Quitting your job for a side gig isn't smart.

A few years ago, I would have laughed at the idea of starting a side business.

I never thought a side gig could earn more than my 9-to-5. My side gig pays more than my main job now.

You may then tell me to leave your job.  But I don't want to gamble, and my side gig is important. Programming and web development help me write better because of my job.

Yes, I share work-related knowledge. Web development, web3, programming, money, investment, and side hustles are key.

Let me now show you how to make one.

Create a side business based on your profession or your interests.

I'd be direct.

Most people don't know where to start or which side business to pursue.

You can make money by taking online surveys, starting a YouTube channel, or playing web3 games, according to several blogs.

You won't make enough money and will waste time.

Nitin directs our efforts. My friend, you've worked and have talent. Profit from your talent.

Example:

College taught me web development. I soon created websites, freelanced, and made money. First year was hardest for me financially and personally.

As I worked, I became more skilled. Soon after, I got more work, wrote about web development on Medium, and started selling products.

I've built multiple income streams from web development. It wasn't easy. Web development skills got me a 9-to-5 job.

Focus on a specific skill and earn money in many ways. Most people start with something they hate or are bad at; the rest is predictable.

Result? They give up, frustrated.

Quietly focus for a year.

I started my side business in college and never told anyone. My parents didn't know what I did for fun.

The only motivation is time constraints. So I focused.

As I've said, I focused on my strengths (learned skills) and made money. Yes, I was among Medium's top 500 authors in a year and got a bonus.

How did I succeed? Since I know success takes time, I never imagined making enough money in a month. I spent a year concentrating.

I became wealthy. Now that I have multiple income sources, some businesses pay me based on my skill.

I recommend learning skills and working quietly for a year. You can do anything with this.

The hardest part will always be the beginning.

When someone says you can make more money working four hours a week. Leave that, it's bad advice.

If someone recommends a paid course to help you succeed, think twice.

The beginning is always the hardest.

I made many mistakes learning web development. When I started my technical content side gig, it was tough. I made mistakes and changed how I create content, which helped.

And it’s applicable everywhere.

Don't worry if you face problems at first. Time and effort heal all wounds.

Quitting your job to work a side job is not a good idea.

Some honest opinions.

Most online gurus encourage side businesses. It takes time to start and grow a side business.

Suppose you quit and started a side business.

After six months, what happens? Your side business won't provide enough money to survive.

Indeed. Later, you'll become demotivated and tense and look for work.

Instead, work 9-5, and start a side business. You decide. Stop watching Netflix and focus on your side business.

I know you're busy, but do it.

Next? It'll succeed or fail in six months. You can continue your side gig for another six months because you have a job and have tried it.

You'll probably make money, but you may need to change your side gig.

That’s it.

You've created a new revenue stream.

Remember.

Starting a side business, a company, or finding work is difficult. There's no free money in a competitive world. You'll only succeed with skill.

Read it again.

Focusing silently for a year can help you succeed.

I studied web development and wrote about it. First year was tough. I went viral, hit the top 500, and other firms asked me to write for them. So, my life changed.

Yours can too. One year of silence is required.

Enjoy!

Sometimes doing everything correctly isn't enough.

In 2020, I could fix my life.

After failing to start a business, I owed $40,000 and had no work.

A $1.2 billion startup on the cusp of going public pulled me up.

Ironically, it was getting ready for an epic fall — with the world watching.

Life sometimes helps. Without a base, even the strongest fall. A corporation that did everything right failed 3 months after going public.

First-row view.

Apple is the creator of Adore.

Out of respect, I've altered the company and employees' names in this account, despite their failure.

Although being a publicly traded company, it may become obvious.

We’ll call it “Adore” — a revolutionary concept in retail shopping.

Two Apple execs established Adore in 2014 with a focus on people-first purchasing.

Jon and Tim:

• The concept for the stylish Apple retail locations you see today was developed by retail expert Jon Swanson, who collaborated closely with Steve Jobs.

• Tim Cruiter is a graphic designer who produced the recognizable bouncing lamp video that appears at the start of every Pixar film.

The dynamic duo realized their vision.

“What if you could combine the convenience of online shopping with the confidence of the conventional brick-and-mortar store experience.”

Adore's mobile store concept combined traditional retail with online shopping.

Adore brought joy to 70+ cities and 4 countries over 7 years, including the US, Canada, and the UK.

Being employed on the ground floor, with world dominance and IPO on the horizon, was exciting.

I started as an Adore Expert.

I delivered cell phones, helped consumers set them up, and sold add-ons.

As the company grew, I became a Virtual Learning Facilitator and trained new employees across North America using Zoom.

In this capacity, I gained corporate insider knowledge. I worked with the creative team and Jon and Tim.

It's where I saw company foundation fissures. Despite appearances, investors were concerned.

The business strategy was ground-breaking.

Even after seeing my employee stocks fall from a home down payment to $0 (when Adore filed for bankruptcy), it's hard to pinpoint what went wrong.

Solid business model, well-executed.

Jon and Tim's chase for public funding ended in glory.

Here’s the business model in a nutshell:

Buying cell phones is cumbersome. You have two choices:

1. Online purchase: not knowing what plan you require or how to operate your device.

2. Enter a store, which can be troublesome and stressful.

Apple, AT&T, and Rogers offered Adore as a free delivery add-on. Customers could:

• Have their phone delivered by UPS or Canada Post in 1-2 weeks.

• Alternately, arrange for a person to visit them the same day (or sometimes even the same hour) to assist them set up their phone and demonstrate how to use it (transferring contacts, switching the SIM card, etc.).

Each Adore Expert brought a van with extra devices and accessories to customers.

Happy customers.

Here’s how Adore and its partners made money:

Adores partners appreciated sending Experts to consumers' homes since they improved customer satisfaction, average sale, and gadget returns.

**Telecom enterprises have low customer satisfaction. The average NPS is 30/100. Adore's global NPS was 80.

Adore made money by:

• a set cost for each delivery

• commission on sold warranties and extras

Consumer product applications seemed infinite.

A proprietary scheduling system (“The Adore App”), allowed for same-day, even same-hour deliveries.

It differentiates Adore.

They treated staff generously by:

• Options on stock

• health advantages

• sales enticements

• high rates per hour

Four-day workweeks were set by experts.

Being hired early felt like joining Uber, Netflix, or Tesla. We hoped the company's stocks would rise.

Exciting times.

I smiled as I greeted more than 1,000 new staff.

I spent a decade in retail before joining Adore. I needed a change.

After a leap of faith, I needed a lifeline. So, I applied for retail sales jobs in the spring of 2019.

The universe typically offers you what you want after you accept what you need. I needed a job to settle my debt and reach $0 again.

And the universe listened.

After being hired as an Adore Expert, I became a Virtual Learning Facilitator. Enough said.

After weeks of economic damage from the pandemic.

This employment let me work from home during the pandemic. It taught me excellent business skills.

I was active in brainstorming, onboarding new personnel, and expanding communication as we grew.

This job gave me vital skills and a regular paycheck during the pandemic.

It wasn’t until January of 2022 that I left on my own accord to try to work for myself again — this time, it’s going much better.

Adore was perfect. We valued:

• Connection

• Discovery

• Empathy

Everything we did centered on compassion, and we held frequent Justice Calls to discuss diversity and work culture.

The last day of onboarding typically ended in tears as employees felt like they'd found a home, as I had.

Like all nice things, the wonderful vibes ended.

First indication of distress

My first day at the workplace was great.

Fun, intuitive, and they wanted creative individuals, not salesman.

While sales were important, the company's vision was more important.

“To deliver joy through life-changing mobile retail experiences.”

Thorough, forward-thinking training. We had a module on intuition. It gave us role ownership.

We were flown cross-country for training, gave feedback, and felt like we made a difference. Multiple contacts responded immediately and enthusiastically.

The atmosphere was genuine.

Making money was secondary, though. Incredible service was a priority.

Jon and Tim answered new hires' questions during Zoom calls during onboarding. CEOs seldom meet new hires this way, but they seemed to enjoy it.

All appeared well.

But in late 2021, things started changing.

Adore's leadership changed after its IPO. From basic values to sales maximization. We lost communication and were forced to fend for ourselves.

Removed the training wheels.

It got tougher to gain instructions from those above me, and new employees told me their roles weren't as advertised.

External money-focused managers were hired.

Instead of creative types, we hired salespeople.

With a new focus on numbers, Adore's uniqueness began to crumble.

Via Zoom, hundreds of workers were let go.

So.

Early in 2022, mass Zoom firings were trending. A CEO firing 900 workers over Zoom went viral.

Adore was special to me, but it became a headline.

30 June 2022, Vice Motherboard published Watch as Adore's CEO Fires Hundreds.

It described a leaked video of Jon Swanson laying off all staff in Canada and the UK.

They called it a “notice of redundancy”.

The corporation couldn't pay its employees.

I loved Adore's underlying ideals, among other things. We called clients Adorers and sold solutions, not add-ons.

But, like anything, a company is only as strong as its weakest link. And obviously, the people-first focus wasn’t making enough money.

There were signs. The expansion was presumably a race against time and money.

Adore finally declared bankruptcy.

Adore declared bankruptcy 3 months after going public. It happened in waves, like any large-scale fall.

• Initial key players to leave were

• Then, communication deteriorated.

• Lastly, the corporate culture disintegrated.

6 months after leaving Adore, I received a letter in the mail from a Law firm — it was about my stocks.

Adore filed Chapter 11. I had to sue to collect my worthless investments.

I hoped those stocks will be valuable someday. Nope. Nope.

Sad, I sighed.

$1.2 billion firm gone.

I left the workplace 3 months before starting a writing business. Despite being mediocre, I'm doing fine.

I got up as Adore fell.

Finally, can we scale kindness?

I trust my gut. Changes at Adore made me leave before it sank.

Adores' unceremonious slide from a top startup to bankruptcy is astonishing to me.

The company did everything perfectly, in my opinion.

• first to market,

• provided excellent service

• paid their staff handsomely.

• was responsible and attentive to criticism

The company wasn't led by an egotistical eccentric. The crew had centuries of cumulative space experience.

I'm optimistic about the future of work culture, but is compassion scalable?

Non-technical talents can benefit data scientists in addition to statistics and programming.

As our article 5 Most In-Demand Skills for Data Scientists shows, being business-minded is useful. How can you get such a diverse skill set? We've compiled a list of helpful resources.

Data science, data analysis, programming, and business are covered. Even a few of these books will make you a better data scientist.

Ready? Let’s dive in.

Best books for data scientists

1. The Black Swan

Author: Nassim Taleb

First, a less obvious title. Nassim Nicholas Taleb's seminal series examines uncertainty, probability, risk, and decision-making.

Three characteristics define a black swan event:

• It is erratic.

• It has a significant impact.

• Many times, people try to come up with an explanation that makes it seem more predictable than it actually was.

People formerly believed all swans were white because they'd never seen otherwise. A black swan in Australia shattered their belief.

Taleb uses this incident to illustrate how human thinking mistakes affect decision-making. The book teaches readers to be aware of unpredictability in the ever-changing IT business.

Try multiple tactics and models because you may find the answer.

2. High Output Management

Author: Andrew Grove

Intel's former chairman and CEO provides his insights on developing a global firm in this business book. We think Grove would choose “management” to describe the talent needed to start and run a business.

That's a skill for CEOs, techies, and data scientists. Grove writes on developing productive teams, motivation, real-life business scenarios, and revolutionizing work.

Five lessons:

• Every action is a procedure.

• Meetings are a medium of work

• Manage short-term goals in accordance with long-term strategies.

• Mission-oriented teams accelerate while functional teams increase leverage.

• Utilize performance evaluations to enhance output.

So — if the above captures your imagination, it’s well worth getting stuck in.

3. The Hard Thing About Hard Things: Building a Business When There Are No Easy Answers

Author: Ben Horowitz

Few realize how difficult it is to run a business, even though many see it as a tremendous opportunity.

Business schools don't teach managers how to handle the toughest difficulties; they're usually on their own. So Ben Horowitz wrote this book.

It gives tips on creating and maintaining a new firm and analyzes the hurdles CEOs face.

Find suggestions on:

• create software

• Run a business.

• Promote a product

• Obtain resources

• Smart investment

• oversee daily operations

This book will help you cope with tough times.

4. Obviously Awesome: How to Nail Product Positioning

Author: April Dunford

Your job as a data scientist is a product. You should be able to sell what you do to clients. Even if your product is great, you must convince them.

How to? April Dunford's advice: Her book explains how to connect with customers by making your offering seem like a secret sauce.

You'll learn:

• Select the ideal market for your products.

• Connect an audience to the value of your goods right away.

• Take use of three positioning philosophies.

• Utilize market trends to aid purchasers

5. The Mom test

Author: Rob Fitzpatrick

The Mom Test improves communication. Client conversations are rarely predictable. The book emphasizes one of the most important communication rules: enquire about specific prior behaviors.

Both ways work. If a client has suggestions or demands, listen carefully and ensure everyone understands. The book is packed with client-speaking tips.

6. Introduction to Machine Learning with Python: A Guide for Data Scientists

Authors: Andreas C. Müller, Sarah Guido

Now, technical documents.

This book is for Python-savvy data scientists who wish to learn machine learning. Authors explain how to use algorithms instead of math theory.

Their technique is ideal for developers who wish to study machine learning basics and use cases. Sci-kit-learn, NumPy, SciPy, pandas, and Jupyter Notebook are covered beyond Python.

If you know machine learning or artificial neural networks, skip this.

7. Python Data Science Handbook: Essential Tools for Working with Data

Author: Jake VanderPlas

Data work isn't easy. Data manipulation, transformation, cleansing, and visualization must be exact.

Python is a popular tool. The Python Data Science Handbook explains everything. The book describes how to utilize Pandas, Numpy, Matplotlib, Scikit-Learn, and Jupyter for beginners.

The only thing missing is a way to apply your learnings.

8. Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython

Author: Wes McKinney

The author leads you through manipulating, processing, cleaning, and analyzing Python datasets using NumPy, Pandas, and IPython.

The book's realistic case studies make it a great resource for Python or scientific computing beginners. Once accomplished, you'll uncover online analytics, finance, social science, and economics solutions.

9. Data Science from Scratch

Author: Joel Grus

Here's a title for data scientists with Python, stats, maths, and algebra skills (alongside a grasp of algorithms and machine learning). You'll learn data science's essential libraries, frameworks, modules, and toolkits.

The author works through all the key principles, providing you with the practical abilities to develop simple code. The book is appropriate for intermediate programmers interested in data science and machine learning.

Not that prior knowledge is required. The writing style matches all experience levels, but understanding will help you absorb more.

10. Machine Learning Yearning

Author: Andrew Ng

Andrew Ng is a machine learning expert. Co-founded and teaches at Stanford. This free book shows you how to structure an ML project, including recognizing mistakes and building in complex contexts.

The book delivers knowledge and teaches how to apply it, so you'll know how to:

• Determine the optimal course of action for your ML project.

• Create software that is more effective than people.

• Recognize when to use end-to-end, transfer, and multi-task learning, and how to do so.

• Identifying machine learning system flaws

Ng writes easy-to-read books. No rigorous math theory; just a terrific approach to understanding how to make technical machine learning decisions.

11. Deep Learning with PyTorch Step-by-Step

Author: Daniel Voigt Godoy

The last title is also the most recent. The book was revised on 23 January 2022 to discuss Deep Learning and PyTorch, a Python coding tool.

It comprises four parts:

1. Fundamentals (gradient descent, training linear and logistic regressions in PyTorch)

2. Machine Learning (deeper models and activation functions, convolutions, transfer learning, initialization schemes)

3. Sequences (RNN, GRU, LSTM, seq2seq models, attention, self-attention, transformers)

4. Automatic Language Recognition (tokenization, embeddings, contextual word embeddings, ELMo, BERT, GPT-2)

We admire the book's readability. The author avoids difficult mathematical concepts, making the material feel like a conversation.

Is every data scientist a humanist?

Even as a technological professional, you can't escape human interaction, especially with clients.

We hope these books will help you develop interpersonal skills.

Why breakthrough technologies must be accessible

ChatGPT has exploded. Over 1 million people have used the app, and coding sites like Stack Overflow have banned its answers. It's huge.

I wouldn't have called that as an AI researcher. ChatGPT uses the same GPT-3 technology that's been around for over two years.

More than impressive technology, ChatGPT 3 shows how access makes breakthroughs usable. OpenAI has finally made people realize the power of AI by packaging GPT-3 for normal users.

We think of Thomas Edison as the inventor of the lightbulb, not because he invented it, but because he popularized it.

Going forward, AI companies that make using AI easy will thrive.

Use-case importance

Most modern AI systems use massive language models. These language models are trained on 6,000+ years of human text.

GPT-3 ate 8 billion pages, almost every book, and Wikipedia. It created an AI that can write sea shanties and solve coding problems.

Nothing new. I began beta testing GPT-3 in 2020, but the system's basics date back further.

Tools like GPT-3 are hidden in many apps. Many of the AI writing assistants on this platform are just wrappers around GPT-3.

Lots of online utilitarian text, like restaurant menu summaries or city guides, is written by AI systems like GPT-3. You've probably read GPT-3 without knowing it.

Accessibility

Why is ChatGPT so popular if the technology is old?

ChatGPT makes the technology accessible. Free to use, people can sign up and text with the chatbot daily. ChatGPT isn't revolutionary. It does it in a way normal people can access and be amazed by.

Accessibility isn't easy. OpenAI's Sam Altman tweeted that opening ChatGPT to the public increased computing costs.

Each chat costs "low-digit cents" to process. OpenAI probably spends several hundred thousand dollars a day to keep ChatGPT running, with no immediate business case.

Academic researchers and others who developed GPT-3 couldn't afford it. Without resources to make technology accessible, it can't be used.

Retrospective

This dynamic is old. In the history of science, a researcher with a breakthrough idea was often overshadowed by an entrepreneur or visionary who made it accessible to the public.

We think of Thomas Edison as the inventor of the lightbulb. But really, Vasilij Petrov, Thomas Wright, and Joseph Swan invented the lightbulb. Edison made technology visible and accessible by electrifying public buildings, building power plants, and wiring.

Edison probably lost a ton of money on stunts like building a power plant to light JP Morgan's home, the NYSE, and several newspaper headquarters.

People wanted electric lights once they saw their benefits. By making the technology accessible and visible, Edison unlocked a hugely profitable market.

Similar things are happening in AI. ChatGPT shows that developing breakthrough technology in the lab or on B2B servers won't change the culture.

AI must engage people's imaginations to become mainstream. Before the tech impacts the world, people must play with it and see its revolutionary power.

As the field evolves, companies that make the technology widely available, even at great cost, will succeed.

OpenAI's compute fees are eye-watering. Revolutions are costly.

ZERO GRACE/ZERO MALICE

Amazon's purchase of One Medical could speed up American healthcare

The U.S. healthcare industry is a 7-ton seal bleeding at sea. Predators are circling. Unearned margin: price increases relative to inflation without quality improvements. Amazon is the 11-foot megalodon with 7-inch teeth. Amazon is no longer circling... but attacking.

In 2020 dollars, per capita U.S. healthcare spending increased from $2,968 in 1980 to $12,531. The result is a massive industry with 13% of the nation's workers and a fifth of GDP.

Doctor No

In 40 years, healthcare has made progress. From 73.7 in 1980 to 78.8 in 2019, life expectancy rose (before Covid knocked it back down a bit). Pharmacological therapies have revolutionized, and genetic research is paying off. The financial return, improvement split by cost increases, is terrible. No country has expense rises like the U.S., and no one spends as much per capita as we do. Developed countries have longer life expectancies, healthier populations, and less economic hardship.

Two-thirds of U.S. personal bankruptcies are due to medical expenses and/or missed work. Mom or Dad getting cancer could bankrupt many middle-class American families. 40% of American adults delayed or skipped needed care due to cost. Every healthcare improvement seems to have a downside. Same pharmacological revolution that helped millions caused opioid epidemic. Our results are poor in many areas: The U.S. has a high infant mortality rate.

Healthcare is the second-worst retail industry in the country. Gas stations are #1. Imagine walking into a Best Buy to buy a TV and a Blue Shirt associate requests you fill out the same 14 pages of paperwork you filled out yesterday. Then you wait in a crowded room until they call you, 20 minutes after the scheduled appointment you were asked to arrive early for, to see the one person in the store who can talk to you about TVs, who has 10 minutes for you. The average emergency room wait time in New York is 6 hours and 10 minutes.

If it's bad for the customer, it's worse for the business. Physicians spend 27% of their time helping patients; 49% on EHRs. Documentation, order entry, billing, and inbox management. Spend a decade getting an M.D., then become a bureaucrat.

No industry better illustrates scale diseconomies. If we got the same return on healthcare spending as other countries, we'd all live to 100. We could spend less, live longer and healthier, and pay off the national debt in 15 years. U.S. healthcare is the worst ever.

What now? Competition is at the heart of capitalism, the worst system of its kind.

Priority Time

Amazon is buying One Medical for $3.9 billion. I think this deal will liberate society. Two years in, I think One Medical is great. When I got Covid, I pressed the One Medical symbol on my phone; a nurse practitioner prescribed Paxlovid and told me which pharmacies had it in stock.

Amazon enables the company's vision. One Medical's stock is down to $10 from $40 at the start of 2021. Last year, it lost $250 million and needs cash (Amazon has $60 billion). ONEM must grow. The service has 736,000 members. Half of U.S. households have Amazon Prime. Finally, delivery. One Medical is a digital health/physical office hybrid, but you must pick up medication at the pharmacy. Upgrade your Paxlovid delivery time after a remote consultation. Amazon's core competency means it'll happen. Healthcare speed and convenience will feel alien.

It's been a long, winding road to disruption. Amazon, JPMorgan, and Berkshire Hathaway formed Haven four years ago to provide better healthcare for their 1.5 million employees. It rocked healthcare stocks the morning of the press release, but folded in 2021.

Amazon Care is an employee-focused service. Home-delivered virtual health services and nurses. It's doing well, expanding nationwide, and providing healthcare for other companies. Hilton is Amazon Care's biggest customer. The acquisition of One Medical will bring 66 million Prime households capital, domain expertise, and billing infrastructure. Imagine:

"Alexa, I'm hot and my back hurts."

"Connecting you to a Prime doctor now."

Want to vs. Have to

I predicted Amazon entering healthcare years ago. Why? For the same reason Apple is getting into auto. Amazon's P/E is 56, double Walmart's. The corporation must add $250 billion in revenue over the next five years to retain its share price. White-label clothes or smart home products won't generate as much revenue. It must enter a huge market without scale, operational competence, and data skills.

Current Situation

Healthcare reform benefits both consumers and investors. In 2015, healthcare services had S&P 500-average multiples. The market is losing faith in public healthcare businesses' growth. Healthcare services have lower EV/EBITDA multiples than the S&P 500.

Amazon isn't the only prey-hunter. Walmart and Alibaba are starting pharmacies. Uber is developing medical transportation. Private markets invested $29 billion in telehealth last year, up 95% from 2020.

The pandemic accelerated telehealth, the immediate unlock. After the first positive Covid case in the U.S., services that had to be delivered in person shifted to Zoom... We lived. We grew. Video house calls continued after in-person visits were allowed. McKinsey estimates telehealth visits are 38 times pre-pandemic levels. Doctors adopted the technology, regulators loosened restrictions, and patients saved time. We're far from remote surgery, but many patient visits are unnecessary. A study of 40 million patients during lockdown found that for chronic disease patients, online visits didn't affect outcomes. This method of care will only improve.

Amazon's disruption will be significant and will inspire a flood of capital, startups, and consumer brands. Mark Cuban launched a pharmacy that eliminates middlemen in January. Outcome? A 90-day supply of acid-reflux medication costs $17. Medicare could have saved $3.6 billion by buying generic drugs from Cuban's pharmacy. Other apex predators will look at different limbs of the carcass for food. Nike could enter healthcare via orthopedics, acupuncture, and chiropractic. LVMH, L'Oréal, and Estée Lauder may launch global plastic surgery brands. Hilton and Four Seasons may open hospitals. Lennar and Pulte could build "Active Living" communities that Nana would leave feet first, avoiding the expense and tragedy of dying among strangers.

Risks

Privacy matters: HIV status is different from credit card and billing address. Most customers (60%) feel fine sharing personal health data via virtual technologies, though. Unavoidable. 85% of doctors believe data-sharing and interoperability will become the norm. Amazon is the most trusted tech company for handling personal data. Not Meta: Amazon.

What about antitrust, then?

Amazon should be required to spin off AWS and/or Amazon Fulfillment and banned from promoting its own products. It should be allowed to acquire hospitals. One Medical's $3.9 billion acquisition is a drop in the bucket compared to UnitedHealth's $498 billion market valuation.

Antitrust enforcement shouldn't assume some people/firms are good/bad. It should recognize that competition is good and focus on making markets more competitive in each deal. The FTC should force asset divestitures in e-commerce, digital marketing, and social media. These companies can also promote competition in a social ill.

U.S. healthcare makes us fat, depressed, and broke. Competition has produced massive value and prosperity across most of our economy.

Dear Amazon … bring it.

As technology develops, you should stay up to date

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

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

You must learn how to code.

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

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

Artificial intelligence (AI) will transform all jobs.

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

Here are seven AI subjects you must know.

What is artificial intelligence?

How does artificial intelligence work?

What are some examples of AI applications?

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

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

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

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

Blockchain Is Changing the Future

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

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

1. Describe blockchain.

2. How does the blockchain function?

3. What advantages does blockchain offer?

4. What possible uses for blockchain are there?

5. What are the dangers of blockchain technology?

6. What are my options for using blockchain technology?

7. What does blockchain technology's future hold?

Cryptocurrencies are here to stay

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

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

Bitcoin is here to stay.

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

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

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

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

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

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

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

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

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

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

IoT Security is key

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

1. fundamental security ideas

2. Authorization and identification

3. Cryptography

4. electronic certificates

5. electronic signatures

6. Private key encryption

7. Public key encryption

Final Thoughts

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