist is the final nail.
He lacks an experienced crew.
This event will surely speed up much-needed regulation.
It's also prompted cryptocurrency exchanges to offer proof of reserves to calm customers.

Aaron Dinin, PhD
2 years ago
The Advantages and Disadvantages of Having Investors Sign Your NDA
Startup entrepreneurs assume what risks when pitching?
Last week I signed four NDAs.
Four!
NDA stands for non-disclosure agreement. A legal document given to someone receiving confidential information. By signing, the person pledges not to share the information for a certain time. If they do, they may be in breach of contract and face legal action.
Companies use NDAs to protect trade secrets and confidential internal information from employees and contractors. Appropriate. If you manage a huge, successful firm, you don't want your employees selling their information to your competitors. To be true, business NDAs don't always prevent corporate espionage, but they usually make employees and contractors think twice before sharing.
I understand employee and contractor NDAs, but I wasn't asked to sign one. I counsel entrepreneurs, thus the NDAs I signed last week were from startups that wanted my feedback on their concepts.
I’m not a startup investor. I give startup guidance online. Despite that, four entrepreneurs thought their company ideas were so important they wanted me to sign a generically written legal form they probably acquired from a shady, spam-filled legal templates website before we could chat.
False. One company tried to get me to sign their NDA a few days after our conversation. I gently rejected, but their tenacity encouraged me. I considered sending retroactive NDAs to everyone I've ever talked to about one of my startups in case they establish a successful company based on something I said.
Two of the other three NDAs were from nearly identical companies. Good thing I didn't sign an NDA for the first one, else they may have sued me for talking to the second one as though I control the firms people pitch me.
I wasn't talking to the fourth NDA company. Instead, I received an unsolicited email from someone who wanted comments on their fundraising pitch deck but required me to sign an NDA before sending it.
That's right, before I could read a random Internet stranger's unsolicited pitch deck, I had to sign his NDA, potentially limiting my ability to discuss what was in it.
You should understand. Advisors, mentors, investors, etc. talk to hundreds of businesses each year. They cannot manage all the companies they deal with, thus they cannot risk legal trouble by talking to someone. Well, if I signed NDAs for all the startups I spoke with, half of the 300+ articles I've written on Medium over the past several years could get me sued into the next century because I've undoubtedly addressed topics in my articles that I discussed with them.
The four NDAs I received last week are part of a recent trend of entrepreneurs sending out NDAs before meetings, despite the practical and legal issues. They act like asking someone to sign away their right to talk about all they see and hear in a day is as straightforward as asking for a glass of water.
Given this inflow of NDAs, I wanted to briefly remind entrepreneurs reading this blog about the merits and cons of requesting investors (or others in the startup ecosystem) to sign your NDA.
Benefits of having investors sign your NDA include:
None. Zero. Nothing.
Disadvantages of requesting investor NDAs:
You'll come off as an amateur who has no idea what it takes to launch a successful firm.
Investors won't trust you with their money since you appear to be a complete amateur.
Printing NDAs will be a waste of paper because no genuine entrepreneur will ever sign one.
I apologize for missing any cons. Please leave your remarks.

SAHIL SAPRU
3 years ago
How I grew my business to a $5 million annual recurring revenue
Scaling your startup requires answering customer demands, not growth tricks.
I cofounded Freedo Rentals in 2019. I reached 50 lakh+ ARR in 6 months before quitting owing to the epidemic.
Freedo aimed to solve 2 customer pain points:
Users lacked a reliable last-mile transportation option.
The amount that Auto walas charge for unmetered services
Solution?
Effectively simple.
Build ports at high-demand spots (colleges, residential societies, metros). Electric ride-sharing can meet demand.
We had many problems scaling. I'll explain using the AARRR model.
Brand unfamiliarity or a novel product offering were the problems with awareness. Nobody knew what Freedo was or what it did.
Problem with awareness: Content and advertisements did a poor job of communicating the task at hand. The advertisements clashed with the white-collar part because they were too cheesy.
Retention Issue: We encountered issues, indicating that the product was insufficient. Problems with keyless entry, creating bills, stealing helmets, etc.
Retention/Revenue Issue: Costly compared to established rivals. Shared cars were 1/3 of our cost.
Referral Issue: Missing the opportunity to seize the AHA moment. After the ride, nobody remembered us.
Once you know where you're struggling with AARRR, iterative solutions are usually best.
Once you have nailed the AARRR model, most startups use paid channels to scale. This dependence, on paid channels, increases with scale unless you crack your organic/inbound game.
Over-index growth loops. Growth loops increase inflow and customers as you scale.
When considering growth, ask yourself:
Who is the solution's ICP (Ideal Customer Profile)? (To whom are you selling)
What are the most important messages I should convey to customers? (This is an A/B test.)
Which marketing channels ought I prioritize? (Conduct analysis based on the startup's maturity/stage.)
Choose the important metrics to monitor for your AARRR funnel (not all metrics are equal)
Identify the Flywheel effect's growth loops (inertia matters)
My biggest mistakes:
not paying attention to consumer comments or satisfaction. It is the main cause of problems with referrals, retention, and acquisition for startups. Beyond your NPS, you should consider second-order consequences.
The tasks at hand should be quite clear.
Here's my scaling equation:
Growth = A x B x C
A = Funnel top (Traffic)
B = Product Valuation (Solving a real pain point)
C = Aha! (Emotional response)
Freedo's A, B, and C created a unique offering.
Freedo’s ABC:
A — Working or Studying population in NCR
B — Electric Vehicles provide last-mile mobility as a clean and affordable solution
C — One click booking with a no-noise scooter
Final outcome:
FWe scaled Freedo to Rs. 50 lakh MRR and were growing 60% month on month till the pandemic ceased our growth story.
How we did it?
We tried ambassadors and coupons. WhatsApp was our most successful A/B test.
We grew widespread adoption through college and society WhatsApp groups. We requested users for referrals in community groups.
What worked for us won't work for others. This scale underwent many revisions.
Every firm is different, thus you must know your customers. Needs to determine which channel to prioritize and when.
Users desired a safe, time-bound means to get there.
This (not mine) growth framework helped me a lot. You should follow suit.
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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

Amelie Carver
3 years ago
Web3 Needs More Writers to Educate Us About It
WRITE FOR THE WEB3
Why web3’s messaging is lost and how crypto winter is growing growth seeds
People interested in crypto, blockchain, and web3 typically read Bitcoin and Ethereum's white papers. It's a good idea. Documents produced for developers and academia aren't always the ideal resource for beginners.
Given the surge of extremely technical material and the number of fly-by-nights, rug pulls, and other scams, it's little wonder mainstream audiences regard the blockchain sector as an expensive sideshow act.
What's the solution?
Web3 needs more than just builders.
After joining TikTok, I followed Amy Suto of SutoScience. Amy switched from TV scriptwriting to IT copywriting years ago. She concentrates on web3 now. Decentralized autonomous organizations (DAOs) are seeking skilled copywriters for web3.
Amy has found that web3's basics are easy to grasp; you don't need technical knowledge. There's a paradigm shift in knowing the basics; be persistent and patient.
Apple is positioning itself as a data privacy advocate, leveraging web3's zero-trust ethos on data ownership.
Finn Lobsien, who writes about web3 copywriting for the Mirror and Twitter, agrees: acronyms and abstractions won't do.
Web3 preached to the choir. Curious newcomers have only found whitepapers and scams when trying to learn why the community loves it. No wonder people resist education and buy-in.
Due to the gender gap in crypto (Crypto Bro is not just a stereotype), it attracts people singing to the choir or trying to cash in on the next big thing.
Last year, the industry was booming, so writing wasn't necessary. Now that the bear market has returned (for everyone, but especially web3), holding readers' attention is a valuable skill.
White papers and the Web3
Why does web3 rely so much on non-growth content?
Businesses must polish and improve their messaging moving into the 2022 recession. The 2021 tech boom provided such a sense of affluence and (unsustainable) growth that no one needed great marketing material. The market found them.
This was especially true for web3 and the first-time crypto believers. Obviously. If they knew which was good.
White papers help. White papers are highly technical texts that walk a reader through a product's details. How Does a White Paper Help Your Business and That White Paper Guy discuss them.
They're meant for knowledgeable readers. Investors and the technical (academic/developer) community read web3 white papers. White papers are used when a product is extremely technical or difficult to assist an informed reader to a conclusion. Web3 uses them most often for ICOs (initial coin offerings).
White papers for web3 education help newcomers learn about the web3 industry's components. It's like sending a first-grader to the Annotated Oxford English Dictionary to learn to read. It's a reference, not a learning tool, for words.
Newcomers can use platforms that teach the basics. These included Coinbase's Crypto Basics tutorials or Cryptochicks Academy, founded by the mother of Ethereum's inventor to get more women utilizing and working in crypto.
Discord and Web3 communities
Discord communities are web3's opposite. Discord communities involve personal communications and group involvement.
Online audience growth begins with community building. User personas prefer 1000 dedicated admirers over 1 million lukewarm followers, and the language is much more easygoing. Discord groups are renowned for phishing scams, compromised wallets, and incorrect information, especially since the crypto crisis.
White papers and Discord increase industry insularity. White papers are complicated, and Discord has a high risk threshold.
Web3 and writing ads
Copywriting is emotional, but white papers are logical. It uses the brain's quick-decision centers. It's meant to make the reader invest immediately.
Not bad. People think sales are sleazy, but they can spot the poor things.
Ethical copywriting helps you reach the correct audience. People who gain a following on Medium are likely to have copywriting training and a readership (or three) in mind when they publish. Tim Denning and Sinem Günel know how to identify a target audience and make them want to learn more.
In a fast-moving market, copywriting is less about long-form content like sales pages or blogs, but many organizations do. Instead, the copy is concise, individualized, and high-value. Tweets, email marketing, and IM apps (Discord, Telegram, Slack to a lesser extent) keep engagement high.
What does web3's messaging lack? As DAOs add stricter copyrighting, narrative and connecting tales seem to be missing.
Web3 is passionate about constructing the next internet. Now, they can connect their passion to a specific audience so newcomers understand why.

Farhad Malik
3 years ago
How This Python Script Makes Me Money Every Day
Starting a passive income stream with data science and programming
My website is fresh. But how do I monetize it?
Creating a passive-income website is difficult. Advertise first. But what useful are ads without traffic?
Let’s Generate Traffic And Put Our Programming Skills To Use
SEO boosts traffic (Search Engine Optimisation). Traffic generation is complex. Keywords matter more than text, URL, photos, etc.
My Python skills helped here. I wanted to find relevant, Google-trending keywords (tags) for my topic.
First The Code
I wrote the script below here.
import re
from string import punctuation
import nltk
from nltk import TreebankWordTokenizer, sent_tokenize
from nltk.corpus import stopwords
class KeywordsGenerator:
def __init__(self, pytrends):
self._pytrends = pytrends
def generate_tags(self, file_path, top_words=30):
file_text = self._get_file_contents(file_path)
clean_text = self._remove_noise(file_text)
top_words = self._get_top_words(clean_text, top_words)
suggestions = []
for top_word in top_words:
suggestions.extend(self.get_suggestions(top_word))
suggestions.extend(top_words)
tags = self._clean_tokens(suggestions)
return ",".join(list(set(tags)))
def _remove_noise(self, text):
#1. Convert Text To Lowercase and remove numbers
lower_case_text = str.lower(text)
just_text = re.sub(r'\d+', '', lower_case_text)
#2. Tokenise Paragraphs To words
list = sent_tokenize(just_text)
tokenizer = TreebankWordTokenizer()
tokens = tokenizer.tokenize(just_text)
#3. Clean text
clean = self._clean_tokens(tokens)
return clean
def _clean_tokens(self, tokens):
clean_words = [w for w in tokens if w not in punctuation]
stopwords_to_remove = stopwords.words('english')
clean = [w for w in clean_words if w not in stopwords_to_remove and not w.isnumeric()]
return clean
def get_suggestions(self, keyword):
print(f'Searching pytrends for {keyword}')
result = []
self._pytrends.build_payload([keyword], cat=0, timeframe='today 12-m')
data = self._pytrends.related_queries()[keyword]['top']
if data is None or data.values is None:
return result
result.extend([x[0] for x in data.values.tolist()][:2])
return result
def _get_file_contents(self, file_path):
return open(file_path, "r", encoding='utf-8',errors='ignore').read()
def _get_top_words(self, words, top):
counts = dict()
for word in words:
if word in counts:
counts[word] += 1
else:
counts[word] = 1
return list({k: v for k, v in sorted(counts.items(), key=lambda item: item[1])}.keys())[:top]
if __name__ == "1__main__":
from pytrends.request import TrendReq
nltk.download('punkt')
nltk.download('stopwords')
pytrends = TrendReq(hl='en-GB', tz=360)
tags = KeywordsGenerator(pytrends)\
.generate_tags('text_file.txt')
print(tags)Then The Dependencies
This script requires:
nltk==3.7
pytrends==4.8.0Analysis of the Script
I copy and paste my article into text file.txt, and the code returns the keywords as a comma-separated string.
To achieve this:
A class I made is called KeywordsGenerator.
This class has a function:
generate_tagsThe function
generate_tagsperforms the following tasks:
retrieves text file contents
uses NLP to clean the text by tokenizing sentences into words, removing punctuation, and other elements.
identifies the most frequent words that are relevant.
The
pytrendsAPI is then used to retrieve related phrases that are trending for each word from Google.finally adds a comma to the end of the word list.
4. I then use the keywords and paste them into the SEO area of my website.
These terms are trending on Google and relevant to my topic. My site's rankings and traffic have improved since I added new keywords. This little script puts our knowledge to work. I shared the script in case anyone faces similar issues.
I hope it helps readers sell their work.
