Could this happen?

Medium isn't like other social media giants. It wasn't a dot-com startup that became a multi-trillion-dollar social media firm. It launched in 2012 but didn't gain popularity until later. Now, it's one of the largest sites by web traffic, but it's still little compared to most. Most of Medium's traffic is external, but they don't run advertisements, so it's all about memberships.

Medium isn't profitable, but they don't disclose how terrible the problem is. Most of the $163 million they raised has been spent or used for acquisitions. If the money turns off, Medium can't stop paying its writers since the site dies. Writers must be paid, but they can't substantially slash payment without hurting the platform. The existing model needs scale to be viable and has a low ceiling. Facebook and other free social media platforms are struggling to retain users. Here, you must pay to appreciate it, and it's bad for writers AND readers. If I had the same Medium stats on YouTube, I'd make thousands of dollars a month.

Then what? Medium has tried to monetize by offering writers a cut of new members, but that's unsustainable. People-based growth is limited. Imagine recruiting non-Facebook users and getting them to pay to join. Some may, but I'd rather write.

Alternatives:

• Donation buttons

• Tiered subscriptions ($5, $10, $25, etc.)

• Expanding content

and these may be short-term fixes, but they're not as profitable as allowing ads. Advertisements can pay several dollars per click and cents every view. If you get 40,000 views a month like me, that's several thousand instead of a few hundred. Also, Medium would have enough money to split ad revenue with writers, who would make more. I'm among the top 6% of Medium writers. Only 6% of Medium writers make more than $100, and I made $500 with 35,000 views last month. Compared to YouTube, the top 1% of Medium authors make a lot. Mr. Beast and PewDiePie make MILLIONS a month, yet top Medium writers make tens of thousands. Sure, paying 3 or 4 people a few grand, or perhaps tens of thousands, will keep them around. What if great authors leveraged their following to go huge on YouTube and abandoned Medium? If people use Medium to get successful on other platforms, Medium will be continuously cycling through authors and paying them to stay.

Ads might make writing on Medium more profitable than making videos on YouTube because they could preserve the present freemium model and pay users based on internal views. The $5 might be ad-free.

Consider: Would you accept Medium ads? A $5 ad-free version + pay-as-you-go, etc. What are your thoughts on this?

Original post available here

Reflections on the closed company Hola! Dating app

I'll discuss my fuckups as an entrepreneur and CEO. All of them refer to the dating app Hola!, which I co-founded and starred in.

Spring 2021 was when we started. Two techies and two non-techies created a dating app. Pokemon Go and Tinder were combined.

Online dating is a business, and it takes two weeks from a like to a date. We questioned online dating app users if they met anyone offline last year.

75% replied yes, 50% sometimes, 25% usually.

Offline dating is popular, yet people have concerns.

• Men are reluctant to make mistakes in front of others.

• Women are curious about the background of everyone who approaches them.

We designed unique mechanics that let people date after a match. No endless chitchat. Women would be safe while men felt like cowboys.

I wish to emphasize three faults that lead to founders' estrangement.

This detachment ultimately led to us shutting down the company.

The wrong technology stack

Situation

Instead of generating a faster MVP and designing an app in a universal stack for iOS and Android, I argued we should pilot the app separately for iOS and Android. Technical founders' expertise made this possible.

Self-reflection

Mistaken strategy. We lost time and resources developing two apps at once. We chose iOS since it's more profitable. Apple took us out after the release, citing Guideline 4.3 Spam. After 4 months, we had nothing. We had a long way to go to get the app on Android and the Store.

I suggested creating a uniform platform for the company's growth. This makes parallel product development easier. The strategist's lack of experience and knowledge made it a piece of crap.

What would I have changed if I could?

We should have designed an Android universal stack. I expected Apple to have issues with a dating app.

Our approach should have been to launch something and subsequently improve it, but prejudice won.

The lesson

Discuss the IT stack with your CTO. It saves time and money. Choose the easiest MVP method.

2. A tardy search for investments

Situation

Though the universe and other founders encouraged me to locate investors first, I started pitching when we almost had an app.

When angels arrived, it was time to close. The app was banned, war broke out, I left the country, and the other co-founders stayed. We had no savings.

Self-reflection

I loved interviewing users. I'm proud of having done 1,000 interviews. I wanted to understand people's pain points and improve the product.

Interview results no longer affected the product. I was terrified to start pitching. I filled out accelerator applications and redid my presentation. You must go through that so you won't be terrified later.

What would I have changed if I could?

Get an external or internal mentor to help me with my first pitch as soon as possible. I'd be supported if criticized. He'd cheer with me if there was enthusiasm.

In 99% of cases, I'm comfortable jumping into the unknown, but there are exceptions. The mentor's encouragement would have prompted me to act sooner.

The lesson

Begin fundraising immediately. Months may pass. Show investors your pre-MVP project. Draw inferences from feedback.

3. Role ambiguity

Situation

My technical co-founders were also part-time lead developers, which produced communication issues. As co-founders, we communicated well and recognized the problems. Stakes, vesting, target markets, and approach were agreed upon.

We were behind schedule. Technical debt and strategic gap grew.

Bi-daily and weekly reviews didn't help. Each time, there were explanations. Inside, I was freaking out.

Self-reflection

I am a fairly easy person to talk to. I always try to stick to agreements; otherwise, my head gets stuffed with unnecessary information, interpretations, and emotions.

Sit down -> talk -> decide -> do -> evaluate the results. Repeat it.

If I don't get detailed comments, I start ruining everyone's mood. If there's a systematic violation of agreements without a good justification, I won't join the project or I'll end the collaboration.

What would I have done otherwise?

This is where it’s scariest to draw conclusions. Probably the most logical thing would have been not to start the project as we started it. But that was already a completely different project. So I would not have done anything differently and would have failed again.

But I drew conclusions for the future.

The lesson

First-time founders should find an adviser or team coach for a strategic session. It helps split the roles and responsibilities.

We love round numbers, don't we? $100,000 is a frequent freelancing milestone. You feel like six figures means you're doing something properly.

You've most likely already conquered initial freelancing challenges like finding clients, setting fair pricing, coping with criticism, getting through dry spells, managing funds, etc.

You think I must be doing well. Last month, my freelance income topped $100,000.

That may not sound impressive considering I've been freelancing for 2.75 years, but I made 30% of that in the previous four months, which is crazy.

Here are the things I wish I'd known during the early days of self-employment that would have helped me hit $100,000 faster.

1. The Volatility of Freelancing Will Stabilize.

Freelancing is risky. No surprise.

Here's an example.

October 2020 was my best month, earning $7,150. Between $4,004 in September and $1,730 in November. Unsteady.

Freelancing is regrettably like that. Moving clients. Content requirements change. Allocating so much time to personal pursuits wasn't smart, but yet.

Stabilizing income takes time. Consider my rolling three-month average income since I started freelancing. My three-month average monthly income. In February, this metric topped $5,000. Now, it's in the mid-$7,000s, but it took a while to get there.

Finding freelance gigs that provide high pay, high volume, and recurring revenue is difficult. But it's not impossible.

TLDR: Don't expect a steady income increase at first. Be patient.

2. You Have More Value Than You Realize.

Writing is difficult. Assembling words, communicating a message, and provoking action are a puzzle.

People are willing to pay you for it because they can't do what you do or don't have enough time.

Keeping that in mind can have huge commercial repercussions.

When talking to clients, don't tiptoe. You can ignore ridiculous deadlines. You don't have to take unmanageable work.

You solve an issue, so make sure you get rightly paid.

TLDR: Frame services as problem-solutions. This will let you charge more and set boundaries.

3. Increase Your Prices.

I studied hard before freelancing. I read articles and watched videos about writing businesses.

I didn't want to work for pennies. Despite this clarity, I had no real strategy to raise my rates.

I then luckily stumbled into higher-paying work. We discussed fees and hours with a friend who launched a consulting business. It's subjective and speculative because value isn't standardized. One company may laugh at your charges. If your solution helps them create a solid ROI, another client may pay $200 per hour.

When he told me he charged his first client $125 per hour, I thought, Why not?

A new-ish client wanted to discuss a huge forthcoming project, so I raised my rates. They knew my worth, so they didn't blink when I handed them my new number.

TLDR: Increase rates periodically (e.g., every 6 or 12 months). Writing skill develops with practice. You'll gain value over time.

4. Remember Your Limits.

If you can squeeze additional time into a day, let me know. I can't manipulate time yet.

We all have time and economic limits. You could theoretically keep boosting rates, but your prospect pool diminishes. Outsourcing and establishing extra revenue sources might boost monthly revenues.

I've devoted a lot of time to side projects (hopefully extra cash sources), but I've only just started outsourcing. I wish I'd tried this earlier.

If you can discover good freelancers, you can grow your firm without sacrificing time.

TLDR: Expand your writing network immediately. You'll meet freelancers who understand your daily grind and locate reference sources.

5. Every Action You Take Involves an Investment. Be Certain to Select Correctly.

Investing in stocks or crypto requires paying money, right?

In business, time is your currency (and maybe money too). Your daily habits define your future. If you spend time collecting software customers and compiling content in the space, you'll end up with both. So be sure.

I only spend around 50% of my time on client work, therefore it's taken me nearly three years to earn $100,000. I spend the remainder of my time on personal projects including a freelance book, an investment newsletter, and this blog.

Why? I don't want to rely on client work forever. So, I'm working on projects that could pay off later and help me live a more fulfilling life.

TLDR: Consider the long-term impact of your time commitments, and don't overextend. You can only make so many "investments" in a given time.

6. LinkedIn Is an Endless Mine of Gold. Use It.

Why didn't I use LinkedIn earlier?

I designed a LinkedIn inbound lead strategy that generates 12 leads a month and a few high-quality offers. As a result, I've turned down good gigs. Wish I'd begun earlier.

If you want to create a freelance business, prioritize LinkedIn. Too many freelancers ignore this site, missing out on high-paying clients. Build your profile, post often, and interact.

TLDR: Study LinkedIn's top creators. Once you understand their audiences, start posting and participating daily.

For 99% of People, Freelancing is Not a Get-Rich-Quick Scheme.

Here's a list of things I wish I'd known when I started freelancing.

1. Although it is erratic, freelancing eventually becomes stable.

2. You deserve respect and discretion over how you conduct business because you have solved an issue.

3. Increase your charges rather than undervaluing yourself. If necessary, add a reminder to your calendar. Your worth grows with time.

4. In order to grow your firm, outsource jobs. After that, you can work on the things that are most important to you.

5. Take into account how your present time commitments may affect the future. It will assist in putting things into perspective and determining whether what you are doing is indeed worthwhile.

6. Participate on LinkedIn. You'll get better jobs as a result.

If I could give my old self (and other freelancers) one bit of advice, it's this:

Despite appearances, you're making progress.

Each job. Tweets. Newsletters. Progress. It's simpler to see retroactively than in the moment.

Consistent, intentional work pays off. No good comes from doing nothing. You must set goals, divide them into time-based targets, and then optimize your calendar.

Then you'll understand you're doing well.

Want to learn more? I’ll teach you.

As technology develops, you should stay up to date

You don't want to fall behind, do you? This post covers 7 tech-related things you should know.

You'll learn how to operate your computer (and other electronic devices) like an expert and how to leverage the Internet and social media to create your brand and business. Read on to stay relevant in today's tech-driven environment.

You must learn how to code.

Future-language is coding. It's how we and computers talk. Learn coding to keep ahead.

Try Codecademy or Code School. There are also numerous free courses like Coursera or Udacity, but they take a long time and aren't necessarily self-paced, so it can be challenging to find the time.

Artificial intelligence (AI) will transform all jobs.

Our skillsets must adapt with technology. AI is a must-know topic. AI will revolutionize every employment due to advances in machine learning.

Here are seven AI subjects you must know.

What is artificial intelligence?

How does artificial intelligence work?

What are some examples of AI applications?

How can I use artificial intelligence in my day-to-day life?

What jobs have a high chance of being replaced by artificial intelligence and how can I prepare for this?

Can machines replace humans? What would happen if they did?

How can we manage the social impact of artificial intelligence and automation on human society and individual people?

Blockchain Is Changing the Future

Few of us know how Bitcoin and blockchain technology function or what impact they will have on our lives. Blockchain offers safe, transparent, tamper-proof transactions.

It may alter everything from business to voting. Seven must-know blockchain topics:

1. Describe blockchain.

2. How does the blockchain function?

3. What advantages does blockchain offer?

4. What possible uses for blockchain are there?

5. What are the dangers of blockchain technology?

6. What are my options for using blockchain technology?

7. What does blockchain technology's future hold?

Cryptocurrencies are here to stay

Cryptocurrencies employ cryptography to safeguard transactions and manage unit creation. Decentralized cryptocurrencies aren't controlled by governments or financial institutions.

Bitcoin, the first cryptocurrency, was launched in 2009. Cryptocurrencies can be bought and sold on decentralized exchanges.

Bitcoin is here to stay.

Bitcoin isn't a fad, despite what some say. Since 2009, Bitcoin's popularity has grown. Bitcoin is worth learning about now. Since 2009, Bitcoin has developed steadily.

With other cryptocurrencies emerging, many people are wondering if Bitcoin still has a bright future. Curiosity is natural. Millions of individuals hope their Bitcoin investments will pay off since they're popular now.

Thankfully, they will. Bitcoin is still running strong a decade after its birth. Here's why.

The Internet of Things (IoT) is no longer just a trendy term.

IoT consists of internet-connected physical items. These items can share data. IoT is young but developing fast.

20 billion IoT-connected devices are expected by 2023. So much data! All IT teams must keep up with quickly expanding technologies. Four must-know IoT topics:

1. Recognize the fundamentals: Priorities first! Before diving into more technical lingo, you should have a fundamental understanding of what an IoT system is. Before exploring how something works, it's crucial to understand what you're working with.

2. Recognize Security: Security does not stand still, even as technology advances at a dizzying pace. As IT professionals, it is our duty to be aware of the ways in which our systems are susceptible to intrusion and to ensure that the necessary precautions are taken to protect them.

3. Be able to discuss cloud computing: The cloud has seen various modifications over the past several years once again. The use of cloud computing is also continually changing. Knowing what kind of cloud computing your firm or clients utilize will enable you to make the appropriate recommendations.

4. Bring Your Own Device (BYOD)/Mobile Device Management (MDM) is a topic worth discussing (MDM). The ability of BYOD and MDM rules to lower expenses while boosting productivity among employees who use these services responsibly is a major factor in their continued growth in popularity.

IoT Security is key

As more gadgets connect, they must be secure. IoT security includes securing devices and encrypting data. Seven IoT security must-knows:

1. fundamental security ideas

2. Authorization and identification

3. Cryptography

4. electronic certificates

5. electronic signatures

6. Private key encryption

7. Public key encryption

Final Thoughts

With so much going on in the globe, it can be hard to stay up with technology. We've produced a list of seven tech must-knows.

Python Volatility-Based Catapult Indicator

As a catapult, this technical indicator uses three systems: Volatility (the fulcrum), Momentum (the propeller), and a Directional Filter (Acting as the support). The goal is to get a signal that predicts volatility acceleration and direction based on historical patterns. We want to know when the market will move. and where. This indicator outperforms standard indicators.

Knowledge must be accessible to everyone. This is why my new publications Contrarian Trading Strategies in Python and Trend Following Strategies in Python now include free PDF copies of my first three books (Therefore, purchasing one of the new books gets you 4 books in total). GitHub-hosted advanced indications and techniques are in the two new books above.

The Foundation: Volatility

The Catapult predicts significant changes with the 21-period Relative Volatility Index.

The Average True Range, Mean Absolute Deviation, and Standard Deviation all assess volatility. Standard Deviation will construct the Relative Volatility Index.

Standard Deviation is the most basic volatility. It underpins descriptive statistics and technical indicators like Bollinger Bands. Before calculating Standard Deviation, let's define Variance.

Variance is the squared deviations from the mean (a dispersion measure). We take the square deviations to compel the distance from the mean to be non-negative, then we take the square root to make the measure have the same units as the mean, comparing apples to apples (mean to standard deviation standard deviation). Variance formula:

As stated, standard deviation is:

# The function to add a number of columns inside an array
def adder(Data, times):
    
    for i in range(1, times + 1):
    
        new_col = np.zeros((len(Data), 1), dtype = float)
        Data = np.append(Data, new_col, axis = 1)
        
    return Data

# The function to delete a number of columns starting from an index
def deleter(Data, index, times):
    
    for i in range(1, times + 1):
    
        Data = np.delete(Data, index, axis = 1)
        
    return Data
    
# The function to delete a number of rows from the beginning
def jump(Data, jump):
    
    Data = Data[jump:, ]
    
    return Data

# Example of adding 3 empty columns to an array
my_ohlc_array = adder(my_ohlc_array, 3)

# Example of deleting the 2 columns after the column indexed at 3
my_ohlc_array = deleter(my_ohlc_array, 3, 2)

# Example of deleting the first 20 rows
my_ohlc_array = jump(my_ohlc_array, 20)

# Remember, OHLC is an abbreviation of Open, High, Low, and Close and it refers to the standard historical data file

def volatility(Data, lookback, what, where):
    
  for i in range(len(Data)):

     try:

        Data[i, where] = (Data[i - lookback + 1:i + 1, what].std())
     except IndexError:
        pass
        
  return Data

The RSI is the most popular momentum indicator, and for good reason—it excels in range markets. Its 0–100 range simplifies interpretation. Fame boosts its potential.

The more traders and portfolio managers look at the RSI, the more people will react to its signals, pushing market prices. Technical Analysis is self-fulfilling, therefore this theory is obvious yet unproven.

RSI is determined simply. Start with one-period pricing discrepancies. We must remove each closing price from the previous one. We then divide the smoothed average of positive differences by the smoothed average of negative differences. The RSI algorithm converts the Relative Strength from the last calculation into a value between 0 and 100.

def ma(Data, lookback, close, where): 
    
    Data = adder(Data, 1)
    
    for i in range(len(Data)):
           
            try:
                Data[i, where] = (Data[i - lookback + 1:i + 1, close].mean())
            
            except IndexError:
                pass
            
    # Cleaning
    Data = jump(Data, lookback)
    
    return Data
def ema(Data, alpha, lookback, what, where):
    
    alpha = alpha / (lookback + 1.0)
    beta  = 1 - alpha
    
    # First value is a simple SMA
    Data = ma(Data, lookback, what, where)
    
    # Calculating first EMA
    Data[lookback + 1, where] = (Data[lookback + 1, what] * alpha) + (Data[lookback, where] * beta)    
 
    # Calculating the rest of EMA
    for i in range(lookback + 2, len(Data)):
            try:
                Data[i, where] = (Data[i, what] * alpha) + (Data[i - 1, where] * beta)
        
            except IndexError:
                pass
            
    return Datadef rsi(Data, lookback, close, where, width = 1, genre = 'Smoothed'):
    
    # Adding a few columns
    Data = adder(Data, 7)
    
    # Calculating Differences
    for i in range(len(Data)):
        
        Data[i, where] = Data[i, close] - Data[i - width, close]
     
    # Calculating the Up and Down absolute values
    for i in range(len(Data)):
        
        if Data[i, where] > 0:
            
            Data[i, where + 1] = Data[i, where]
            
        elif Data[i, where] < 0:
            
            Data[i, where + 2] = abs(Data[i, where])
            
    # Calculating the Smoothed Moving Average on Up and Down
    absolute values        
                             
    lookback = (lookback * 2) - 1 # From exponential to smoothed
    Data = ema(Data, 2, lookback, where + 1, where + 3)
    Data = ema(Data, 2, lookback, where + 2, where + 4)
    
    # Calculating the Relative Strength
    Data[:, where + 5] = Data[:, where + 3] / Data[:, where + 4]
    
    # Calculate the Relative Strength Index
    Data[:, where + 6] = (100 - (100 / (1 + Data[:, where + 5])))  
    
    # Cleaning
    Data = deleter(Data, where, 6)
    Data = jump(Data, lookback)

    return Data

def relative_volatility_index(Data, lookback, close, where):

    # Calculating Volatility
    Data = volatility(Data, lookback, close, where)
    
    # Calculating the RSI on Volatility
    Data = rsi(Data, lookback, where, where + 1) 
    
    # Cleaning
    Data = deleter(Data, where, 1)
    
    return Data

The Arm Section: Speed

The Catapult predicts momentum direction using the 14-period Relative Strength Index.

As a reminder, the RSI ranges from 0 to 100. Two levels give contrarian signals:

• A positive response is anticipated when the market is deemed to have gone too far down at the oversold level 30, which is 30.

• When the market is deemed to have gone up too much, at overbought level 70, a bearish reaction is to be expected.

Comparing the RSI to 50 is another intriguing use. RSI above 50 indicates bullish momentum, while below 50 indicates negative momentum.

The direction-finding filter in the frame

The Catapult's directional filter uses the 200-period simple moving average to keep us trending. This keeps us sane and increases our odds.

Moving averages confirm and ride trends. Its simplicity and track record of delivering value to analysis make them the most popular technical indicator. They help us locate support and resistance, stops and targets, and the trend. Its versatility makes them essential trading tools.

This is the plain mean, employed in statistics and everywhere else in life. Simply divide the number of observations by their total values. Mathematically, it's:

We defined the moving average function above. Create the Catapult indication now.

Indicator of the Catapult

The indicator is a healthy mix of the three indicators:

• The first trigger will be provided by the 21-period Relative Volatility Index, which indicates that there will now be above average volatility and, as a result, it is possible for a directional shift.

• If the reading is above 50, the move is likely bullish, and if it is below 50, the move is likely bearish, according to the 14-period Relative Strength Index, which indicates the likelihood of the direction of the move.

• The likelihood of the move's direction will be strengthened by the 200-period simple moving average. When the market is above the 200-period moving average, we can infer that bullish pressure is there and that the upward trend will likely continue. Similar to this, if the market falls below the 200-period moving average, we recognize that there is negative pressure and that the downside is quite likely to continue.

lookback_rvi = 21
lookback_rsi = 14
lookback_ma  = 200
my_data = ma(my_data, lookback_ma, 3, 4)
my_data = rsi(my_data, lookback_rsi, 3, 5)
my_data = relative_volatility_index(my_data, lookback_rvi, 3, 6)

Two-handled overlay indicator Catapult. The first exhibits blue and green arrows for a buy signal, and the second shows blue and red for a sell signal.

The chart below shows recent EURUSD hourly values.

def signal(Data, rvi_col, signal):
    
    Data = adder(Data, 10)
        
    for i in range(len(Data)):
            
        if Data[i,     rvi_col] < 30 and \
           Data[i - 1, rvi_col] > 30 and \
           Data[i - 2, rvi_col] > 30 and \
           Data[i - 3, rvi_col] > 30 and \
           Data[i - 4, rvi_col] > 30 and \
           Data[i - 5, rvi_col] > 30:
               
               Data[i, signal] = 1
                           
    return Data

Signals are straightforward. The indicator can be utilized with other methods.

my_data = signal(my_data, 6, 7)

Lumiwealth shows how to develop all kinds of algorithms. I recommend their hands-on courses in algorithmic trading, blockchain, and machine learning.

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.

After you find a trading method or approach, follow these steps:

• Put emotions aside and adopt an analytical perspective.

• Test it in the past in conditions and simulations taken from real life.

• Try improving it and performing a forward test if you notice any possibility.

• Transaction charges and any slippage simulation should always be included in your tests.

• Risk management and position sizing should always be included in your tests.

After checking the aforementioned, monitor the plan because market dynamics may change and render it unprofitable.

UST depegs and LUNA crashes 99.999% in a fraction of the time it takes the Moon to orbit the Earth.

Fat Man, a Terra whistle-blower, promises to expose Do Kwon's dirty secrets and shady deals.

The Terra community has voted to relaunch Terra LUNA on a new blockchain. The Terra 2.0 Pheonix-1 blockchain went live on May 28, 2022, and people were airdropped the new LUNA, now called LUNA, while the old LUNA became LUNA Classic.

Does LUNA deserve another chance? To answer this, or at least start a conversation about the Terra 2.0 chain's advantages and limitations, we must assess its fundamentals, ideology, and long-term vision.

Whatever the result, our analysis must be thorough and ruthless. A failure of this magnitude cannot happen again, so we must magnify every potential breaking point by 10.

Will UST and LUNA holders be compensated in full?

The obvious. First, and arguably most important, is to restore previous UST and LUNA holders' bags.

Terra 2.0 has 1,000,000,000,000 tokens to distribute.

• 25% of a community pool

• Holders of pre-attack LUNA: 35%

• 10% of aUST holders prior to attack

• Holders of LUNA after an attack: 10%

• UST holders as of the attack: 20%

Every LUNA and UST holder has been compensated according to the above proposal.

According to self-reported data, the new chain has 210.000.000 tokens and a $1.3bn marketcap. LUNC and UST alone lost $40bn. The new token must fill this gap. Since launch:

LUNA holders collectively own $1b worth of LUNA if we subtract the 25% community pool airdrop from the current market cap and assume airdropped LUNA was never sold.

At the current supply, the chain must grow 40 times to compensate holders. At the current supply, LUNA must reach $240.

LUNA needs a full-on Bull Market to make LUNC and UST holders whole.

Who knows if you'll be whole? From the time you bought to the amount and price, there are too many variables to determine if Terra can cover individual losses.

The above distribution doesn't consider individual cases. Terra didn't solve individual cases. It would have been huge.

What does LUNA offer in terms of value?

UST's marketcap peaked at $18bn, while LUNC's was $41bn. LUNC and UST drove the Terra chain's value.

After it was confirmed (again) that algorithmic stablecoins are bad, Terra 2.0 will no longer support them.

Algorithmic stablecoins contributed greatly to Terra's growth and value proposition. Terra 2.0 has no product without algorithmic stablecoins.

Terra 2.0 has an identity crisis because it has no actual product. It's like Volkswagen faking carbon emission results and then stopping car production.

A project that has already lost the trust of its users and nearly all of its value cannot survive without a clear and in-demand use case.

Do Kwon, how about him?

Oh, the Twitter-caller-poor? Who challenges crypto billionaires to break his LUNA chain? Who dissolved Terra Labs South Korea before depeg? Arrogant guy?

That's not a good image for LUNA, especially when making amends. I think he should step down and let a nicer person be Terra 2.0's frontman.

The verdict

Terra has a terrific community with an arrogant, unlikeable leader. The new LUNA chain must grow 40 times before it can start making up its losses, and even then, not everyone's losses will be covered.

I won't invest in Terra 2.0 or other algorithmic stablecoins in the near future. I won't be near any Do Kwon-related project within 100 miles. My opinion.

Can Terra 2.0 be saved? Comment below.