More on Personal Growth
Zuzanna Sieja
3 years ago
In 2022, each data scientist needs to read these 11 books.
Non-technical talents can benefit data scientists in addition to statistics and programming.
As our article 5 Most In-Demand Skills for Data Scientists shows, being business-minded is useful. How can you get such a diverse skill set? We've compiled a list of helpful resources.
Data science, data analysis, programming, and business are covered. Even a few of these books will make you a better data scientist.
Ready? Let’s dive in.
Best books for data scientists
1. The Black Swan
Author: Nassim Taleb
First, a less obvious title. Nassim Nicholas Taleb's seminal series examines uncertainty, probability, risk, and decision-making.
Three characteristics define a black swan event:
It is erratic.
It has a significant impact.
Many times, people try to come up with an explanation that makes it seem more predictable than it actually was.
People formerly believed all swans were white because they'd never seen otherwise. A black swan in Australia shattered their belief.
Taleb uses this incident to illustrate how human thinking mistakes affect decision-making. The book teaches readers to be aware of unpredictability in the ever-changing IT business.
Try multiple tactics and models because you may find the answer.
2. High Output Management
Author: Andrew Grove
Intel's former chairman and CEO provides his insights on developing a global firm in this business book. We think Grove would choose “management” to describe the talent needed to start and run a business.
That's a skill for CEOs, techies, and data scientists. Grove writes on developing productive teams, motivation, real-life business scenarios, and revolutionizing work.
Five lessons:
Every action is a procedure.
Meetings are a medium of work
Manage short-term goals in accordance with long-term strategies.
Mission-oriented teams accelerate while functional teams increase leverage.
Utilize performance evaluations to enhance output.
So — if the above captures your imagination, it’s well worth getting stuck in.
3. The Hard Thing About Hard Things: Building a Business When There Are No Easy Answers
Author: Ben Horowitz
Few realize how difficult it is to run a business, even though many see it as a tremendous opportunity.
Business schools don't teach managers how to handle the toughest difficulties; they're usually on their own. So Ben Horowitz wrote this book.
It gives tips on creating and maintaining a new firm and analyzes the hurdles CEOs face.
Find suggestions on:
create software
Run a business.
Promote a product
Obtain resources
Smart investment
oversee daily operations
This book will help you cope with tough times.
4. Obviously Awesome: How to Nail Product Positioning
Author: April Dunford
Your job as a data scientist is a product. You should be able to sell what you do to clients. Even if your product is great, you must convince them.
How to? April Dunford's advice: Her book explains how to connect with customers by making your offering seem like a secret sauce.
You'll learn:
Select the ideal market for your products.
Connect an audience to the value of your goods right away.
Take use of three positioning philosophies.
Utilize market trends to aid purchasers
5. The Mom test
Author: Rob Fitzpatrick
The Mom Test improves communication. Client conversations are rarely predictable. The book emphasizes one of the most important communication rules: enquire about specific prior behaviors.
Both ways work. If a client has suggestions or demands, listen carefully and ensure everyone understands. The book is packed with client-speaking tips.
6. Introduction to Machine Learning with Python: A Guide for Data Scientists
Authors: Andreas C. Müller, Sarah Guido
Now, technical documents.
This book is for Python-savvy data scientists who wish to learn machine learning. Authors explain how to use algorithms instead of math theory.
Their technique is ideal for developers who wish to study machine learning basics and use cases. Sci-kit-learn, NumPy, SciPy, pandas, and Jupyter Notebook are covered beyond Python.
If you know machine learning or artificial neural networks, skip this.
7. Python Data Science Handbook: Essential Tools for Working with Data
Author: Jake VanderPlas
Data work isn't easy. Data manipulation, transformation, cleansing, and visualization must be exact.
Python is a popular tool. The Python Data Science Handbook explains everything. The book describes how to utilize Pandas, Numpy, Matplotlib, Scikit-Learn, and Jupyter for beginners.
The only thing missing is a way to apply your learnings.
8. Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython
Author: Wes McKinney
The author leads you through manipulating, processing, cleaning, and analyzing Python datasets using NumPy, Pandas, and IPython.
The book's realistic case studies make it a great resource for Python or scientific computing beginners. Once accomplished, you'll uncover online analytics, finance, social science, and economics solutions.
9. Data Science from Scratch
Author: Joel Grus
Here's a title for data scientists with Python, stats, maths, and algebra skills (alongside a grasp of algorithms and machine learning). You'll learn data science's essential libraries, frameworks, modules, and toolkits.
The author works through all the key principles, providing you with the practical abilities to develop simple code. The book is appropriate for intermediate programmers interested in data science and machine learning.
Not that prior knowledge is required. The writing style matches all experience levels, but understanding will help you absorb more.
10. Machine Learning Yearning
Author: Andrew Ng
Andrew Ng is a machine learning expert. Co-founded and teaches at Stanford. This free book shows you how to structure an ML project, including recognizing mistakes and building in complex contexts.
The book delivers knowledge and teaches how to apply it, so you'll know how to:
Determine the optimal course of action for your ML project.
Create software that is more effective than people.
Recognize when to use end-to-end, transfer, and multi-task learning, and how to do so.
Identifying machine learning system flaws
Ng writes easy-to-read books. No rigorous math theory; just a terrific approach to understanding how to make technical machine learning decisions.
11. Deep Learning with PyTorch Step-by-Step
Author: Daniel Voigt Godoy
The last title is also the most recent. The book was revised on 23 January 2022 to discuss Deep Learning and PyTorch, a Python coding tool.
It comprises four parts:
Fundamentals (gradient descent, training linear and logistic regressions in PyTorch)
Machine Learning (deeper models and activation functions, convolutions, transfer learning, initialization schemes)
Sequences (RNN, GRU, LSTM, seq2seq models, attention, self-attention, transformers)
Automatic Language Recognition (tokenization, embeddings, contextual word embeddings, ELMo, BERT, GPT-2)
We admire the book's readability. The author avoids difficult mathematical concepts, making the material feel like a conversation.
Is every data scientist a humanist?
Even as a technological professional, you can't escape human interaction, especially with clients.
We hope these books will help you develop interpersonal skills.
Hudson Rennie
3 years ago
My Work at a $1.2 Billion Startup That Failed
Sometimes doing everything correctly isn't enough.
In 2020, I could fix my life.
After failing to start a business, I owed $40,000 and had no work.
A $1.2 billion startup on the cusp of going public pulled me up.
Ironically, it was getting ready for an epic fall — with the world watching.
Life sometimes helps. Without a base, even the strongest fall. A corporation that did everything right failed 3 months after going public.
First-row view.
Apple is the creator of Adore.
Out of respect, I've altered the company and employees' names in this account, despite their failure.
Although being a publicly traded company, it may become obvious.
We’ll call it “Adore” — a revolutionary concept in retail shopping.
Two Apple execs established Adore in 2014 with a focus on people-first purchasing.
Jon and Tim:
The concept for the stylish Apple retail locations you see today was developed by retail expert Jon Swanson, who collaborated closely with Steve Jobs.
Tim Cruiter is a graphic designer who produced the recognizable bouncing lamp video that appears at the start of every Pixar film.
The dynamic duo realized their vision.
“What if you could combine the convenience of online shopping with the confidence of the conventional brick-and-mortar store experience.”
Adore's mobile store concept combined traditional retail with online shopping.
Adore brought joy to 70+ cities and 4 countries over 7 years, including the US, Canada, and the UK.
Being employed on the ground floor, with world dominance and IPO on the horizon, was exciting.
I started as an Adore Expert.
I delivered cell phones, helped consumers set them up, and sold add-ons.
As the company grew, I became a Virtual Learning Facilitator and trained new employees across North America using Zoom.
In this capacity, I gained corporate insider knowledge. I worked with the creative team and Jon and Tim.
It's where I saw company foundation fissures. Despite appearances, investors were concerned.
The business strategy was ground-breaking.
Even after seeing my employee stocks fall from a home down payment to $0 (when Adore filed for bankruptcy), it's hard to pinpoint what went wrong.
Solid business model, well-executed.
Jon and Tim's chase for public funding ended in glory.
Here’s the business model in a nutshell:
Buying cell phones is cumbersome. You have two choices:
Online purchase: not knowing what plan you require or how to operate your device.
Enter a store, which can be troublesome and stressful.
Apple, AT&T, and Rogers offered Adore as a free delivery add-on. Customers could:
Have their phone delivered by UPS or Canada Post in 1-2 weeks.
Alternately, arrange for a person to visit them the same day (or sometimes even the same hour) to assist them set up their phone and demonstrate how to use it (transferring contacts, switching the SIM card, etc.).
Each Adore Expert brought a van with extra devices and accessories to customers.
Happy customers.
Here’s how Adore and its partners made money:
Adores partners appreciated sending Experts to consumers' homes since they improved customer satisfaction, average sale, and gadget returns.
**Telecom enterprises have low customer satisfaction. The average NPS is 30/100. Adore's global NPS was 80.
Adore made money by:
a set cost for each delivery
commission on sold warranties and extras
Consumer product applications seemed infinite.
A proprietary scheduling system (“The Adore App”), allowed for same-day, even same-hour deliveries.
It differentiates Adore.
They treated staff generously by:
Options on stock
health advantages
sales enticements
high rates per hour
Four-day workweeks were set by experts.
Being hired early felt like joining Uber, Netflix, or Tesla. We hoped the company's stocks would rise.
Exciting times.
I smiled as I greeted more than 1,000 new staff.
I spent a decade in retail before joining Adore. I needed a change.
After a leap of faith, I needed a lifeline. So, I applied for retail sales jobs in the spring of 2019.
The universe typically offers you what you want after you accept what you need. I needed a job to settle my debt and reach $0 again.
And the universe listened.
After being hired as an Adore Expert, I became a Virtual Learning Facilitator. Enough said.
After weeks of economic damage from the pandemic.
This employment let me work from home during the pandemic. It taught me excellent business skills.
I was active in brainstorming, onboarding new personnel, and expanding communication as we grew.
This job gave me vital skills and a regular paycheck during the pandemic.
It wasn’t until January of 2022 that I left on my own accord to try to work for myself again — this time, it’s going much better.
Adore was perfect. We valued:
Connection
Discovery
Empathy
Everything we did centered on compassion, and we held frequent Justice Calls to discuss diversity and work culture.
The last day of onboarding typically ended in tears as employees felt like they'd found a home, as I had.
Like all nice things, the wonderful vibes ended.
First indication of distress
My first day at the workplace was great.
Fun, intuitive, and they wanted creative individuals, not salesman.
While sales were important, the company's vision was more important.
“To deliver joy through life-changing mobile retail experiences.”
Thorough, forward-thinking training. We had a module on intuition. It gave us role ownership.
We were flown cross-country for training, gave feedback, and felt like we made a difference. Multiple contacts responded immediately and enthusiastically.
The atmosphere was genuine.
Making money was secondary, though. Incredible service was a priority.
Jon and Tim answered new hires' questions during Zoom calls during onboarding. CEOs seldom meet new hires this way, but they seemed to enjoy it.
All appeared well.
But in late 2021, things started changing.
Adore's leadership changed after its IPO. From basic values to sales maximization. We lost communication and were forced to fend for ourselves.
Removed the training wheels.
It got tougher to gain instructions from those above me, and new employees told me their roles weren't as advertised.
External money-focused managers were hired.
Instead of creative types, we hired salespeople.
With a new focus on numbers, Adore's uniqueness began to crumble.
Via Zoom, hundreds of workers were let go.
So.
Early in 2022, mass Zoom firings were trending. A CEO firing 900 workers over Zoom went viral.
Adore was special to me, but it became a headline.
30 June 2022, Vice Motherboard published Watch as Adore's CEO Fires Hundreds.
It described a leaked video of Jon Swanson laying off all staff in Canada and the UK.
They called it a “notice of redundancy”.
The corporation couldn't pay its employees.
I loved Adore's underlying ideals, among other things. We called clients Adorers and sold solutions, not add-ons.
But, like anything, a company is only as strong as its weakest link. And obviously, the people-first focus wasn’t making enough money.
There were signs. The expansion was presumably a race against time and money.
Adore finally declared bankruptcy.
Adore declared bankruptcy 3 months after going public. It happened in waves, like any large-scale fall.
Initial key players to leave were
Then, communication deteriorated.
Lastly, the corporate culture disintegrated.
6 months after leaving Adore, I received a letter in the mail from a Law firm — it was about my stocks.
Adore filed Chapter 11. I had to sue to collect my worthless investments.
I hoped those stocks will be valuable someday. Nope. Nope.
Sad, I sighed.
$1.2 billion firm gone.
I left the workplace 3 months before starting a writing business. Despite being mediocre, I'm doing fine.
I got up as Adore fell.
Finally, can we scale kindness?
I trust my gut. Changes at Adore made me leave before it sank.
Adores' unceremonious slide from a top startup to bankruptcy is astonishing to me.
The company did everything perfectly, in my opinion.
first to market,
provided excellent service
paid their staff handsomely.
was responsible and attentive to criticism
The company wasn't led by an egotistical eccentric. The crew had centuries of cumulative space experience.
I'm optimistic about the future of work culture, but is compassion scalable?
Daniel Vassallo
3 years ago
Why I quit a $500K job at Amazon to work for myself
I quit my 8-year Amazon job last week. I wasn't motivated to do another year despite promotions, pay, recognition, and praise.
In AWS, I built developer tools. I could have worked in that field forever.
I became an Amazon developer. Within 3.5 years, I was promoted twice to senior engineer and would have been promoted to principal engineer if I stayed. The company said I had great potential.
Over time, I became a reputed expert and leader within the company. I was respected.
First year I made $75K, last year $511K. If I stayed another two years, I could have made $1M.
Despite Amazon's reputation, my work–life balance was good. I no longer needed to prove myself and could do everything in 40 hours a week. My team worked from home once a week, and I rarely opened my laptop nights or weekends.
My coworkers were great. I had three generous, empathetic managers. I’m very grateful to everyone I worked with.
Everything was going well and getting better. My motivation to go to work each morning was declining despite my career and income growth.
Another promotion, pay raise, or big project wouldn't have boosted my motivation. Motivation was also waning. It was my freedom.
Demotivation
My motivation was high in the beginning. I worked with someone on an internal tool with little scrutiny. I had more freedom to choose how and what to work on than in recent years. Me and another person improved it, talked to users, released updates, and tested it. Whatever we wanted, we did. We did our best and were mostly self-directed.
In recent years, things have changed. My department's most important project had many stakeholders and complex goals. What I could do depended on my ability to convince others it was the best way to achieve our goals.
Amazon was always someone else's terms. The terms started out simple (keep fixing it), but became more complex over time (maximize all goals; satisfy all stakeholders). Working in a large organization imposed restrictions on how to do the work, what to do, what goals to set, and what business to pursue. This situation forced me to do things I didn't want to do.
Finding New Motivation
What would I do forever? Not something I did until I reached a milestone (an exit), but something I'd do until I'm 80. What could I do for the next 45 years that would make me excited to wake up and pay my bills? Is that too unambitious? Nope. Because I'm motivated by two things.
One is an external carrot or stick. I'm not forced to file my taxes every April, but I do because I don't want to go to jail. Or I may not like something but do it anyway because I need to pay the bills or want a nice car. Extrinsic motivation
One is internal. When there's no carrot or stick, this motivates me. This fuels hobbies. I wanted a job that was intrinsically motivated.
Is this too low-key? Extrinsic motivation isn't sustainable. Getting promoted felt good for a week, then it was over. When I hit $100K, I admired my W2 for a few days, but then it wore off. Same thing happened at $200K, $300K, $400K, and $500K. Earning $1M or $10M wouldn't change anything. I feel the same about every material reward or possession. Getting them feels good at first, but quickly fades.
Things I've done since I was a kid, when no one forced me to, don't wear off. Coding, selling my creations, charting my own path, and being honest. Why not always use my strengths and motivation? I'm lucky to live in a time when I can work independently in my field without large investments. So that’s what I’m doing.
What’s Next?
I'm going all-in on independence and will make a living from scratch. I won't do only what I like, but on my terms. My goal is to cover my family's expenses before my savings run out while doing something I enjoy. What more could I want from my work?
You can now follow me on Twitter as I continue to document my journey.
This post is a summary. Read full article here
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Bastian Hasslinger
3 years ago
Before 2021, most startups had excessive valuations. It is currently causing issues.
Higher startup valuations are often favorable for all parties. High valuations show a business's potential. New customers and talent are attracted. They earn respect.
Everyone benefits if a company's valuation rises.
Founders and investors have always been incentivized to overestimate a company's value.
Post-money valuations were inflated by 2021 market expectations and the valuation model's mechanisms.
Founders must understand both levers to handle a normalizing market.
2021, the year of miracles
2021 must've seemed miraculous to entrepreneurs, employees, and VCs. Valuations rose, and funding resumed after the first Covid-19 epidemic caution.
In 2021, VC investments increased from $335B to $643B. 518 new worldwide unicorns vs. 134 in 2020; 951 US IPOs vs. 431.
Things can change quickly, as 2020-21 showed.
Rising interest rates, geopolitical developments, and normalizing technology conditions drive down share prices and tech company market caps in 2022. Zoom, the poster-child of early lockdown success, is down 37% since 1st Jan.
Once-inflated valuations can become a problem in a normalizing market, especially for founders, employees, and early investors.
the reason why startups are always overvalued
To see why inflated valuations are a problem, consider one of its causes.
Private company values only fluctuate following a new investment round, unlike publicly-traded corporations. The startup's new value is calculated simply:
(Latest round share price) x (total number of company shares)
This is the industry standard Post-Money Valuation model.
Let’s illustrate how it works with an example. If a VC invests $10M for 1M shares (at $10/share), and the company has 10M shares after the round, its Post-Money Valuation is $100M (10/share x 10M shares).
This approach might seem like the most natural way to assess a business, but the model often unintentionally overstates the underlying value of the company even if the share price paid by the investor is fair. All shares aren't equal.
New investors in a corporation will always try to minimize their downside risk, or the amount they lose if things go wrong. New investors will try to negotiate better terms and pay a premium.
How the value of a struggling SpaceX increased
SpaceX's 2008 Series D is an example. Despite the financial crisis and unsuccessful rocket launches, the company's Post-Money Valuation was 36% higher after the investment round. Why?
Series D SpaceX shares were protected. In case of liquidation, Series D investors were guaranteed a 2x return before other shareholders.
Due to downside protection, investors were willing to pay a higher price for this new share class.
The Post-Money Valuation model overpriced SpaceX because it viewed all the shares as equal (they weren't).
Why entrepreneurs, workers, and early investors stand to lose the most
Post-Money Valuation is an effective and sufficient method for assessing a startup's valuation, despite not taking share class disparities into consideration.
In a robust market, where the firm valuation will certainly expand with the next fundraising round or exit, the inflated value is of little significance.
Fairness endures. If a corporation leaves at a greater valuation, each stakeholder will receive a proportional distribution. (i.e., 5% of a $100M corporation yields $5M).
SpaceX's inherent overvaluation was never a problem. Had it been sold for less than its Post-Money Valuation, some shareholders, including founders, staff, and early investors, would have seen their ownership drop.
The unforgiving world of 2022
In 2022, founders, employees, and investors who benefited from inflated values will face below-valuation exits and down-rounds.
For them, 2021 will be a curse, not a blessing.
Some tech giants are worried. Klarna's valuation fell from $45B (Oct 21) to $30B (Jun 22), Canvas from $40B to $27B, and GoPuffs from $17B to $8.3B.
Shazam and Blue Apron have to exit or IPO at a cheaper price. Premium share classes are protected, while others receive less. The same goes for bankrupts.
Those who continue at lower valuations will lose reputation and talent. When their value declines by half, generous employee stock options become less enticing, and their ability to return anything is questioned.
What can we infer about the present situation?
Such techniques to enhance your company's value or stop a normalizing market are fiction.
The current situation is a painful reminder for entrepreneurs and a crucial lesson for future firms.
The devastating market fall of the previous six months has taught us one thing:
Keep in mind that any valuation is speculative. Money Post A startup's valuation is a highly simplified approximation of its true value, particularly in the early phases when it lacks significant income or a cutting-edge product. It is merely a projection of the future and a hypothetical meter. Until it is achieved by an exit, a valuation is nothing more than a number on paper.
Assume the value of your company is lower than it was in the past. Your previous valuation might not be accurate now due to substantial changes in the startup financing markets. There is little reason to think that your company's value will remain the same given the 50%+ decline in many newly listed IT companies. Recognize how the market situation is changing and use caution.
Recognize the importance of the stake you hold. Each share class has a unique value that varies. Know the sort of share class you own and how additional contractual provisions affect the market value of your security. Frameworks have been provided by Metrick and Yasuda (Yale & UC) and Gornall and Strebulaev (Stanford) for comprehending the terms that affect investors' cash-flow rights upon withdrawal. As a result, you will be able to more accurately evaluate your firm and determine the worth of each share class.
Be wary of approving excessively protective share terms.
The trade-offs should be considered while negotiating subsequent rounds. Accepting punitive contractual terms could first seem like a smart option in order to uphold your inflated worth, but you should proceed with caution. Such provisions ALWAYS result in misaligned shareholders, with common shareholders (such as you and your staff) at the bottom of the list.
Tim Denning
3 years ago
Bills are paid by your 9 to 5. 6 through 12 help you build money.
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.
Vitalik
3 years ago
Fairness alternatives to selling below market clearing prices (or community sentiment, or fun)
When a seller has a limited supply of an item in high (or uncertain and possibly high) demand, they frequently set a price far below what "the market will bear." As a result, the item sells out quickly, with lucky buyers being those who tried to buy first. This has happened in the Ethereum ecosystem, particularly with NFT sales and token sales/ICOs. But this phenomenon is much older; concerts and restaurants frequently make similar choices, resulting in fast sell-outs or long lines.
Why do sellers do this? Economists have long wondered. A seller should sell at the market-clearing price if the amount buyers are willing to buy exactly equals the amount the seller has to sell. If the seller is unsure of the market-clearing price, they should sell at auction and let the market decide. So, if you want to sell something below market value, don't do it. It will hurt your sales and it will hurt your customers. The competitions created by non-price-based allocation mechanisms can sometimes have negative externalities that harm third parties, as we will see.
However, the prevalence of below-market-clearing pricing suggests that sellers do it for good reason. And indeed, as decades of research into this topic has shown, there often are. So, is it possible to achieve the same goals with less unfairness, inefficiency, and harm?
Selling at below market-clearing prices has large inefficiencies and negative externalities
An item that is sold at market value or at an auction allows someone who really wants it to pay the high price or bid high in the auction. So, if a seller sells an item below market value, some people will get it and others won't. But the mechanism deciding who gets the item isn't random, and it's not always well correlated with participant desire. It's not always about being the fastest at clicking buttons. Sometimes it means waking up at 2 a.m. (but 11 p.m. or even 2 p.m. elsewhere). Sometimes it's just a "auction by other means" that's more chaotic, less efficient, and has far more negative externalities.
There are many examples of this in the Ethereum ecosystem. Let's start with the 2017 ICO craze. For example, an ICO project would set the price of the token and a hard maximum for how many tokens they are willing to sell, and the sale would start automatically at some point in time. The sale ends when the cap is reached.
So what? In practice, these sales often ended in 30 seconds or less. Everyone would start sending transactions in as soon as (or just before) the sale started, offering higher and higher fees to encourage miners to include their transaction first. Instead of the token seller receiving revenue, miners receive it, and the sale prices out all other applications on-chain.
The most expensive transaction in the BAT sale set a fee of 580,000 gwei, paying a fee of $6,600 to get included in the sale.
Many ICOs after that tried various strategies to avoid these gas price auctions; one ICO notably had a smart contract that checked the transaction's gasprice and rejected it if it exceeded 50 gwei. But that didn't solve the issue. Buyers hoping to game the system sent many transactions hoping one would get through. An auction by another name, clogging the chain even more.
ICOs have recently lost popularity, but NFTs and NFT sales have risen in popularity. But the NFT space didn't learn from 2017; they do fixed-quantity sales just like ICOs (eg. see the mint function on lines 97-108 of this contract here). So what?
That's not the worst; some NFT sales have caused gas price spikes of up to 2000 gwei.
High gas prices from users fighting to get in first by sending higher and higher transaction fees. An auction renamed, pricing out all other applications on-chain for 15 minutes.
So why do sellers sometimes sell below market price?
Selling below market value is nothing new, and many articles, papers, and podcasts have written (and sometimes bitterly complained) about the unwillingness to use auctions or set prices to market-clearing levels.
Many of the arguments are the same for both blockchain (NFTs and ICOs) and non-blockchain examples (popular restaurants and concerts). Fairness and the desire not to exclude the poor, lose fans or create tension by being perceived as greedy are major concerns. The 1986 paper by Kahneman, Knetsch, and Thaler explains how fairness and greed can influence these decisions. I recall that the desire to avoid perceptions of greed was also a major factor in discouraging the use of auction-like mechanisms in 2017.
Aside from fairness concerns, there is the argument that selling out and long lines create a sense of popularity and prestige, making the product more appealing to others. Long lines should have the same effect as high prices in a rational actor model, but this is not the case in reality. This applies to ICOs and NFTs as well as restaurants. Aside from increasing marketing value, some people find the game of grabbing a limited set of opportunities first before everyone else is quite entertaining.
But there are some blockchain-specific factors. One argument for selling ICO tokens below market value (and one that persuaded the OmiseGo team to adopt their capped sale strategy) is community dynamics. The first rule of community sentiment management is to encourage price increases. People are happy if they are "in the green." If the price drops below what the community members paid, they are unhappy and start calling you a scammer, possibly causing a social media cascade where everyone calls you a scammer.
This effect can only be avoided by pricing low enough that post-launch market prices will almost certainly be higher. But how do you do this without creating a rush for the gates that leads to an auction?
Interesting solutions
It's 2021. We have a blockchain. The blockchain is home to a powerful decentralized finance ecosystem, as well as a rapidly expanding set of non-financial tools. The blockchain also allows us to reset social norms. Where decades of economists yelling about "efficiency" failed, blockchains may be able to legitimize new uses of mechanism design. If we could use our more advanced tools to create an approach that more directly solves the problems, with fewer side effects, wouldn't that be better than fiddling with a coarse-grained one-dimensional strategy space of selling at market price versus below market price?
Begin with the goals. We'll try to cover ICOs, NFTs, and conference tickets (really a type of NFT) all at the same time.
1. Fairness: don't completely exclude low-income people from participation; give them a chance. The goal of token sales is to avoid high initial wealth concentration and have a larger and more diverse initial token holder community.
2. Don’t create races: Avoid situations where many people rush to do the same thing and only a few get in (this is the type of situation that leads to the horrible auctions-by-another-name that we saw above).
3. Don't require precise market knowledge: the mechanism should work even if the seller has no idea how much demand exists.
4. Fun: The process of participating in the sale should be fun and game-like, but not frustrating.
5. Give buyers positive expected returns: in the case of a token (or an NFT), buyers should expect price increases rather than decreases. This requires selling below market value.
Let's start with (1). From Ethereum's perspective, there is a simple solution. Use a tool designed for the job: proof of personhood protocols! Here's one quick idea:
Mechanism 1 Each participant (verified by ID) can buy up to ‘’X’’ tokens at price P, with the option to buy more at an auction.
With the per-person mechanism, buyers can get positive expected returns for the portion sold through the per-person mechanism, and the auction part does not require sellers to understand demand levels. Is it race-free? The number of participants buying through the per-person pool appears to be high. But what if the per-person pool isn't big enough to accommodate everyone?
Make the per-person allocation amount dynamic.
Mechanism 2 Each participant can deposit up to X tokens into a smart contract to declare interest. Last but not least, each buyer receives min(X, N / buyers) tokens, where N is the total sold through the per-person pool (some other amount can also be sold by auction). The buyer gets their deposit back if it exceeds the amount needed to buy their allocation.
No longer is there a race condition based on the number of buyers per person. No matter how high the demand, it's always better to join sooner rather than later.
Here's another idea if you like clever game mechanics with fancy quadratic formulas.
Mechanism 3 Each participant can buy X units at a price P X 2 up to a maximum of C tokens per buyer. C starts low and gradually increases until enough units are sold.
The quantity allocated to each buyer is theoretically optimal, though post-sale transfers will degrade this optimality over time. Mechanisms 2 and 3 appear to meet all of the above objectives. They're not perfect, but they're good starting points.
One more issue. For fixed and limited supply NFTs, the equilibrium purchased quantity per participant may be fractional (in mechanism 2, number of buyers > N, and in mechanism 3, setting C = 1 may already lead to over-subscription). With fractional sales, you can offer lottery tickets: if there are N items available, you have a chance of N/number of buyers of getting the item, otherwise you get a refund. For a conference, groups could bundle their lottery tickets to guarantee a win or a loss. The certainty of getting the item can be auctioned.
The bottom tier of "sponsorships" can be used to sell conference tickets at market rate. You may end up with a sponsor board full of people's faces, but is that okay? After all, John Lilic was on EthCC's sponsor board!
Simply put, if you want to be reliably fair to people, you need an input that explicitly measures people. Authentication protocols do this (and if desired can be combined with zero knowledge proofs to ensure privacy). So we should combine the efficiency of market and auction-based pricing with the equality of proof of personhood mechanics.
Answers to possible questions
Q: Won't people who don't care about your project buy the item and immediately resell it?
A: Not at first. Meta-games take time to appear in practice. If they do, making them untradeable for a while may help mitigate the damage. Using your face to claim that your previous account was hacked and that your identity, including everything in it, should be moved to another account works because proof-of-personhood identities are untradeable.
Q: What if I want to make my item available to a specific community?
A: Instead of ID, use proof of participation tokens linked to community events. Another option, also serving egalitarian and gamification purposes, is to encrypt items within publicly available puzzle solutions.
Q: How do we know they'll accept? Strange new mechanisms have previously been resisted.
A: Having economists write screeds about how they "should" accept a new mechanism that they find strange is difficult (or even "equity"). However, abrupt changes in context effectively reset people's expectations. So the blockchain space is the best place to try this. You could wait for the "metaverse", but it's possible that the best version will run on Ethereum anyway, so start now.