Perovskite solar cells will revolutionize everything.

Humanity is in a climatic Armageddon. Our widespread ecological crimes of the previous century are catching up with us, and planet-scale karma threatens everyone. We must adjust to new technologies and lifestyles to avoid this fate. Even solar power, a renewable energy source, has climate problems. A recent discovery could boost solar power's eco-friendliness and affordability. Perovskite solar cells are amazing.

Perovskite is a silicon-like semiconductor. Semiconductors are used to make computer chips, LEDs, camera sensors, and solar cells. Silicon makes sturdy and long-lasting solar cells, thus it's used in most modern solar panels.

Perovskite solar cells are far better. First, they're easy to make at room temperature, unlike silicon cells, which require long, intricate baking processes. This makes perovskite cells cheaper to make and reduces their carbon footprint. Perovskite cells are efficient. Most silicon panel solar farms are 18% efficient, meaning 18% of solar radiation energy is transformed into electricity. Perovskite cells are 25% efficient, making them 38% more efficient than silicon.

However, perovskite cells are nowhere near as durable. A normal silicon panel will lose efficiency after 20 years. The first perovskite cells were ineffective since they lasted barely minutes.

Recent research from Princeton shows that perovskite cells can endure 30 years. The cells kept their efficiency, therefore no sacrifices were made.

No electrical or chemical engineer here, thus I can't explain how they did it. But strangely, the team said longevity isn't the big deal. In the next years, perovskite panels will become longer-lasting. How do you test a panel if you only have a month or two? This breakthrough technique needs a uniform method to estimate perovskite life expectancy fast. The study's key milestone was establishing a standard procedure.

Lab-based advanced aging tests are their solution. Perovskite cells decay faster at higher temperatures, so scientists can extrapolate from that. The test heated the panel to 110 degrees and waited for its output to reduce by 20%. Their panel lasted 2,100 hours (87.5 days) before a 20% decline.

They did some math to extrapolate this data and figure out how long the panel would have lasted in different climates, and were shocked to find it would last 30 years in Princeton. This made perovskite panels as durable as silicon panels. This panel could theoretically be sold today.

This technology will soon allow these brilliant panels to be released into the wild. This technology could be commercially viable in ten, maybe five years.

Solar power will be the best once it does. Solar power is cheap and low-carbon. Perovskite is the cheapest renewable energy source if we switch to it. Solar panel manufacturing's carbon footprint will also drop.

Perovskites' impact goes beyond cost and carbon. Silicon panels require harmful mining and contain toxic elements (cadmium). Perovskite panels don't require intense mining or horrible materials, making their production and expiration more eco-friendly.

Solar power destroys habitat. Massive solar farms could reduce biodiversity and disrupt local ecology by destroying vital habitats. Perovskite cells are more efficient, so they can shrink a solar farm while maintaining energy output. This reduces land requirements, making perovskite solar power cheaper, and could reduce solar's environmental impact.

Perovskite solar power is scalable and environmentally friendly. Princeton scientists will speed up the development and rollout of this energy.

Why bother with fusion, fast reactors, SMRs, or traditional nuclear power? We're close to developing a nearly perfect environmentally friendly power source, and we have the tools and systems to do so quickly. It's also affordable, so we can adopt it quickly and let the developing world use it to grow. Even I struggle to justify spending billions on fusion when a great, cheap technology outperforms it. Perovskite's eco-credentials and cost advantages could save the world and power humanity's future.

Better technology and lower launch costs revive science-fiction tech.

Airbus engineers showed off sustainable energy's future in Munich last month. They captured sunlight with solar panels, turned it into microwaves, and beamed it into an airplane hangar, where it lighted a city model. The test delivered 2 kW across 36 meters, but it posed a serious question: Should we send enormous satellites to capture solar energy in space? In orbit, free of clouds and nighttime, they could create power 24/7 and send it to Earth.

Airbus engineer Jean-Dominique Coste calls it an engineering problem. “But it’s never been done at [large] scale.”

Proponents of space solar power say the demand for green energy, cheaper space access, and improved technology might change that. Once someone invests commercially, it will grow. Former NASA researcher John Mankins says it might be a trillion-dollar industry.

Myriad uncertainties remain, including whether beaming gigawatts of power to Earth can be done efficiently and without burning birds or people. Concept papers are being replaced with ground and space testing. The European Space Agency (ESA), which supported the Munich demo, will propose ground tests to member nations next month. The U.K. government offered £6 million to evaluate innovations this year. Chinese, Japanese, South Korean, and U.S. agencies are working. NASA policy analyst Nikolai Joseph, author of an upcoming assessment, thinks the conversation's tone has altered. What formerly appeared unattainable may now be a matter of "bringing it all together"

NASA studied space solar power during the mid-1970s fuel crunch. A projected space demonstration trip using 1970s technology would have cost $1 trillion. According to Mankins, the idea is taboo in the agency.

Space and solar power technology have evolved. Photovoltaic (PV) solar cell efficiency has increased 25% over the past decade, Jones claims. Telecoms use microwave transmitters and receivers. Robots designed to repair and refuel spacecraft might create solar panels.

Falling launch costs have boosted the idea. A solar power satellite large enough to replace a nuclear or coal plant would require hundreds of launches. ESA scientist Sanjay Vijendran: "It would require a massive construction complex in orbit."

SpaceX has made the idea more plausible. A SpaceX Falcon 9 rocket costs $2600 per kilogram, less than 5% of what the Space Shuttle did, and the company promised $10 per kilogram for its giant Starship, slated to launch this year. Jones: "It changes the equation." "Economics rules"

Mass production reduces space hardware costs. Satellites are one-offs made with pricey space-rated parts. Mars rover Perseverance cost $2 million per kilogram. SpaceX's Starlink satellites cost less than $1000 per kilogram. This strategy may work for massive space buildings consisting of many identical low-cost components, Mankins has long contended. Low-cost launches and "hypermodularity" make space solar power economical, he claims.

Better engineering can improve economics. Coste says Airbus's Munich trial was 5% efficient, comparing solar input to electricity production. When the Sun shines, ground-based solar arrays perform better. Studies show space solar might compete with existing energy sources on price if it reaches 20% efficiency.

Lighter parts reduce costs. "Sandwich panels" with PV cells on one side, electronics in the middle, and a microwave transmitter on the other could help. Thousands of them build a solar satellite without heavy wiring to move power. In 2020, a team from the U.S. Naval Research Laboratory (NRL) flew on the Air Force's X-37B space plane.

NRL project head Paul Jaffe said the satellite is still providing data. The panel converts solar power into microwaves at 8% efficiency, but not to Earth. The Air Force expects to test a beaming sandwich panel next year. MIT will launch its prototype panel with SpaceX in December.

As a satellite orbits, the PV side of sandwich panels sometimes faces away from the Sun since the microwave side must always face Earth. To maintain 24-hour power, a satellite needs mirrors to keep that side illuminated and focus light on the PV. In a 2012 NASA study by Mankins, a bowl-shaped device with thousands of thin-film mirrors focuses light onto the PV array.

International Electric Company's Ian Cash has a new strategy. His proposed satellite uses enormous, fixed mirrors to redirect light onto a PV and microwave array while the structure spins (see graphic, above). 1 billion minuscule perpendicular antennas act as a "phased array" to electronically guide the beam toward Earth, regardless of the satellite's orientation. This design, argues Cash, is "the most competitive economically"

If a space-based power plant ever flies, its power must be delivered securely and efficiently. Jaffe's team at NRL just beamed 1.6 kW over 1 km, and teams in Japan, China, and South Korea have comparable attempts. Transmitters and receivers lose half their input power. Vijendran says space solar beaming needs 75% efficiency, "preferably 90%."

Beaming gigawatts through the atmosphere demands testing. Most designs aim to produce a beam kilometers wide so every ship, plane, human, or bird that strays into it only receives a tiny—hopefully harmless—portion of the 2-gigawatt transmission. Receiving antennas are cheap to build but require a lot of land, adds Jones. You could grow crops under them or place them offshore.

Europe's public agencies currently prioritize space solar power. Jones: "There's a devotion you don't see in the U.S." ESA commissioned two solar cost/benefit studies last year. Vijendran claims it might match ground-based renewables' cost. Even at a higher price, equivalent to nuclear, its 24/7 availability would make it competitive.

ESA will urge member states in November to fund a technical assessment. If the news is good, the agency will plan for 2025. With €15 billion to €20 billion, ESA may launch a megawatt-scale demonstration facility by 2030 and a gigawatt-scale facility by 2040. "Moonshot"

The Kardashev scale grades civilizations from Type 1 to Type 3 based on energy harvesting.

How do technologically proficient civilizations emerge across timescales measuring in the tens of thousands or even millions of years? This is a question that worries me as a researcher in the search for “technosignatures” from other civilizations on other worlds. Since it is already established that longer-lived civilizations are the ones we are most likely to detect, knowing something about their prospective evolutionary trajectories could be translated into improved search tactics. But even more than knowing what to seek for, what I really want to know is what happens to a society after so long time. What are they capable of? What do they become?

This was the question Russian SETI pioneer Nikolai Kardashev asked himself back in 1964. His answer was the now-famous “Kardashev Scale.” Kardashev was the first, although not the last, scientist to try and define the processes (or stages) of the evolution of civilizations. Today, I want to launch a series on this question. It is crucial to technosignature studies (of which our NASA team is hard at work), and it is also important for comprehending what might lay ahead for mankind if we manage to get through the bottlenecks we have now.

The Kardashev scale

Kardashev’s question can be expressed another way. What milestones in a civilization’s advancement up the ladder of technical complexity will be universal? The main notion here is that all (or at least most) civilizations will pass through some kind of definable stages as they progress, and some of these steps might be mirrored in how we could identify them. But, while Kardashev’s major focus was identifying signals from exo-civilizations, his scale gave us a clear way to think about their evolution.

The classification scheme Kardashev employed was not based on social systems of ethics because they are something that we can probably never predict about alien cultures. Instead, it was built on energy, which is something near and dear to the heart of everybody trained in physics. Energy use might offer the basis for universal stages of civilisation progression because you cannot do the work of establishing a civilization without consuming energy. So, Kardashev looked at what energy sources were accessible to civilizations as they evolved technologically and used those to build his scale.

From Kardashev’s perspective, there are three primary levels or “types” of advancement in terms of harvesting energy through which a civilization should progress.

Type 1: Civilizations that can capture all the energy resources of their native planet constitute the first stage. This would imply capturing all the light energy that falls on a world from its host star. This makes it reasonable, given solar energy will be the largest source available on most planets where life could form. For example, Earth absorbs hundreds of atomic bombs’ worth of energy from the Sun every second. That is a rather formidable energy source, and a Type 1 race would have all this power at their disposal for civilization construction.

Type 2: These civilizations can extract the whole energy resources of their home star. Nobel Prize-winning scientist Freeman Dyson famously anticipated Kardashev’s thinking on this when he imagined an advanced civilization erecting a large sphere around its star. This “Dyson Sphere” would be a machine the size of the complete solar system for gathering stellar photons and their energy.

Type 3: These super-civilizations could use all the energy produced by all the stars in their home galaxy. A normal galaxy has a few hundred billion stars, so that is a whole lot of energy. One way this may be done is if the civilization covered every star in their galaxy with Dyson spheres, but there could also be more inventive approaches.

Implications of the Kardashev scale

Climbing from Type 1 upward, we travel from the imaginable to the god-like. For example, it is not hard to envisage utilizing lots of big satellites in space to gather solar energy and then beaming that energy down to Earth via microwaves. That would get us to a Type 1 civilization. But creating a Dyson sphere would require chewing up whole planets. How long until we obtain that level of power? How would we have to change to get there? And once we get to Type 3 civilizations, we are virtually thinking about gods with the potential to engineer the entire cosmos.

For me, this is part of the point of the Kardashev scale. Its application for thinking about identifying technosignatures is crucial, but even more strong is its capacity to help us shape our imaginations. The mind might become blank staring across hundreds or thousands of millennia, and so we need tools and guides to focus our attention. That may be the only way to see what life might become — what we might become — once it arises to start out beyond the boundaries of space and time and potential.

This is a summary. Read the full article here.

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.

A must-read for everyone

Jobs is well-known. You probably know the tall, thin guy who wore the same clothing every day. His influence is unavoidable. In fewer than 40 years, Jobs' innovations have impacted computers, movies, cellphones, music, and communication.

Steve Jobs may be more imaginative than the typical person, but if we can use some of his ingenuity, ambition, and good traits, we'll be successful. This essay explains how to follow his guidance and success secrets.

1. Repetition is necessary for success.

Be patient and diligent to master something. Practice makes perfect. This is why older workers are often more skilled.

When should you repeat a task? When you're confident and excited to share your product. It's when to stop tweaking and repeating.

Jobs stated he'd make the crowd sh** their pants with an iChat demo.

Use this in your daily life.

• Start with the end in mind. You can put it in writing and be as detailed as you like with your plan's schedule and metrics. For instance, you have a goal of selling three coffee makers in a week.

• Break it down, break the goal down into particular tasks you must complete, and then repeat those tasks. To sell your coffee maker, you might need to make 50 phone calls.

• Be mindful of the amount of work necessary to produce the desired results. Continue doing this until you are happy with your product.

2. Acquire the ability to add and subtract.

How did Picasso invent cubism? Pablo Picasso was influenced by stylised, non-naturalistic African masks that depict a human figure.

Artists create. Constantly seeking inspiration. They think creatively about random objects. Jobs said creativity is linking things. Creative people feel terrible when asked how they achieved something unique because they didn't do it all. They saw innovation. They had mastered connecting and synthesizing experiences.

Use this in your daily life.

• On your phone, there is a note-taking app. Ideas for what you desire to learn should be written down. It may be learning a new language, calligraphy, or anything else that inspires or intrigues you.

• Note any ideas you have, quotations, or any information that strikes you as important.

• Spend time with smart individuals, that is the most important thing. Jim Rohn, a well-known motivational speaker, has observed that we are the average of the five people with whom we spend the most time.

• Learning alone won't get you very far. You need to put what you've learnt into practice. If you don't use your knowledge and skills, they are useless.

3. Develop the ability to refuse.

Steve Jobs deleted thousands of items when he created Apple's design ethic. Saying no to distractions meant upsetting customers and partners.

John Sculley, the former CEO of Apple, said something like this. According to Sculley, Steve’s methodology differs from others as he always believed that the most critical decisions are things you choose not to do.

Use this in your daily life.

• Never be afraid to say "no," "I won't," or "I don't want to." Keep it simple. This method works well in some situations.

• Give a different option. For instance, X might be interested even if I won't be able to achieve it.

• Control your top priority. Before saying yes to anything, make sure your work schedule and priority list are up to date.

4. Follow your passion

“Follow your passion” is the worst advice people can give you. Steve Jobs didn't start Apple because he suddenly loved computers. He wanted to help others attain their maximum potential.

Great things take a lot of work, so quitting makes sense if you're not passionate. Jobs learned from history that successful people were passionate about their work and persisted through challenges.

Use this in your daily life.

• Stay away from your passion. Allow it to develop daily. Keep working at your 9-5-hour job while carefully gauging your level of desire and endurance. Less risk exists.

• The truth is that if you decide to work on a project by yourself rather than in a group, it will take you years to complete it instead of a week. Instead, network with others who have interests in common.

• Prepare a fallback strategy in case things go wrong.

Success, this small two-syllable word eventually gives your life meaning, a perspective. What is success?  For most, it's achieving their ambitions. However, there's a catch. Successful people aren't always happy.

Furthermore, where do people’s goals and achievements end? It’s a never-ending process. Success is a journey, not a destination. We wish you not to lose your way on this journey.

Without self-control, you'll waste time chasing dopamine fixes.

I found a powerful quote in Tony Robbins' Awaken The Giant Within:

“Most of the challenges that we have in our personal lives come from a short-term focus” — Tony Robbins

Most people are short-term oriented, but highly successful people are long-term oriented.

Successful people act in line with their long-term goals and values, while the rest are distracted by short-term pleasures and dopamine fixes.

Instant gratification wrecks lives

Instant pleasure is fleeting. Quickly fading effects leave you craving more stimulation.

Before you know it, you're in a cycle of quick fixes. This explains binging on food, social media, and Netflix.

These things cause a dopamine spike, which is entertaining. This dopamine spike crashes quickly, leaving you craving more stimulation.

It's fine to watch TV or play video games occasionally. Problems arise when brain impulses aren't controlled. You waste hours chasing dopamine fixes.

Instant gratification becomes problematic when it interferes with long-term goals, happiness, and life fulfillment.

Most rewarding things require delay

Life's greatest rewards require patience and delayed gratification. They must be earned through patience, consistency, and effort.

Ex:

• A fit, healthy body

• A deep connection with your spouse

• A thriving career/business

• A healthy financial situation

These are some of life's most rewarding things, but they take work and patience. They all require the ability to delay gratification.

To have a healthy bank account, you must save (and invest) a large portion of your monthly income. This means no new tech or clothes.

If you want a fit, healthy body, you must eat better and exercise three times a week. So no fast food and Netflix.

It's a battle between what you want now and what you want most.

Successful people choose what they want most over what they want now. It's a major difference.

Instant vs. delayed gratification

Most people subconsciously prefer instant rewards over future rewards, even if the future rewards are more significant.

We humans aren't logical. Emotions and instincts drive us. So we act against our goals and values.

Fortunately, instant gratification bias can be overridden. This is a modern superpower. Effective methods include:

#1: Train your brain to handle overstimulation

Training your brain to function without constant stimulation is a powerful change. Boredom can lead to long-term rewards.

Unlike impulsive shopping, saving money is boring. Having lots of cash is amazing.

Compared to video games, deep work is boring. A successful online business is rewarding.

Reading books is boring compared to scrolling through funny videos on social media. Knowledge is invaluable.

You can't do these things if your brain is overstimulated. Your impulses will control you. To reduce overstimulation addiction, try:

• Daily meditation (10 minutes is enough)

• Daily study/work for 90 minutes (no distractions allowed)

• First hour of the day without phone, social media, and Netflix

• Nature walks, journaling, reading, sports, etc.

#2: Make Important Activities Less Intimidating

Instant gratification helps us cope with stress. Starting a book or business can be intimidating. Video games and social media offer a quick escape in such situations.

Make intimidating tasks less so. Break them down into small tasks. Start a new business/side-hustle by:

• Get domain name

• Design website

• Write out a business plan

• Research competition/peers

• Approach first potential client

Instead of one big mountain, divide it into smaller sub-tasks. This makes a task easier and less intimidating.

#3: Plan ahead for important activities

Distractions will invade unplanned time. Your time is dictated by your impulses, which are usually Netflix, social media, fast food, and video games. It wants quick rewards and dopamine fixes.

Plan your days and be proactive with your time. Studies show that scheduling activities makes you 3x more likely to do them.

To achieve big goals, you must plan. Don't gamble.

Want to get fit? Schedule next week's workouts. Want a side-job? Schedule your work time.