More on Economics & Investing

Desiree Peralta
3 years ago
How to Use the 2023 Recession to Grow Your Wealth Exponentially
This season's three best money moves.
“Millionaires are made in recessions.” — Time Capital
We're in a serious downturn, whether or not we're in a recession.
97% of business owners are decreasing costs by more than 10%, and all markets are down 30%.
If you know what you're doing and analyze the markets correctly, this is your chance to become a millionaire.
In any recession, there are always excellent possibilities to seize. Real estate, crypto, stocks, enterprises, etc.
What you do with your money could influence your future riches.
This article analyzes the three key markets, their circumstances for 2023, and how to profit from them.
Ways to make money on the stock market.
If you're conservative like me, you should invest in an index fund. Most of these funds are down 10-30% of ATH:
In earlier recessions, most money index funds lost 20%. After this downturn, they grew and passed the ATH in subsequent months.
Now is the greatest moment to invest in index funds to grow your money in a low-risk approach and make 20%.
If you want to be risky but wise, pick companies that will get better next year but are struggling now.
Even while we can't be 100% confident of a company's future performance, we know some are strong and will have a fantastic year.
Microsoft (down 22%), JPMorgan Chase (15.6%), Amazon (45%), and Disney (33.8%).
These firms give dividends, so you can earn passively while you wait.
So I consider that a good strategy to make wealth in the current stock market is to create two portfolios: one based on index funds to earn 10% to 20% profit when the corrections end, and the other based on individual stocks of popular and strong companies to earn 20%-30% return and dividends while you wait.
How to profit from the downturn in the real estate industry.
With rising mortgage rates, it's the worst moment to buy a home if you don't want to be eaten by banks. In the U.S., interest rates are double what they were three years ago, so buying now looks foolish.
Due to these rates, property prices are falling, but that won't last long since individuals will take advantage.
According to historical data, now is the ideal moment to buy a house for the next five years and perhaps forever.
If you can buy a house, do it. You can refinance the interest at a lower rate with acceptable credit, but not the house price.
Take advantage of the housing market prices now because you won't find a decent deal when rates normalize.
How to profit from the cryptocurrency market.
This is the riskiest market to tackle right now, but it could offer the most opportunities if done appropriately.
The most powerful cryptocurrencies are down more than 60% from last year: $68,990 for BTC and $4,865 for ETH.
If you focus on those two coins, you can make 30%-60% without waiting for them to return to their ATH, and they're low enough to be a solid investment.
I don't encourage trying other altcoins because the crypto market is in crisis and you can lose everything if you're greedy.
Still, the main Cryptos are a good investment provided you store them in an external wallet and follow financial gurus' security advice.
Last thoughts
We can't anticipate a recession until it ends. We can't forecast a market or asset's lowest point, therefore waiting makes little sense.
If you want to develop your wealth, assess the money prospects on all the marketplaces and initiate long-term trades.
Many millionaires are made during recessions because they don't fear negative figures and use them to scale their money.

Tanya Aggarwal
3 years ago
What I learned from my experience as a recent graduate working in venture capital
Every week I meet many people interested in VC. Many of them ask me what it's like to be a junior analyst in VC or what I've learned so far.
Looking back, I've learned many things as a junior VC, having gone through an almost-euphoric peak bull market, failed tech IPOs of 2019 including WeWorks' catastrophic fall, and the beginnings of a bearish market.
1. Network, network, network!
VCs spend 80% of their time networking. Junior VCs source deals or manage portfolios. You spend your time bringing startups to your fund or helping existing portfolio companies grow. Knowing stakeholders (corporations, star talent, investors) in your particular areas of investment helps you develop your portfolio.
Networking was one of my strengths. When I first started in the industry, I'd go to startup events and meet 50 people a month. Over time, I realized these relationships were shallow and I was only getting business cards. So I stopped seeing networking as a transaction. VC is a long-term game, so you should work with people you like. Now I know who I click with and can build deeper relationships with them. My network is smaller but more valuable than before.
2. The Most Important Metric Is Founder
People often ask how we pick investments. Why some companies can raise money and others can't is a mystery. The founder is the most important metric for VCs. When a company is young, the product, environment, and team all change, but the founder remains constant. VCs bet on the founder, not the company.
How do we decide which founders are best after 2-3 calls? When looking at a founder's profile, ask why this person can solve this problem. The founders' track record will tell. If the founder is a serial entrepreneur, you know he/she possesses the entrepreneur DNA and will likely succeed again. If it's his/her first startup, focus on industry knowledge to deliver the best solution.
3. A company's fate can be determined by macrotrends.
Macro trends are crucial. A company can have the perfect product, founder, and team, but if it's solving the wrong problem, it won't succeed. I've also seen average companies ride the wave to success. When you're on the right side of a trend, there's so much demand that more companies can get a piece of the pie.
In COVID-19, macro trends made or broke a company. Ed-tech and health-tech companies gained unicorn status and raised funding at inflated valuations due to sudden demand. With the easing of pandemic restrictions and the start of a bear market, many of these companies' valuations are in question.
4. Look for methods to ACTUALLY add value.
You only need to go on VC twitter (read: @vcstartterkit and @vcbrags) for 5 minutes or look at fin-meme accounts on Instagram to see how much VCs claim to add value but how little they actually do. VC is a long-term game, though. Long-term, founders won't work with you if you don't add value.
How can we add value when we're young and have no network? Leaning on my strengths helped me. Instead of viewing my age and limited experience as a disadvantage, I realized that I brought a unique perspective to the table.
As a VC, you invest in companies that will be big in 5-7 years, and millennials and Gen Z will have the most purchasing power. Because you can relate to that market, you can offer insights that most Partners at 40 can't. I added value by helping with hiring because I had direct access to university talent pools and by finding university students for product beta testing.
5. Develop your personal brand.
Generalists or specialists run most funds. This means that funds either invest across industries or have a specific mandate. Most funds are becoming specialists, I've noticed. Top-tier founders don't lack capital, so funds must find other ways to attract them. Why would a founder work with a generalist fund when a specialist can offer better industry connections and partnership opportunities?
Same for fund members. Founders want quality investors. Become a thought leader in your industry to meet founders. Create content and share your thoughts on industry-related social media. When I first started building my brand, I found it helpful to interview industry veterans to create better content than I could on my own. Over time, my content attracted quality founders so I didn't have to look for them.
These are my biggest VC lessons. This list isn't exhaustive, but it's my industry survival guide.

Sofien Kaabar, CFA
3 years ago
How to Make a Trading Heatmap
Python Heatmap Technical Indicator
Heatmaps provide an instant overview. They can be used with correlations or to predict reactions or confirm the trend in trading. This article covers RSI heatmap creation.
The Market System
Market regime:
Bullish trend: The market tends to make higher highs, which indicates that the overall trend is upward.
Sideways: The market tends to fluctuate while staying within predetermined zones.
Bearish trend: The market has the propensity to make lower lows, indicating that the overall trend is downward.
Most tools detect the trend, but we cannot predict the next state. The best way to solve this problem is to assume the current state will continue and trade any reactions, preferably in the trend.
If the EURUSD is above its moving average and making higher highs, a trend-following strategy would be to wait for dips before buying and assuming the bullish trend will continue.
Indicator of Relative Strength
J. Welles Wilder Jr. introduced the RSI, a popular and versatile technical indicator. Used as a contrarian indicator to exploit extreme reactions. Calculating the default RSI usually involves these steps:
Determine the difference between the closing prices from the prior ones.
Distinguish between the positive and negative net changes.
Create a smoothed moving average for both the absolute values of the positive net changes and the negative net changes.
Take the difference between the smoothed positive and negative changes. The Relative Strength RS will be the name we use to describe this calculation.
To obtain the RSI, use the normalization formula shown below for each time step.
The 13-period RSI and black GBPUSD hourly values are shown above. RSI bounces near 25 and pauses around 75. Python requires a four-column OHLC array for RSI coding.
import numpy as np
def add_column(data, times):
for i in range(1, times + 1):
new = np.zeros((len(data), 1), dtype = float)
data = np.append(data, new, axis = 1)
return data
def delete_column(data, index, times):
for i in range(1, times + 1):
data = np.delete(data, index, axis = 1)
return data
def delete_row(data, number):
data = data[number:, ]
return data
def ma(data, lookback, close, position):
data = add_column(data, 1)
for i in range(len(data)):
try:
data[i, position] = (data[i - lookback + 1:i + 1, close].mean())
except IndexError:
pass
data = delete_row(data, lookback)
return data
def smoothed_ma(data, alpha, lookback, close, position):
lookback = (2 * lookback) - 1
alpha = alpha / (lookback + 1.0)
beta = 1 - alpha
data = ma(data, lookback, close, position)
data[lookback + 1, position] = (data[lookback + 1, close] * alpha) + (data[lookback, position] * beta)
for i in range(lookback + 2, len(data)):
try:
data[i, position] = (data[i, close] * alpha) + (data[i - 1, position] * beta)
except IndexError:
pass
return data
def rsi(data, lookback, close, position):
data = add_column(data, 5)
for i in range(len(data)):
data[i, position] = data[i, close] - data[i - 1, close]
for i in range(len(data)):
if data[i, position] > 0:
data[i, position + 1] = data[i, position]
elif data[i, position] < 0:
data[i, position + 2] = abs(data[i, position])
data = smoothed_ma(data, 2, lookback, position + 1, position + 3)
data = smoothed_ma(data, 2, lookback, position + 2, position + 4)
data[:, position + 5] = data[:, position + 3] / data[:, position + 4]
data[:, position + 6] = (100 - (100 / (1 + data[:, position + 5])))
data = delete_column(data, position, 6)
data = delete_row(data, lookback)
return dataMake sure to focus on the concepts and not the code. You can find the codes of most of my strategies in my books. The most important thing is to comprehend the techniques and strategies.
My weekly market sentiment report uses complex and simple models to understand the current positioning and predict the future direction of several major markets. Check out the report here:
Using the Heatmap to Find the Trend
RSI trend detection is easy but useless. Bullish and bearish regimes are in effect when the RSI is above or below 50, respectively. Tracing a vertical colored line creates the conditions below. How:
When the RSI is higher than 50, a green vertical line is drawn.
When the RSI is lower than 50, a red vertical line is drawn.
Zooming out yields a basic heatmap, as shown below.
Plot code:
def indicator_plot(data, second_panel, window = 250):
fig, ax = plt.subplots(2, figsize = (10, 5))
sample = data[-window:, ]
for i in range(len(sample)):
ax[0].vlines(x = i, ymin = sample[i, 2], ymax = sample[i, 1], color = 'black', linewidth = 1)
if sample[i, 3] > sample[i, 0]:
ax[0].vlines(x = i, ymin = sample[i, 0], ymax = sample[i, 3], color = 'black', linewidth = 1.5)
if sample[i, 3] < sample[i, 0]:
ax[0].vlines(x = i, ymin = sample[i, 3], ymax = sample[i, 0], color = 'black', linewidth = 1.5)
if sample[i, 3] == sample[i, 0]:
ax[0].vlines(x = i, ymin = sample[i, 3], ymax = sample[i, 0], color = 'black', linewidth = 1.5)
ax[0].grid()
for i in range(len(sample)):
if sample[i, second_panel] > 50:
ax[1].vlines(x = i, ymin = 0, ymax = 100, color = 'green', linewidth = 1.5)
if sample[i, second_panel] < 50:
ax[1].vlines(x = i, ymin = 0, ymax = 100, color = 'red', linewidth = 1.5)
ax[1].grid()
indicator_plot(my_data, 4, window = 500)Call RSI on your OHLC array's fifth column. 4. Adjusting lookback parameters reduces lag and false signals. Other indicators and conditions are possible.
Another suggestion is to develop an RSI Heatmap for Extreme Conditions.
Contrarian indicator RSI. The following rules apply:
Whenever the RSI is approaching the upper values, the color approaches red.
The color tends toward green whenever the RSI is getting close to the lower values.
Zooming out yields a basic heatmap, as shown below.
Plot code:
import matplotlib.pyplot as plt
def indicator_plot(data, second_panel, window = 250):
fig, ax = plt.subplots(2, figsize = (10, 5))
sample = data[-window:, ]
for i in range(len(sample)):
ax[0].vlines(x = i, ymin = sample[i, 2], ymax = sample[i, 1], color = 'black', linewidth = 1)
if sample[i, 3] > sample[i, 0]:
ax[0].vlines(x = i, ymin = sample[i, 0], ymax = sample[i, 3], color = 'black', linewidth = 1.5)
if sample[i, 3] < sample[i, 0]:
ax[0].vlines(x = i, ymin = sample[i, 3], ymax = sample[i, 0], color = 'black', linewidth = 1.5)
if sample[i, 3] == sample[i, 0]:
ax[0].vlines(x = i, ymin = sample[i, 3], ymax = sample[i, 0], color = 'black', linewidth = 1.5)
ax[0].grid()
for i in range(len(sample)):
if sample[i, second_panel] > 90:
ax[1].vlines(x = i, ymin = 0, ymax = 100, color = 'red', linewidth = 1.5)
if sample[i, second_panel] > 80 and sample[i, second_panel] < 90:
ax[1].vlines(x = i, ymin = 0, ymax = 100, color = 'darkred', linewidth = 1.5)
if sample[i, second_panel] > 70 and sample[i, second_panel] < 80:
ax[1].vlines(x = i, ymin = 0, ymax = 100, color = 'maroon', linewidth = 1.5)
if sample[i, second_panel] > 60 and sample[i, second_panel] < 70:
ax[1].vlines(x = i, ymin = 0, ymax = 100, color = 'firebrick', linewidth = 1.5)
if sample[i, second_panel] > 50 and sample[i, second_panel] < 60:
ax[1].vlines(x = i, ymin = 0, ymax = 100, color = 'grey', linewidth = 1.5)
if sample[i, second_panel] > 40 and sample[i, second_panel] < 50:
ax[1].vlines(x = i, ymin = 0, ymax = 100, color = 'grey', linewidth = 1.5)
if sample[i, second_panel] > 30 and sample[i, second_panel] < 40:
ax[1].vlines(x = i, ymin = 0, ymax = 100, color = 'lightgreen', linewidth = 1.5)
if sample[i, second_panel] > 20 and sample[i, second_panel] < 30:
ax[1].vlines(x = i, ymin = 0, ymax = 100, color = 'limegreen', linewidth = 1.5)
if sample[i, second_panel] > 10 and sample[i, second_panel] < 20:
ax[1].vlines(x = i, ymin = 0, ymax = 100, color = 'seagreen', linewidth = 1.5)
if sample[i, second_panel] > 0 and sample[i, second_panel] < 10:
ax[1].vlines(x = i, ymin = 0, ymax = 100, color = 'green', linewidth = 1.5)
ax[1].grid()
indicator_plot(my_data, 4, window = 500)Dark green and red areas indicate imminent bullish and bearish reactions, respectively. RSI around 50 is grey.
Summary
To conclude, my goal is to contribute to objective technical analysis, which promotes more transparent methods and strategies that must be back-tested before implementation.
Technical analysis will lose its reputation as subjective and unscientific.
When you find a trading strategy or technique, follow these steps:
Put emotions aside and adopt a critical mindset.
Test it in the past under conditions and simulations taken from real life.
Try optimizing it and performing a forward test if you find any potential.
Transaction costs and any slippage simulation should always be included in your tests.
Risk management and position sizing should always be considered in your tests.
After checking the above, monitor the strategy because market dynamics may change and make it unprofitable.
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Vitalik
4 years ago
An approximate introduction to how zk-SNARKs are possible (part 2)
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? But it turns out that there is a clever solution.
Polynomials
Polynomials are a special class of algebraic expressions of the form:
- x+5
- x^4
- x^3+3x^2+3x+1
- 628x^{271}+318x^{270}+530x^{269}+…+69x+381
i.e. they are a sum of any (finite!) number of terms of the form cx^k
There are many things that are fascinating about polynomials. But here we are going to zoom in on a particular one: polynomials are a single mathematical object that can contain an unbounded amount of information (think of them as a list of integers and this is obvious). The fourth example above contained 816 digits of tau, and one can easily imagine a polynomial that contains far more.
Furthermore, a single equation between polynomials can represent an unbounded number of equations between numbers. For example, consider the equation A(x)+ B(x) = C(x). If this equation is true, then it's also true that:
- A(0)+B(0)=C(0)
- A(1)+B(1)=C(1)
- A(2)+B(2)=C(2)
- A(3)+B(3)=C(3)
And so on for every possible coordinate. You can even construct polynomials to deliberately represent sets of numbers so you can check many equations all at once. For example, suppose that you wanted to check:
- 12+1=13
- 10+8=18
- 15+8=23
- 15+13=28
You can use a procedure called Lagrange interpolation to construct polynomials A(x) that give (12,10,15,15) as outputs at some specific set of coordinates (eg. (0,1,2,3)), B(x) the outputs (1,8,8,13) on thos same coordinates, and so forth. In fact, here are the polynomials:
- A(x)=-2x^3+\frac{19}{2}x^2-\frac{19}{2}x+12
- B(x)=2x^3-\frac{19}{2}x^2+\frac{29}{2}x+1
- C(x)=5x+13
Checking the equation A(x)+B(x)=C(x) with these polynomials checks all four above equations at the same time.
Comparing a polynomial to itself
You can even check relationships between a large number of adjacent evaluations of the same polynomial using a simple polynomial equation. This is slightly more advanced. Suppose that you want to check that, for a given polynomial F, F(x+2)=F(x)+F(x+1) with the integer range {0,1…89} (so if you also check F(0)=F(1)=1, then F(100) would be the 100th Fibonacci number)
As polynomials, F(x+2)-F(x+1)-F(x) would not be exactly zero, as it could give arbitrary answers outside the range x={0,1…98}. But we can do something clever. In general, there is a rule that if a polynomial P is zero across some set S=\{x_1,x_2…x_n\} then it can be expressed as P(x)=Z(x)*H(x), where Z(x)=(x-x_1)*(x-x_2)*…*(x-x_n) and H(x) is also a polynomial. In other words, any polynomial that equals zero across some set is a (polynomial) multiple of the simplest (lowest-degree) polynomial that equals zero across that same set.
Why is this the case? It is a nice corollary of polynomial long division: the factor theorem. We know that, when dividing P(x) by Z(x), we will get a quotient Q(x) and a remainder R(x) is strictly less than that of Z(x). Since we know that P is zero on all of S, it means that R has to be zero on all of S as well. So we can simply compute R(x) via polynomial interpolation, since it's a polynomial of degree at most n-1 and we know n values (the zeros at S). Interpolating a polynomial with all zeroes gives the zero polynomial, thus R(x)=0 and H(x)=Q(x).
Going back to our example, if we have a polynomial F that encodes Fibonacci numbers (so F(x+2)=F(x)+F(x+1) across x=\{0,1…98\}), then I can convince you that F actually satisfies this condition by proving that the polynomial P(x)=F(x+2)-F(x+1)-F(x) is zero over that range, by giving you the quotient:
H(x)=\frac{F(x+2)-F(x+1)-F(x)}{Z(x)}
Where Z(x) = (x-0)*(x-1)*…*(x-98).
You can calculate Z(x) yourself (ideally you would have it precomputed), check the equation, and if the check passes then F(x) satisfies the condition!
Now, step back and notice what we did here. We converted a 100-step-long computation into a single equation with polynomials. Of course, proving the N'th Fibonacci number is not an especially useful task, especially since Fibonacci numbers have a closed form. But you can use exactly the same basic technique, just with some extra polynomials and some more complicated equations, to encode arbitrary computations with an arbitrarily large number of steps.
see part 3
Jamie Ducharme
3 years ago
How monkeypox spreads (and doesn't spread)
Monkeypox was rare until recently. In 2005, a research called a cluster of six monkeypox cases in the Republic of Congo "the longest reported chain to date."
That's changed. This year, over 25,000 monkeypox cases have been reported in 83 countries, indicating widespread human-to-human transmission.
What spreads monkeypox? Monkeypox transmission research is ongoing; findings may change. But science says...
Most cases were formerly animal-related.
According to the WHO, monkeypox was first diagnosed in an infant in the DRC in 1970. After that, instances were infrequent and often tied to animals. In 2003, 47 Americans contracted rabies from pet prairie dogs.
In 2017, Nigeria saw a significant outbreak. NPR reported that doctors diagnosed young guys without animal exposure who had genital sores. Nigerian researchers highlighted the idea of sexual transmission in a 2019 study, but the theory didn't catch on. “People tend to cling on to tradition, and the idea is that monkeypox is transmitted from animals to humans,” explains research co-author Dr. Dimie Ogoina.
Most monkeypox cases are sex-related.
Human-to-human transmission of monkeypox occurs, and sexual activity plays a role.
Joseph Osmundson, a clinical assistant professor of biology at NYU, says most transmission occurs in queer and gay sexual networks through sexual or personal contact.
Monkeypox spreads by skin-to-skin contact, especially with its blister-like rash, explains Ogoina. Researchers are exploring whether people can be asymptomatically contagious, but they are infectious until their rash heals and fresh skin forms, according to the CDC.
A July research in the New England Journal of Medicine reported that of more than 500 monkeypox cases in 16 countries as of June, 95% were linked to sexual activity and 98% were among males who have sex with men. WHO Director-General Tedros Adhanom Ghebreyesus encouraged males to temporarily restrict their number of male partners in July.
Is monkeypox a sexually transmitted infection (STI)?
Skin-to-skin contact can spread monkeypox, not simply sexual activities. Dr. Roy Gulick, infectious disease chief at Weill Cornell Medicine and NewYork-Presbyterian, said monkeypox is not a "typical" STI. Monkeypox isn't a STI, claims the CDC.
Most cases in the current outbreak are tied to male sexual behavior, but Osmundson thinks the virus might also spread on sports teams, in spas, or in college dorms.
Can you get monkeypox from surfaces?
Monkeypox can be spread by touching infected clothing or bedding. According to a study, a U.K. health care worker caught monkeypox in 2018 after handling ill patient's bedding.
Angela Rasmussen, a virologist at the University of Saskatchewan in Canada, believes "incidental" contact seldom distributes the virus. “You need enough virus exposure to get infected,” she says. It's conceivable after sharing a bed or towel with an infectious person, but less likely after touching a doorknob, she says.
Dr. Müge evik, a clinical lecturer in infectious diseases at the University of St. Andrews in Scotland, says there is a "spectrum" of risk connected with monkeypox. "Every exposure isn't equal," she explains. "People must know where to be cautious. Reducing [sexual] partners may be more useful than cleaning coffee shop seats.
Is monkeypox airborne?
Exposure to an infectious person's respiratory fluids can cause monkeypox, but the WHO says it needs close, continuous face-to-face contact. CDC researchers are still examining how often this happens.
Under precise laboratory conditions, scientists have shown that monkeypox can spread via aerosols, or tiny airborne particles. But there's no clear evidence that this is happening in the real world, Rasmussen adds. “This is expanding predominantly in communities of males who have sex with men, which suggests skin-to-skin contact,” she explains. If airborne transmission were frequent, she argues, we'd find more occurrences in other demographics.
In the shadow of COVID-19, people are worried about aerosolized monkeypox. Rasmussen believes the epidemiology is different. Different viruses.
Can kids get monkeypox?
More than 80 youngsters have contracted the virus thus far, mainly through household transmission. CDC says pregnant women can spread the illness to their fetus.
Among the 1970s, monkeypox predominantly affected children, but by the 2010s, it was more common in adults, according to a February study. The study's authors say routine smallpox immunization (which protects against monkeypox) halted when smallpox was eradicated. Only toddlers were born after smallpox vaccination halted decades ago. More people are vulnerable now.
Schools and daycares could become monkeypox hotspots, according to pediatric instances. Ogoina adds this hasn't happened in Nigeria's outbreaks, which is encouraging. He says, "I'm not sure if we should worry." We must be careful and seek evidence.

Micah Daigle
3 years ago
Facebook is going away. Here are two explanations for why it hasn't been replaced yet.
And tips for anyone trying.
We see the same story every few years.
BREAKING NEWS: [Platform X] launched a social network. With Facebook's reputation down, the new startup bets millions will switch.
Despite the excitement surrounding each new platform (Diaspora, Ello, Path, MeWe, Minds, Vero, etc.), no major exodus occurred.
Snapchat and TikTok attracted teens with fresh experiences (ephemeral messaging and rapid-fire videos). These features aren't Facebook, even if Facebook replicated them.
Facebook's core is simple: you publish items (typically text/images) and your friends (generally people you know IRL) can discuss them.
It's cool. Sometimes I don't want to, but sh*t. I like it.
Because, well, I like many folks I've met. I enjoy keeping in touch with them and their banter.
I dislike Facebook's corporation. I've been cautiously optimistic whenever a Facebook-killer surfaced.
None succeeded.
Why? Two causes, I think:
People couldn't switch quickly enough, which is reason #1
Your buddies make a social network social.
Facebook started in self-contained communities (college campuses) then grew outward. But a new platform can't.
If we're expected to leave Facebook, we want to know that most of our friends will too.
Most Facebook-killers had bottlenecks. You have to waitlist or jump through hoops (e.g. setting up a server).
Same outcome. Upload. Chirp.
After a week or two of silence, individuals returned to Facebook.
Reason #2: The fundamental experience was different.
Even when many of our friends joined in the first few weeks, it wasn't the same.
There were missing features or a different UX.
Want to reply with a meme? No photos in comments yet. (Trying!)
Want to tag a friend? Nope, sorry. 2019!
Want your friends to see your post? You must post to all your friends' servers. Good luck!
It's difficult to introduce a platform with 100% of the same features as one that's been there for 20 years, yet customers want a core experience.
If you can't, they'll depart.
The causes that led to the causes
Having worked on software teams for 14+ years, I'm not surprised by these challenges. They are a natural development of a few tech sector meta-problems:
Lean startup methodology
Silicon Valley worships lean startup. It's a way of developing software that involves testing a stripped-down version with a limited number of people before selecting what to build.
Billion people use Facebook's functions. They aren't tested. It must work right away*
*This may seem weird to software people, but it's how non-software works! You can't sell a car without wheels.
2. Creativity
Startup entrepreneurs build new things, not copies. I understand. Reinventing the wheel is boring.
We know what works. Different experiences raise adoption friction. Once millions have transferred, more features (and a friendlier UX) can be implemented.
3. Cost scaling
True. Building a product that can sustain hundreds of millions of users in weeks is expensive and complex.
Your lifeboats must have the same capacity as the ship you're evacuating. It's required.
4. Pure ideologies
People who work on Facebook-alternatives are (understandably) critical of Facebook.
They build an open-source, fully-distributed, data-portable, interface-customizable, offline-capable, censorship-proof platform.
Prioritizing these aims can prevent replicating the straightforward experience users expect. Github, not Facebook, is for techies only.
What about the business plan, though?
Facebook-killer attempts have followed three models.
Utilize VC funding to increase your user base, then monetize them later. (If you do this, you won't kill Facebook; instead, Facebook will become you.)
Users must pay to utilize it. (This causes a huge bottleneck and slows the required quick expansion, preventing it from seeming like a true social network.)
Make it a volunteer-run, open-source endeavor that is free. (This typically denotes that something is cumbersome, difficult to operate, and is only for techies.)
Wikipedia is a fourth way.
Wikipedia is one of the most popular websites and a charity. No ads. Donations support them.
A Facebook-killer managed by a good team may gather millions (from affluent contributors and the crowd) for their initial phase of development. Then it might sustain on regular donations, ethical transactions (e.g. fees on commerce, business sites, etc.), and government grants/subsidies (since it would essentially be a public utility).
When you're not aiming to make investors rich, it's remarkable how little money you need.
If you want to build a Facebook competitor, follow these tips:
Drop the lean startup philosophy. Wait until you have a finished product before launching. Build it, thoroughly test it for bugs, and then release it.
Delay innovating. Wait till millions of people have switched before introducing your great new features. Make it nearly identical for now.
Spend money climbing. Make sure that guests can arrive as soon as they are invited. Never keep them waiting. Make things easy for them.
Make it accessible to all. Even if doing so renders it less philosophically pure, it shouldn't require technical expertise to utilize.
Constitute a nonprofit. Additionally, develop community ownership structures. Profit maximization is not the only strategy for preserving valued assets.
Last thoughts
Nobody has killed Facebook, but Facebook is killing itself.
The startup is burying the newsfeed to become a TikTok clone. Meta itself seems to be ditching the platform for the metaverse.
I wish I was happy, but I'm not. I miss (understandably) removed friends' postings and remarks. It could be a ghost town in a few years. My dance moves aren't TikTok-worthy.
Who will lead? It's time to develop a social network for the people.
Greetings if you're working on it. I'm not a company founder, but I like to help hard-working folks.
