Consider yourself lucky if your last FAANG interview was rejected.

FAANG—Facebook, Apple, Amazon, Netflix, Google

(I know its manga now, but watch me not care)

These big companies offer many benefits.

1. large salaries and benefits

2. Prestige

3. high expectations for both you and your coworkers.

However, these jobs may have major drawbacks that only become apparent when you're thrown to the wolves, so it's up to you whether you see them as drawbacks or opportunities.

I know most college graduates start working at big tech companies because of their perceived coolness.

I've worked in these companies for years and can tell you what to expect if you get a job here.

Little fish in a vast ocean

The most obvious. Most billion/trillion-dollar companies employ thousands.

You may work on a small, unnoticed product part.

Directors and higher will sometimes make you redo projects they didn't communicate well without respecting your time, talent, or will to work on trivial stuff that doesn't move company needles.

Peers will only say, "Someone has to take out the trash," even though you know company resources are being wasted.

The power imbalance is frustrating.

What you can do about it

Know your WHY. Consider long-term priorities. Though riskier, I stayed in customer-facing teams because I loved building user-facing products.

This increased my impact. However, if you enjoy helping coworkers build products, you may be better suited for an internal team.

I told the Directors and Vice Presidents that their actions could waste Engineering time, even though it was unpopular. Some were receptive, some not.

I kept having tough conversations because they were good for me and the company.

However, some of my coworkers praised my candor but said they'd rather follow the boss.

An outdated piece of technology can take years to update.

Apple introduced Swift for iOS development in 2014. Most large tech companies adopted the new language after five years.

This is frustrating if you want to learn new skills and increase your market value.

Knowing that my lack of Swift practice could hurt me if I changed jobs made writing verbose Objective C painful.

What you can do about it

1. Work on the new technology in side projects; one engineer rewrote the Lyft app in Swift over the course of a weekend and promoted its adoption throughout the entire organization.

2. To integrate new technologies and determine how to combine legacy and modern code, suggest minor changes to the existing codebase.

Most managers spend their entire day in consecutive meetings.

After their last meeting, the last thing they want is another meeting to discuss your career goals.

Sometimes a manager has 15-20 reports, making it hard to communicate your impact.

Misunderstandings and stress can result.

Especially when the manager should focus on selfish parts of the team. Success won't concern them.

What you can do about it

1. Tell your manager that you are a self-starter and that you will pro-actively update them on your progress, especially if they aren't present at the meetings you regularly attend.

2. Keep being proactive and look for mentorship elsewhere if you believe your boss doesn't have enough time to work on your career goals.

3. Alternately, look for a team where the manager has more authority to assist you in making career decisions.

After a certain point, company loyalty can become quite harmful.

Because big tech companies create brand loyalty, too many colleagues stayed in unhealthy environments.

When you work for a well-known company and strangers compliment you, it's fun to tell your friends.

Work defines you. This can make you stay too long even though your career isn't progressing and you're unhappy.

Google may become your surname.

Workplaces are not families.

If you're unhappy, don't stay just because they gave you the paycheck to buy your first home and make you feel like you owe your life to them.

Many employees stayed too long. Though depressed and suicidal.

What you can do about it

1. Your life is not worth a company.

2. Do you want your job title and workplace to be listed on your gravestone? If not, leave if conditions deteriorate.

3. Recognize that change can be challenging. It's difficult to leave a job you've held for a number of years.

4. Ask those who have experienced this change how they handled it.

You still have a bright future if you were rejected from FAANG interviews.

Rejections only lead to amazing opportunities. If you're young and childless, work for a startup.

Companies may pay more than FAANGs. Do your research.

Ask recruiters and hiring managers tough questions about how the company and teams prioritize respectful working hours and boundaries for workers.

I know many 15-year-olds who have a lifelong dream of working at Google, and it saddens me that they're chasing a name on their resume instead of excellence.

This article is not meant to discourage you from working at these companies, but to share my experience about what HR/managers will never mention in interviews.

Read both sides before signing the big offer letter.

https://tomconnor.me/wp-content/uploads/2021/08/10x-Engineer-Mental-Models.pdf

I keep returning to the same mental models and tricks after writing and reading about a wide range of topics.

Top 12 mental models

12.

Survival bias - We perceive the surviving population as remarkable, yet they may have gotten there through sheer grit.

Survivorship bias affects us in many situations. Our retirement fund; the unicorn business; the winning team. We often study and imitate the last one standing. This can lead to genuine insights and performance improvements, but it can also lead us astray because the leader may just be lucky.

11.

The Helsinki Bus Theory - How to persevere Buss up!

Always display new work, and always be compared to others. Why? Easy. Keep riding. Stay on the fucking bus.

10.

Until it sticks… Turning up every day… — Artists teach engineers plenty. Quality work over a career comes from showing up every day and starting.

9.

WRAP decision making process (Heath Brothers)

Decision-making WRAP Model:

W — Widen your Options

R — Reality test your assumptions

A — Attain Distance

P — Prepare to be wrong or Right

8.

Systems for knowledge worker excellence - Todd Henry and Cal Newport write about techniques knowledge workers can employ to build a creative rhythm and do better work.

Todd Henry's FRESH framework:

1. Focus: Keep the start in mind as you wrap up.

2. Relationships: close a loop that's open.

3. Pruning is an energy.

4. Set aside time to be inspired by stimuli.

5. Hours: Spend time thinking.

7.

Black Box Thinking…..

BBT is learning from mistakes. Science has transformed the world because it constantly updates its theories in light of failures. Complexity guarantees failure. Do we learn or self-justify?

6.

The OODA Loop - Competitive advantage

O: Observe: collect the data. Figure out exactly where you are, what’s happening.

O: Orient: analyze/synthesize the data to form an accurate picture.

D: Decide: select an action from possible options

A: Action: execute the action, and return to step (1)

Boyd's approach indicates that speed and agility are about information processing, not physical reactions. They form feedback loops. More OODA loops improve speed.

5.

Know your Domain 

Leaders who try to impose order in a complex situation fail; those who set the stage, step back, and allow patterns to develop win.

https://vimeo.com/640941172?embedded=true&source=vimeo_logo&owner=11999906

4.

The Three Critical Gaps

• Information Gap - The discrepancy between what we know and what we would like to know

• Gap in Alignment - What individuals actually do as opposed to what we wish them to do

• Effects Gap - the discrepancy between our expectations and the results of our actions

3.

Theory of Constraints — The Goal  - To maximize system production, maximize bottleneck throughput.

• Goldratt creates a five-step procedure:

1. Determine the restriction

2. Improve the restriction.

3. Everything else should be based on the limitation.

4. Increase the restriction

5. Go back to step 1 Avoid letting inertia become a limitation.

Any non-constraint improvement is an illusion.

2.

Serendipity and the Adjacent Possible - Why do several amazing ideas emerge at once? How can you foster serendipity in your work?

You need specialized abilities to reach to the edge of possibilities, where you can pursue exciting tasks that will change the world. Few people do it since it takes a lot of hard work. You'll stand out if you do.

Most people simply lack the comfort with discomfort required to tackle really hard things. At some point, in other words, there’s no way getting around the necessity to clear your calendar, shut down your phone, and spend several hard days trying to make sense of the damn proof.

1.

Boundaries of failure - Rasmussen's accident model.

Rasmussen modeled this. It has economic, workload, and performance boundaries.

The economic boundary is a company's profit zone. If the lights are on, you're within the economic boundaries, but there's pressure to cut costs and do more.

Performance limit reflects system capacity. Taking shortcuts is a human desire to minimize work. This is often necessary to survive because there's always more labor.

Both push operating points toward acceptable performance. Personal or process safety, or equipment performance.

If you exceed acceptable performance, you'll push back, typically forcefully.

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.

Its not a fuc*ing fad.

It's not about boring monkeys or photos as nfts; that's just what's been pushed up and made a lot of money. The technology underlying those ridiculous nft photos will one day prove your house and automobile ownership and tell you where your banana came from. Are you ready for web3? Soar!

People don't realize that absolutely anything can and will be part of the blockchain and smart contracts, making them even better. I'll tell you a secret: it will and is happening.

Why?

Why is something blockchain-based a good idea? So let’s speak about cars!

So a new Tesla car is manufactured, and when you buy it, it is bound to an NFT on the blockchain that proves current ownership. The NFT in the smart contract can contain some data about the current owner of the car and some data about the car's status, such as the number of miles driven, the car's overall quality, and so on, as well as a reference to a digital document bound to the NFT that has more information.

Now, 40 years from now, if you want to buy a used automobile, you can scan the car's serial number to view its NFT and see all of its history, each owner, how long they owned it, if it had damages, and more. Since it's on the blockchain, it can't be tampered with.

When you're ready to buy it, the owner posts it for sale, you buy it, and it's sent to your wallet. 5 seconds to change owner, 100% safe and verifiable.

Incorporate insurance logic into the car contract. If you crashed, your car's smart contract would take money from your insurance contract and deposit it in an insurance company wallet.

It's limitless. Your funds may be used by investors to provide insurance as they profit from everyone's investments.

Or suppose all car owners in a country deposit a fixed amount of money into an insurance smart contract that promises if something happens, we'll take care of it. It could be as little as $100-$500 per year, and in a country with 10 million people, maybe 3 million would do that, which would be $500 000 000 in that smart contract and it would be used by the insurance company to invest in assets or take a cut, literally endless possibilities.

Instead of $300 per month, you may pay $300 per year to be covered if something goes wrong, and that may include multiple insurances.

What about your grocery store banana, though?

Yes that too.

You can scan a banana to learn its complete history. You'll be able to see where it was cultivated, every middleman in the supply chain, and hopefully the banana's quality, farm, and ingredients used.

If you want locally decent bananas, you can only buy them, offering you transparency and options. I believe it will be an online marketplace where farmers publish their farms and products for trust and transparency. You might also buy bananas from the farmer.

And? Food security to finish the article. If an order of bananas included a toxin, you could easily track down every banana from the same origin and supply chain and uncover the root cause. This is a tremendous thing that will save lives and have a big impact; did you realize that 1 in 6 Americans gets poisoned by food every year? This could lower the number.

To summarize:

Smart contracts can issue nfts as proof of ownership and include functionality.

Are you one of these Zoom dogs?

The Person Who Is Apparently Always on Mute

Waffles thinks he can overpower the mute button by shouting loudly.

The person who believed their camera to be off

Barkley's used to remote work, but he hasn't mastered the "Stop Video" button. Everyone is affected.

Who is driving for some reason, exactly?

Why is Pumpkin always late? Who knows? Shouldn't she be driving? If you could hear her over the freeway, she'd answer these questions.

The Person With the Amazing Bookcase

Cicero likes to use SAT-words like "leverage" and "robust" in Zoom sessions, presumably from all the books he wants you to see behind him.

The Individual Who Is Unnecessarily Dressed

We hope Bandit is going somewhere beautiful after this meeting, or else he neglected the quarterly earnings report and is overcompensating to distract us.

The person who works through lunch in between zoom calls

Barksworth has back-to-back meetings all day, so you can watch her eat while she talks.

The Person Who Is A Little Too Comfy

Hercules thinks Zoom meetings happen between sleeps. He'd appreciate everyone speaking more quietly.

The Person Who Answered the Phone Outside

Frisbee has a gorgeous backyard and lives in a place with great weather year-round, and she wants you to think about that during the daily team huddle.

Who Wants You to Pay Attention to Their Pet

Snickers hasn't listened to you in 20 minutes unless you tell her how cute her kitten is.

One who is, for some reason, positioned incorrectly on the screen

Nelson's meetings consist primarily of attempting to figure out how he positioned his laptop so absurdly.

The person who says too many goodbyes

Zeus waves farewell like it's your first day of school while everyone else searches for the "Leave Meeting" button. It's nice.

He who has a poor internet connection

Ziggy's connectivity problems continue... She gives a long speech as everyone waits awkwardly to inform her they missed it.

The Clearly Multitasking Person

Tinkerbell can play fetch during the monthly staff meeting if she works from home, but that's not a good idea.

The Person Using Zoom as a Makeup and Hair Mirror

If Gail and Bob knew Zoom had a "hide self view" option, they'd be distraught.

The person who feels at ease with simply leaving

Rusty bails when a Zoom conference is over. Rusty's concept is decent.

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.