A reading list for successful people

Elon Musk reads and talks. So, one learns. Many brilliant individuals & amazing literature.

This article recommends 3 Elon Musk novels. All of them helped me succeed. Hope they'll help you.

Douglas Adams's The Hitchhiker's Guide to the Galaxy

Page Count: 193
Rating on Goodreads: 4.23

Arthur Dent is pulled off Earth by a buddy seconds before it's razed for a cosmic motorway. The trio hitchhikes through space and gets into problems.

I initially read Hitchhiker's as a child. To evade my mum, I'd read with a flashlight under the covers. She'd scold at me for not sleeping on school nights when she found out. Oops.

The Hitchhiker's Guide to the Galaxy is lighthearted science fiction.

My favorite book quotes are:

• “Space is big. You won’t believe how vastly, hugely, mind-bogglingly big it is. I mean, you may think it’s a long way down the road to the chemist’s, but that’s just peanuts to space.”

• “Far out in the uncharted backwaters of the unfashionable end of the western spiral arm of the Galaxy lies a small unregarded yellow sun. Orbiting this at a distance of roughly ninety-two million miles is an utterly insignificant little blue-green planet whose ape-descended life forms are so amazingly primitive that they still think digital watches are a pretty neat idea.”

• “On planet Earth, man had always assumed that he was more intelligent than dolphins because he had achieved so much — the wheel, New York, wars, and so on — whilst all the dolphins had ever done was muck about in the water having a good time. But conversely, the dolphins had always believed that they were far more intelligent than man — for precisely the same reasons.”

the Sun Tzu book The Art Of War

Page Count: 273
Rating on Goodreads: 3.97

It's a classic. You may apply The Art of War's ideas to (nearly) every facet of life. Ex:

• Pick your fights.

• Keep in mind that timing is crucial.

• Create a backup plan in case something goes wrong.

• Obstacles provide us a chance to adapt and change.

This book was my first. Since then, I'm a more strategic entrepreneur. Excellent book. And read it ASAP!

My favorite book quotes are:

• “Victorious warriors win first and then go to war, while defeated warriors go to war first and then seek to win.”

• “Engage people with what they expect; it is what they are able to discern and confirms their projections. It settles them into predictable patterns of response, occupying their minds while you wait for the extraordinary moment — that which they cannot anticipate.”

• “If you know the enemy and know yourself, you need not fear the result of a hundred battles. If you know yourself but not the enemy, for every victory gained, you will also suffer a defeat. If you know neither the enemy nor yourself, you will succumb in every battle.”

Peter Thiel's book Zero to One

Page Count: 195
Rating on Goodreads: 4.18

Peter argues the best money-making strategies are typically unproven. Entrepreneurship should never have a defined path to success. Whoever says differently is lying.

Zero to One explores technology and society. Peter is a philosophy major and law school graduate, which informs the work.

Peters' ideas, depth, and intellect stood out in Zero to One. It's a top business book.

My favorite book quotes are:

• “The most valuable businesses of coming decades will be built by entrepreneurs who seek to empower people rather than try to make them obsolete.”

• “The next Bill Gates will not build an operating system. The next Larry Page or Sergey Brin won’t make a search engine. And the next Mark Zuckerberg won’t create a social network. If you are copying these guys, you aren’t learning from them.”

• “If your goal is to never make a mistake in your life, you shouldn’t look for secrets. The prospect of being lonely but right — dedicating your life to something that no one else believes in — is already hard. The prospect of being lonely and wrong can be unbearable.”

Learn to identify them, make habits to avoid them

First, a clarification. This article is aimed to make new programmers aware of their mistakes, train them to detect them, and remind them to prevent them.

I learned from all these blunders. I'm glad I have coding habits to avoid them. Do too.

These mistakes are not ordered.

1) Writing code haphazardly

Writing good content is hard. It takes planning and investigation. Quality programs don't differ.

Think. Research. Plan. Write. Validate. Modify. Unfortunately, no good acronym exists. Create a habit of doing the proper quantity of these activities.

As a newbie programmer, my biggest error was writing code without thinking or researching. This works for small stand-alone apps but hurts larger ones.

Like saying anything you might regret, you should think before coding something you could regret. Coding expresses your thoughts.

When angry, count to 10 before you speak. If very angry, a hundred. — Thomas Jefferson.

My quote:

When reviewing code, count to 10 before you refactor a line. If the code does not have tests, a hundred. — Samer Buna

Programming is primarily about reviewing prior code, investigating what is needed and how it fits into the current system, and developing small, testable features. Only 10% of the process involves writing code.

Programming is not writing code. Programming need nurturing.

2) Making excessive plans prior to writing code

Yes. Planning before writing code is good, but too much of it is bad. Water poisons.

Avoid perfect plans. Programming does not have that. Find a good starting plan. Your plan will change, but it helped you structure your code for clarity. Overplanning wastes time.

Only planning small features. All-feature planning should be illegal! The Waterfall Approach is a step-by-step system. That strategy requires extensive planning. This is not planning. Most software projects fail with waterfall. Implementing anything sophisticated requires agile changes to reality.

Programming requires responsiveness. You'll add waterfall plan-unthinkable features. You will eliminate functionality for reasons you never considered in a waterfall plan. Fix bugs and adjust. Be agile.

Plan your future features, though. Do it cautiously since too little or too much planning can affect code quality, which you must risk.

3) Underestimating the Value of Good Code

Readability should be your code's exclusive goal. Unintelligible code stinks. Non-recyclable.

Never undervalue code quality. Coding communicates implementations. Coders must explicitly communicate solution implementations.

Programming quote I like:

Always code as if the guy who ends up maintaining your code will be a violent psychopath who knows where you live. — John Woods

John, great advice!

Small things matter. If your indentation and capitalization are inconsistent, you should lose your coding license.

Long queues are also simple. Readability decreases after 80 characters. To highlight an if-statement block, you might put a long condition on the same line. No. Just never exceed 80 characters.

Linting and formatting tools fix many basic issues like this. ESLint and Prettier work great together in JavaScript. Use them.

Code quality errors:

Multiple lines in a function or file. Break long code into manageable bits. My rule of thumb is that any function with more than 10 lines is excessively long.

Double-negatives. Don't.

Using double negatives is just very not not wrong

Short, generic, or type-based variable names. Name variables clearly.

There are only two hard things in Computer Science: cache invalidation and naming things. — Phil Karlton

Hard-coding primitive strings and numbers without descriptions. If your logic relies on a constant primitive string or numeric value, identify it.

Avoiding simple difficulties with sloppy shortcuts and workarounds. Avoid evasion. Take stock.

Considering lengthier code better. Shorter code is usually preferable. Only write lengthier versions if they improve code readability. For instance, don't utilize clever one-liners and nested ternary statements just to make the code shorter. In any application, removing unneeded code is better.

Measuring programming progress by lines of code is like measuring aircraft building progress by weight. — Bill Gates

Excessive conditional logic. Conditional logic is unnecessary for most tasks. Choose based on readability. Measure performance before optimizing. Avoid Yoda conditions and conditional assignments.

4) Selecting the First Approach

When I started programming, I would solve an issue and move on. I would apply my initial solution without considering its intricacies and probable shortcomings.

After questioning all the solutions, the best ones usually emerge. If you can't think of several answers, you don't grasp the problem.

Programmers do not solve problems. Find the easiest solution. The solution must work well and be easy to read, comprehend, and maintain.

There are two ways of constructing a software design. One way is to make it so simple that there are obviously no deficiencies, and the other way is to make it so complicated that there are no obvious deficiencies. — C.A.R. Hoare

5) Not Giving Up

I generally stick with the original solution even though it may not be the best. The not-quitting mentality may explain this. This mindset is helpful for most things, but not programming. Program writers should fail early and often.

If you doubt a solution, toss it and rethink the situation. No matter how much you put in that solution. GIT lets you branch off and try various solutions. Use it.

Do not be attached to code because of how much effort you put into it. Bad code needs to be discarded.

6) Avoiding Google

I've wasted time solving problems when I should have researched them first.

Unless you're employing cutting-edge technology, someone else has probably solved your problem. Google It First.

Googling may discover that what you think is an issue isn't and that you should embrace it. Do not presume you know everything needed to choose a solution. Google surprises.

But Google carefully. Newbies also copy code without knowing it. Use only code you understand, even if it solves your problem.

Never assume you know how to code creatively.

The most dangerous thought that you can have as a creative person is to think that you know what you’re doing. — Bret Victor

7) Failing to Use Encapsulation

Not about object-oriented paradigm. Encapsulation is always useful. Unencapsulated systems are difficult to maintain.

An application should only handle a feature once. One object handles that. The application's other objects should only see what's essential. Reducing application dependencies is not about secrecy. Following these guidelines lets you safely update class, object, and function internals without breaking things.

Classify logic and state concepts. Class means blueprint template. Class or Function objects are possible. It could be a Module or Package.

Self-contained tasks need methods in a logic class. Methods should accomplish one thing well. Similar classes should share method names.

As a rookie programmer, I didn't always establish a new class for a conceptual unit or recognize self-contained units. Newbie code has a Util class full of unrelated code. Another symptom of novice code is when a small change cascades and requires numerous other adjustments.

Think before adding a method or new responsibilities to a method. Time's needed. Avoid skipping or refactoring. Start right.

High Cohesion and Low Coupling involves grouping relevant code in a class and reducing class dependencies.

8) Arranging for Uncertainty

Thinking beyond your solution is appealing. Every line of code will bring up what-ifs. This is excellent for edge cases but not for foreseeable needs.

Your what-ifs must fall into one of these two categories. Write only code you need today. Avoid future planning.

Writing a feature for future use is improper. No.

Write only the code you need today for your solution. Handle edge-cases, but don't introduce edge-features.

Growth for the sake of growth is the ideology of the cancer cell. — Edward Abbey

9) Making the incorrect data structure choices

Beginner programmers often overemphasize algorithms when preparing for interviews. Good algorithms should be identified and used when needed, but memorizing them won't make you a programming genius.

However, learning your language's data structures' strengths and shortcomings will make you a better developer.

The improper data structure shouts "newbie coding" here.

Let me give you a few instances of data structures without teaching you:

Managing records with arrays instead of maps (objects).

Most data structure mistakes include using lists instead of maps to manage records. Use a map to organize a list of records.

This list of records has an identifier to look up each entry. Lists for scalar values are OK and frequently superior, especially if the focus is pushing values to the list.

Arrays and objects are the most common JavaScript list and map structures, respectively (there is also a map structure in modern JavaScript).

Lists over maps for record management often fail. I recommend always using this point, even though it only applies to huge collections. This is crucial because maps are faster than lists in looking up records by identifier.

Stackless

Simple recursive functions are often tempting when writing recursive programming. In single-threaded settings, optimizing recursive code is difficult.

Recursive function returns determine code optimization. Optimizing a recursive function that returns two or more calls to itself is harder than optimizing a single call.

Beginners overlook the alternative to recursive functions. Use Stack. Push function calls to a stack and start popping them out to traverse them back.

10) Worsening the current code

Imagine this:

Add an item to that room. You might want to store that object anywhere as it's a mess. You can finish in seconds.

Not with messy code. Do not worsen! Keep the code cleaner than when you started.

Clean the room above to place the new object. If the item is clothing, clear a route to the closet. That's proper execution.

The following bad habits frequently make code worse:

• code duplication You are merely duplicating code and creating more chaos if you copy/paste a code block and then alter just the line after that. This would be equivalent to adding another chair with a lower base rather than purchasing a new chair with a height-adjustable seat in the context of the aforementioned dirty room example. Always keep abstraction in mind, and use it when appropriate.

• utilizing configuration files not at all. A configuration file should contain the value you need to utilize if it may differ in certain circumstances or at different times. A configuration file should contain a value if you need to use it across numerous lines of code. Every time you add a new value to the code, simply ask yourself: "Does this value belong in a configuration file?" The most likely response is "yes."

• using temporary variables and pointless conditional statements. Every if-statement represents a logic branch that should at the very least be tested twice. When avoiding conditionals doesn't compromise readability, it should be done. The main issue with this is that branch logic is being used to extend an existing function rather than creating a new function. Are you altering the code at the appropriate level, or should you go think about the issue at a higher level every time you feel you need an if-statement or a new function variable?

This code illustrates superfluous if-statements:

function isOdd(number) {
  if (number % 2 === 1) {
    return true;
  } else {
    return false;
  }
}

Can you spot the biggest issue with the isOdd function above?

Unnecessary if-statement. Similar code:

function isOdd(number) {
  return (number % 2 === 1);
};

11) Making remarks on things that are obvious

I've learnt to avoid comments. Most code comments can be renamed.

instead of:

// This function sums only odd numbers in an array
const sum = (val) => {
  return val.reduce((a, b) => {
    if (b % 2 === 1) { // If the current number is odd
      a+=b;            // Add current number to accumulator
    }
    return a;          // The accumulator
  }, 0);
};

Commentless code looks like this:

const sumOddValues = (array) => {
  return array.reduce((accumulator, currentNumber) => {
    if (isOdd(currentNumber)) { 
      return accumulator + currentNumber;
    }
    return accumulator;
  }, 0);
};

Better function and argument names eliminate most comments. Remember that before commenting.

Sometimes you have to use comments to clarify the code. This is when your comments should answer WHY this code rather than WHAT it does.

Do not write a WHAT remark to clarify the code. Here are some unnecessary comments that clutter code:

// create a variable and initialize it to 0
let sum = 0;
// Loop over array
array.forEach(
  // For each number in the array
  (number) => {
    // Add the current number to the sum variable
    sum += number;
  }
);

Avoid that programmer. Reject that code. Remove such comments if necessary. Most importantly, teach programmers how awful these remarks are. Tell programmers who publish remarks like this that they may lose their jobs. That terrible.

12) Skipping tests

I'll simplify. If you develop code without tests because you think you're an excellent programmer, you're a rookie.

If you're not writing tests in code, you're probably testing manually. Every few lines of code in a web application will be refreshed and interacted with. Also. Manual code testing is fine. To learn how to automatically test your code, manually test it. After testing your application, return to your code editor and write code to automatically perform the same interaction the next time you add code.

Human. After each code update, you will forget to test all successful validations. Automate it!

Before writing code to fulfill validations, guess or design them. TDD is real. It improves your feature design thinking.

If you can use TDD, even partially, do so.

13) Making the assumption that if something is working, it must be right.

See this sumOddValues function. Is it flawed?

const sumOddValues = (array) => {
  return array.reduce((accumulator, currentNumber) => {
    if (currentNumber % 2 === 1) { 
      return accumulator + currentNumber;
    }
    return accumulator;
  });
};
 
 
console.assert(
  sumOddValues([1, 2, 3, 4, 5]) === 9
);

Verified. Good life. Correct?

Code above is incomplete. It handles some scenarios correctly, including the assumption used, but it has many other issues. I'll list some:

#1: No empty input handling. What happens when the function is called without arguments? That results in an error revealing the function's implementation:

TypeError: Cannot read property 'reduce' of undefined.

Two main factors indicate faulty code.

• Your function's users shouldn't come across implementation-related information.

• The user cannot benefit from the error. Simply said, they were unable to use your function. They would be aware that they misused the function if the error was more obvious about the usage issue. You might decide to make the function throw a custom exception, for instance:

TypeError: Cannot execute function for empty list.

Instead of returning an error, your method should disregard empty input and return a sum of 0. This case requires action.

Problem #2: No input validation. What happens if the function is invoked with a text, integer, or object instead of an array?

The function now throws:

sumOddValues(42);
TypeError: array.reduce is not a function

Unfortunately, array. cut's a function!

The function labels anything you call it with (42 in the example above) as array because we named the argument array. The error says 42.reduce is not a function.

See how that error confuses? An mistake like:

TypeError: 42 is not an array, dude.

Edge-cases are #1 and #2. These edge-cases are typical, but you should also consider less obvious ones. Negative numbers—what happens?

sumOddValues([1, 2, 3, 4, 5, -13]) // => still 9

-13's unusual. Is this the desired function behavior? Error? Should it sum negative numbers? Should it keep ignoring negative numbers? You may notice the function should have been titled sumPositiveOddNumbers.

This decision is simple. The more essential point is that if you don't write a test case to document your decision, future function maintainers won't know if you ignored negative values intentionally or accidentally.

It’s not a bug. It’s a feature. — Someone who forgot a test case

#3: Valid cases are not tested. Forget edge-cases, this function mishandles a straightforward case:

sumOddValues([2, 1, 3, 4, 5]) // => 11

The 2 above was wrongly included in sum.

The solution is simple: reduce accepts a second input to initialize the accumulator. Reduce will use the first value in the collection as the accumulator if that argument is not provided, like in the code above. The sum included the test case's first even value.

This test case should have been included in the tests along with many others, such as all-even numbers, a list with 0 in it, and an empty list.

Newbie code also has rudimentary tests that disregard edge-cases.

14) Adhering to Current Law

Unless you're a lone supercoder, you'll encounter stupid code. Beginners don't identify it and assume it's decent code because it works and has been in the codebase for a while.

Worse, if the terrible code uses bad practices, the newbie may be enticed to use them elsewhere in the codebase since they learnt them from good code.

A unique condition may have pushed the developer to write faulty code. This is a nice spot for a thorough note that informs newbies about that condition and why the code is written that way.

Beginners should presume that undocumented code they don't understand is bad. Ask. Enquire. Blame it!

If the code's author is dead or can't remember it, research and understand it. Only after understanding the code can you judge its quality. Before that, presume nothing.

15) Being fixated on best practices

Best practices damage. It suggests no further research. Best practice ever. No doubts!

No best practices. Today's programming language may have good practices.

Programming best practices are now considered bad practices.

Time will reveal better methods. Focus on your strengths, not best practices.

Do not do anything because you read a quote, saw someone else do it, or heard it is a recommended practice. This contains all my article advice! Ask questions, challenge theories, know your options, and make informed decisions.

16) Being preoccupied with performance

Premature optimization is the root of all evil (or at least most of it) in programming — Donald Knuth (1974)

I think Donald Knuth's advice is still relevant today, even though programming has changed.

Do not optimize code if you cannot measure the suspected performance problem.

Optimizing before code execution is likely premature. You may possibly be wasting time optimizing.

There are obvious optimizations to consider when writing new code. You must not flood the event loop or block the call stack in Node.js. Remember this early optimization. Will this code block the call stack?

Avoid non-obvious code optimization without measurements. If done, your performance boost may cause new issues.

Stop optimizing unmeasured performance issues.

17) Missing the End-User Experience as a Goal

How can an app add a feature easily? Look at it from your perspective or in the existing User Interface. Right? Add it to the form if the feature captures user input. Add it to your nested menu of links if it adds a link to a page.

Avoid that developer. Be a professional who empathizes with customers. They imagine this feature's consumers' needs and behavior. They focus on making the feature easy to find and use, not just adding it to the software.

18) Choosing the incorrect tool for the task

Every programmer has their preferred tools. Most tools are good for one thing and bad for others.

The worst tool for screwing in a screw is a hammer. Do not use your favorite hammer on a screw. Don't use Amazon's most popular hammer on a screw.

A true beginner relies on tool popularity rather than problem fit.

You may not know the best tools for a project. You may know the best tool. However, it wouldn't rank high. You must learn your tools and be open to new ones.

Some coders shun new tools. They like their tools and don't want to learn new ones. I can relate, but it's wrong.

You can build a house slowly with basic tools or rapidly with superior tools. You must learn and use new tools.

19) Failing to recognize that data issues are caused by code issues

Programs commonly manage data. The software will add, delete, and change records.

Even the simplest programming errors can make data unpredictable. Especially if the same defective application validates all data.

Code-data relationships may be confusing for beginners. They may employ broken code in production since feature X is not critical. Buggy coding may cause hidden data integrity issues.

Worse, deploying code that corrected flaws without fixing minor data problems caused by these defects will only collect more data problems that take the situation into the unrecoverable-level category.

How do you avoid these issues? Simply employ numerous data integrity validation levels. Use several interfaces. Front-end, back-end, network, and database validations. If not, apply database constraints.

Use all database constraints when adding columns and tables:

• If a column has a NOT NULL constraint, null values will be rejected for that column. If your application expects that field has a value, your database should designate its source as not null.

• If a column has a UNIQUE constraint, the entire table cannot include duplicate values for that column. This is ideal for a username or email field on a Users table, for instance.

• For the data to be accepted, a CHECK constraint, or custom expression, must evaluate to true. For instance, you can apply a check constraint to ensure that the values of a normal % column must fall within the range of 0 and 100.

• With a PRIMARY KEY constraint, the values of the columns must be both distinct and not null. This one is presumably what you're utilizing. To distinguish the records in each table, the database needs have a primary key.

• A FOREIGN KEY constraint requires that the values in one database column, typically a primary key, match those in another table column.

Transaction apathy is another data integrity issue for newbies. If numerous actions affect the same data source and depend on each other, they must be wrapped in a transaction that can be rolled back if one fails.

20) Reinventing the Wheel

Tricky. Some programming wheels need reinvention. Programming is undefined. New requirements and changes happen faster than any team can handle.

Instead of modifying the wheel we all adore, maybe we should rethink it if you need a wheel that spins at varied speeds depending on the time of day. If you don't require a non-standard wheel, don't reinvent it. Use the darn wheel.

Wheel brands can be hard to choose from. Research and test before buying! Most software wheels are free and transparent. Internal design quality lets you evaluate coding wheels. Try open-source wheels. Debug and fix open-source software simply. They're easily replaceable. In-house support is also easy.

If you need a wheel, don't buy a new automobile and put your maintained car on top. Do not include a library to use a few functions. Lodash in JavaScript is the finest example. Import shuffle to shuffle an array. Don't import lodash.

21) Adopting the incorrect perspective on code reviews

Beginners often see code reviews as criticism. Dislike them. Not appreciated. Even fear them.

Incorrect. If so, modify your mindset immediately. Learn from every code review. Salute them. Observe. Most crucial, thank reviewers who teach you.

Always learning code. Accept it. Most code reviews teach something new. Use these for learning.

You may need to correct the reviewer. If your code didn't make that evident, it may need to be changed. If you must teach your reviewer, remember that teaching is one of the most enjoyable things a programmer can do.

22) Not Using Source Control

Newbies often underestimate Git's capabilities.

Source control is more than sharing your modifications. It's much bigger. Clear history is source control. The history of coding will assist address complex problems. Commit messages matter. They are another way to communicate your implementations, and utilizing them with modest commits helps future maintainers understand how the code got where it is.

Commit early and often with present-tense verbs. Summarize your messages but be detailed. If you need more than a few lines, your commit is too long. Rebase!

Avoid needless commit messages. Commit summaries should not list new, changed, or deleted files. Git commands can display that list from the commit object. The summary message would be noise. I think a big commit has many summaries per file altered.

Source control involves discoverability. You can discover the commit that introduced a function and see its context if you doubt its need or design. Commits can even pinpoint which code caused a bug. Git has a binary search within commits (bisect) to find the bug-causing commit.

Source control can be used before commits to great effect. Staging changes, patching selectively, resetting, stashing, editing, applying, diffing, reversing, and others enrich your coding flow. Know, use, and enjoy them.

I consider a Git rookie someone who knows less functionalities.

23) Excessive Use of Shared State

Again, this is not about functional programming vs. other paradigms. That's another article.

Shared state is problematic and should be avoided if feasible. If not, use shared state as little as possible.

As a new programmer, I didn't know that all variables represent shared states. All variables in the same scope can change its data. Global scope reduces shared state span. Keep new states in limited scopes and avoid upward leakage.

When numerous resources modify common state in the same event loop tick, the situation becomes severe (in event-loop-based environments). Races happen.

This shared state race condition problem may encourage a rookie to utilize a timer, especially if they have a data lock issue. Red flag. No. Never accept it.

24) Adopting the Wrong Mentality Toward Errors

Errors are good. Progress. They indicate a simple way to improve.

Expert programmers enjoy errors. Newbies detest them.

If these lovely red error warnings irritate you, modify your mindset. Consider them helpers. Handle them. Use them to advance.

Some errors need exceptions. Plan for user-defined exceptions. Ignore some mistakes. Crash and exit the app.

25) Ignoring rest periods

Humans require mental breaks. Take breaks. In the zone, you'll forget breaks. Another symptom of beginners. No compromises. Make breaks mandatory in your process. Take frequent pauses. Take a little walk to plan your next move. Reread the code.

This has been a long post. You deserve a break.

Calmness is a vital life skill.

It aids communication. It boosts creativity and performance.

I've studied calm people's habits for years. Commonalities:

Have learned to laugh at themselves.

Those who have something to protect can’t help but make it a very serious business, which drains the energy out of the room.

They are fixated on positive pursuits like making cool things, building a strong physique, and having fun with others rather than on depressing influences like the news and gossip.

Every day, spend at least 20 minutes moving, whether it's walking, yoga, or lifting weights.

Discover ways to take pleasure in life's challenges.

Since perspective is malleable, they change their view.

Set your own needs first.

Stressed people neglect themselves and wonder why they struggle.

Prioritize self-care.

Don't ruin your life to please others.

Make something.

Calm people create more than react.

They love creating beautiful things—paintings, children, relationships, and projects.

Hold your breath, please.

If you're stressed or angry, you may be surprised how much time you spend holding your breath and tightening your belly.

Release, breathe, and relax to find calm.

Stopped rushing.

Rushing is disadvantageous.

Calm people handle life better.

Are attuned to their personal dietary needs.

They avoid junk food and eat foods that keep them healthy, happy, and calm.

Don’t take anything personally.

Stressed people control everything.

Self-conscious.

Calm people put others and their work first.

Keep their surroundings neat.

Maintaining an uplifting and clutter-free environment daily calms the mind.

Minimise negative people.

Calm people are ruthless with their boundaries and avoid negative and drama-prone people.

Looking back into the past can help you understand what's happening today and why.

"Creator economy" conjures up images of originality, sincerity, and passion. Where do Michelangelos and da Vincis push advancement with their gifts without battling for bread and proving themselves posthumously? 

Creativity has been as long as humanity, but it's just recently become a new economic paradigm. We even talk about Web3 now.

Let's examine the creative economy's history to better comprehend it. What brought us here? Looking back can help you understand what's happening now.

No yawning, I promise 😉.

Creator Economy's history

Long, uneven transition to creator economy. Let's examine the economic and societal changes that led us there.

1. Agriculture to industry

Mid-18th-century Industrial Revolution led to shift from agriculture to manufacturing. The industrial economy lasted until World War II.

The industrial economy's principal goal was to provide more affordable, accessible commodities.

Unlike today, products were scarce and inaccessible.

To fulfill its goals, industrialization triggered enormous economic changes, moving power from agrarians to manufacturers. Industrialization brought hard work, rivalry, and new ideas connected to production and automation. Creative thinkers focused on that then.

It doesn't mean music, poetry, or painting had no place back then. They weren't top priority. Artists were independent. The creative field wasn't considered a different economic subdivision.

2. The consumer economy

Manufacturers produced more things than consumers desired after World War II. Stuff was no longer scarce.

The economy must make customers want to buy what the market offers.

The consumer economic paradigm supplanted the industrial one. Customers (or consumers) replaced producers as the new economic center.

Salesmen, marketing, and journalists also played key roles (TV, radio, newspapers, etc.). Mass media greatly boosted demand for goods, defined trends, and changed views regarding nearly everything.

Mass media also gave rise to pop culture, which focuses on mass-market creative products. Design, printing, publishing, multi-media, audio-visual, cinematographic productions, etc. supported pop culture.

The consumer paradigm generated creative occupations and activities, unlike the industrial economy. Creativity was limited by the need for wide appeal.

Most creators were corporate employees.

Creating a following and making a living from it were difficult.

Paul Saffo said that only journalists and TV workers were known. Creators who wished to be known relied on producers, publishers, and other gatekeepers. To win their favor was crucial. Luck was the best tactic.

3. The creative economy

Consumer economy was digitized in the 1990s. IT solutions transformed several economic segments. This new digital economy demanded innovative, digital creativity.

Later, states declared innovation a "valuable asset that creates money and jobs." They also introduced the "creative industries" and the "creative economy" (not creator!) and tasked themselves with supporting them. Australia and the UK were early adopters.

Individual skill, innovation, and intellectual property fueled the creative economy. Its span covered design, writing, audio, video material, etc. The creative economy required IT-powered activity.

The new challenge was to introduce innovations to most economic segments and meet demand for digital products and services.

Despite what the title "creative economy" may imply, it was primarily oriented at meeting consumer needs. It didn't provide inventors any new options to become entrepreneurs. Instead of encouraging innovators to flourish on their own, the creative economy emphasized "employment-based creativity."

4. The creator economy

Next, huge IT platforms like Google, Facebook, YouTube, and others competed with traditional mainstream media.

During the 2008 global financial crisis, these mediums surpassed traditional media. People relied on them for information, knowledge, and networking. That was a digital media revolution. The creator economy started there.

The new economic paradigm aimed to engage and convert clients. The creator economy allowed customers to engage, interact, and provide value, unlike the consumer economy. It gave them instruments to promote themselves as "products" and make money.

Writers, singers, painters, and other creators have a great way to reach fans. Instead of appeasing old-fashioned gatekeepers (producers, casting managers, publishers, etc.), they can use the platforms to express their talent and gain admirers. Barriers fell.

It's not only for pros. Everyone with a laptop and internet can now create.

2022 creator economy:

Since there is no academic description for the current creator economy, we can freestyle.

The current (or Web2) creator economy is fueled by interactive digital platforms, marketplaces, and tools that allow users to access, produce, and monetize content.

No entry hurdles or casting in the creative economy. Sign up and follow platforms' rules. Trick: A platform's algorithm aggregates your data and tracks you. This is the payment for participation.

The platforms offer content creation, design, and ad distribution options. This is platforms' main revenue source.

The creator economy opens many avenues for creators to monetize their work. Artists can now earn money through advertising, tipping, brand sponsorship, affiliate links, streaming, and other digital marketing activities.

Even if your content isn't digital, you can utilize platforms to promote it, interact and convert your audience, and more. No limits. However, some of your income always goes to a platform (well, a huge one).

The creator economy aims to empower online entrepreneurship by offering digital marketing tools and reducing impediments.

Barriers remain. They are just different. Next articles will examine these.

Why update the creator economy for Web3?

I could address this question by listing the present creator economy's difficulties that led us to contemplate a Web3 upgrade.

I don't think these difficulties are the main cause. The mentality shift made us see these challenges and understand there was a better reality without them.

Crypto drove this thinking shift. It promoted disintermediation, independence from third-party service providers, 100% data ownership, and self-sovereignty. Crypto has changed the way we view everyday things.

Crypto's disruptive mission has migrated to other economic segments. It's now called Web3. Web3's creator economy is unique.

Here's the essence of the Web3 economy:

• Eliminating middlemen between creators and fans.

• 100% of creators' data, brand, and effort.

• Business and money-making transparency.

• Authentic originality above ad-driven content.

In the next several articles, I'll explain. We'll also discuss the creator economy and Web3's remedies.

Final thoughts

The creator economy is the organic developmental stage we've reached after all these social and economic transformations.

The Web3 paradigm of the creator economy intends to allow creators to construct their own independent "open economy" and directly monetize it without a third party.

If this approach succeeds, we may enter a new era of wealth creation where producers aren't only the products. New economies will emerge.

This article is a summary. To read the full post, click here.

Ethereum's blockchain rules NFTs. Many consider it the one-stop shop for NFTs, and it's become the most talked-about and trafficked blockchain in existence.

Other blockchains are becoming popular in NFTs. Crypto-artists and NFT enthusiasts have sought new places to mint and trade NFTs due to Ethereum's high transaction costs and environmental impact.

When choosing a blockchain to mint on, there are several factors to consider. Size, creator costs, consumer spending habits, security, and community input are important. We've created a high-level summary of blockchains for NFTs to help clarify the fast-paced world of web3 tech.

Ethereum

Ethereum currently has the most NFTs. It's decentralized and provides financial and legal services without intermediaries. It houses popular NFT marketplaces (OpenSea), projects (CryptoPunks and the Bored Ape Yacht Club), and artists (Pak and Beeple).

It's also expensive and energy-intensive. This is because Ethereum works using a Proof-of-Work (PoW) mechanism. PoW requires computers to solve puzzles to add blocks and transactions to the blockchain. Solving these puzzles requires a lot of computer power, resulting in astronomical energy loss.

You should consider this blockchain first due to its popularity, security, decentralization, and ease of use.

Solana

Solana is a fast programmable blockchain. Its proof-of-history and proof-of-stake (PoS) consensus mechanisms eliminate complex puzzles. Reduced validation times and fees result.

PoS users stake their cryptocurrency to become a block validator. Validators get SOL. This encourages and rewards users to become stakers. PoH works with PoS to cryptographically verify time between events. Solana blockchain ensures transactions are in order and found by the correct leader (validator).

Solana's PoS and PoH mechanisms keep transaction fees and times low. Solana isn't as popular as Ethereum, so there are fewer NFT marketplaces and blockchain traders.

Tezos

Tezos is a greener blockchain. Tezos rose in 2021. Hic et Nunc was hailed as an economic alternative to Ethereum-centric marketplaces until Nov. 14, 2021.

Similar to Solana, Tezos uses a PoS consensus mechanism and only a PoS mechanism to reduce computational work. This blockchain uses two million times less energy than Ethereum. It's cheaper than Ethereum (but does cost more than Solana).

Tezos is a good place to start minting NFTs in bulk. Objkt is the largest Tezos marketplace.

Flow

Flow is a high-performance blockchain for NFTs, games, and decentralized apps (dApps). Flow is built with scalability in mind, so billions of people could interact with NFTs on the blockchain.

Flow became the NBA's blockchain partner in 2019. Flow, a product of Dapper labs (the team behind CryptoKitties), launched and hosts NBA Top Shot, making the blockchain integral to the popularity of non-fungible tokens.

Flow uses PoS to verify transactions, like Tezos. Developers are working on a model to handle 10,000 transactions per second on the blockchain. Low transaction fees.

Flow NFTs are tradeable on Blocktobay, OpenSea, Rarible, Foundation, and other platforms. NBA, NFL, UFC, and others have launched NFT marketplaces on Flow. Flow isn't as popular as Ethereum, resulting in fewer NFT marketplaces and blockchain traders.

Asset Exchange (WAX)

WAX is king of virtual collectibles. WAX is popular for digitalized versions of legacy collectibles like trading cards, figurines, memorabilia, etc.

Wax uses a PoS mechanism, but also creates carbon offset NFTs and partners with Climate Care. Like Flow, WAX transaction fees are low, and network fees are redistributed to the WAX community as an incentive to collectors.

WAX marketplaces host Topps, NASCAR, Hot Wheels, and cult classic film franchises like Godzilla, The Princess Bride, and Spiderman.

Binance Smart Chain

BSC is another good option for balancing fees and performance. High-speed transactions and low fees hurt decentralization. BSC is most centralized.

Binance Smart Chain uses Proof of Staked Authority (PoSA) to support a short block time and low fees. The 21 validators needed to run the exchange switch every 24 hours. 11 of the 21 validators are directly connected to the Binance Crypto Exchange, according to reports.

While many in the crypto and NFT ecosystems dislike centralization, the BSC NFT market picked up speed in 2021. OpenBiSea, AirNFTs, JuggerWorld, and others are gaining popularity despite not having as robust an ecosystem as Ethereum.

Ethereum is the second-largest cryptocurrency. The Merge, a mid-September event that will convert Ethereum's consensus process from proof-of-work to proof-of-stake if all goes according to plan, will be a game changer.

Why is Ethereum ditching proof-of-work? Because it can. We're talking about a fully functioning, open-source ecosystem with a capacity for evolution that other cryptocurrencies lack, a change that would allow it to scale up its performance from 15 transactions per second to 100,000 as its blockchain is used for more and more things. It would reduce its energy consumption by 99.95%. Vitalik Buterin, the system's founder, would play a less active role due to decentralization, and miners, who validated transactions through proof of work, would be far less important.

Why has this conversion taken so long and been so cautious? Because it involves modifying a core process while it's running to boost its performance. It requires running the new mechanism in test chains on an ever-increasing scale, assessing participant reactions, and checking for issues or restrictions. The last big test was in early June and was successful. All that's left is to converge the mechanism with the Ethereum blockchain to conclude the switch.

What's stopping Bitcoin, the leader in market capitalization and the cryptocurrency that began blockchain's appeal, from doing the same? Satoshi Nakamoto, whoever he or she is, departed from public life long ago, therefore there's no community leadership. Changing it takes a level of consensus that is impossible to achieve without strong leadership, which is why Bitcoin's evolution has been sluggish and conservative, with few modifications.

Secondly, The Merge will balance the consensus mechanism (proof-of-work or proof-of-stake) and the system decentralization or centralization. Proof-of-work prevents double-spending, thus validators must buy hardware. The system works, but it requires a lot of electricity and, as it scales up, tends to re-centralize as validators acquire more hardware and the entire network activity gets focused in a few nodes. Larger operations save more money, which increases profitability and market share. This evolution runs opposed to the concept of decentralization, and some anticipate that any system that uses proof of work as a consensus mechanism will evolve towards centralization, with fewer large firms able to invest in efficient network nodes.

Yet radical bitcoin enthusiasts share an opposite argument. In proof-of-stake, transaction validators put their funds at stake to attest that transactions are valid. The algorithm chooses who validates each transaction, giving more possibilities to nodes that put more coins at stake, which could open the door to centralization and government control.

In both cases, we're talking about long-term changes, but Bitcoin's proof-of-work has been evolving longer and seems to confirm those fears, while proof-of-stake is only employed in coins with a minuscule volume compared to Ethereum and has no predictive value.

As of mid-September, we will have two significant cryptocurrencies, each with a different consensus mechanisms and equally different characteristics: one is intrinsically conservative and used only for economic transactions, while the other has been evolving in open source mode, and can be used for other types of assets, smart contracts, or decentralized finance systems. Some even see it as the foundation of Web3.

Many things could change before September 15, but The Merge is likely to be a turning point. We'll have to follow this closely.