Elon's rides start rough, but then...

Elon Musk has never been so hated.

They don’t get Elon.

• He began using PayPal in this manner.

• He began with SpaceX in a similar manner.

• He began with Tesla in this manner.

Disruptive.

Elon had rocky starts. His creativity requires it. Just like writing a first draft.

His fastest way to find the way is to avoid it.

PayPal's pricey launch

PayPal was a 1999 business flop.

They were considered insane.

Elon and his co-founders had big plans for PayPal. They adopted the popular philosophy of the time, exchanging short-term profit for growth, and pulled off a miracle just before the bubble burst.

PayPal was created as a dollar alternative. Original PayPal software allowed PalmPilot money transfers. Unfortunately, there weren't enough PalmPilot users.

Since everyone had email, the company emailed payments. Costs rose faster than sales.

The startup wanted to get a million subscribers by paying $10 to sign up and $10 for each referral. Elon thought the price was fair because PayPal made money by charging transaction fees. They needed to make money quickly.

A Wall Street Journal article valuing PayPal at $500 million attracted investors. The dot-com bubble burst soon after they rushed to get financing.

Musk and his partners sold PayPal to eBay for $1.5 billion in 2002. Musk's most successful company was PayPal.

SpaceX's start-up error

Elon and his friends bought a reconditioned ICBM in Russia in 2002.

He planned to invest much of his wealth in a stunt to promote NASA and space travel.

Many called Elon crazy.

The goal was to buy a cheap Russian rocket to launch mice or plants to Mars and return them. He thought SpaceX would revive global space interest. After a bad meeting in Moscow, Elon decided to build his own rockets to undercut launch contracts.

Then SpaceX was founded.

Elon’s plan was harder than expected.

Explosions followed explosions.

• Millions lost on cargo.

• Millions lost on the rockets.

Investors thought Elon was crazy, but he wasn't.

NASA's biggest competitor became SpaceX. NASA hired SpaceX to handle many of its missions.

Tesla's shaky beginning

Tesla began shakily.

• Clients detested their roadster.

• They continued to miss deadlines.

Lotus would handle the car while Tesla focused on the EV component, easing Tesla's entry. The business experienced elegance creep. Modifying specific parts kept the car from getting worse.

Cost overruns, delays, and other factors changed the Elise-like car's appearance. Only 7% of the Tesla Roadster's parts matched its Lotus twin.

Tesla was about to die.

Elon saved the mess as CEO.

He fired 25% of the workforce to reduce costs.

Elon Musk transformed Tesla into the world's most valuable automaker by running it like a startup.

Tesla hasn't spent a dime on advertising. They let the media do the talking by investing in innovation.

Elon sheds. Elon tries. Elon learns. Elon refines.

Twitter doesn't worry me.

The media is shocked. I’m not.

This is just Elon being Elon.

• Elon makes lean.

• Elon tries new things.

• Elon listens to feedback.

• Elon refines.

Besides Twitter will always be Twitter.

Fixing problems isn't enough.

Numerous articles and how-to guides on value propositions focus on fixing consumer concerns.

Contrary to popular opinion, addressing customer pain rarely suffices. Win your market category too.

Core Value Statement

Value proposition usually means a product's main value.

Its how your product solves client problems. The product's core.

Answering these questions creates a relevant core value proposition:

• What tasks is your customer trying to complete? (Jobs for clients)

• How much discomfort do they feel while they perform this? (pains)

• What would they like to see improved or changed? (gains)

After that, you create products and services that alleviate those pains and give value to clients.

Value Proposition by Category

Your product belongs to a market category and must follow its regulations, regardless of its value proposition.

Creating a new market category is challenging. Fitting into customers' product perceptions is usually better than trying to change them.

New product users simplify market categories. Products are labeled.

Your product will likely be associated with a collection of products people already use.

Example: IT experts will use your communication and management app.

If your target clients think it's an advanced mail software, they'll compare it to others and expect things like:

• comprehensive calendar

• spam detectors

• adequate storage space

• list of contacts

• etc.

If your target users view your product as a task management app, things change. You can survive without a contact list, but not status management.

Find out what your customers compare your product to and if it fits your value offer. If so, adapt your product plan to dominate this market. If not, try different value propositions and messaging to put the product in the right context.

Finished Value Proposition

A comprehensive value proposition is when your solution addresses user problems and wins its market category.

Addressing simply the primary value proposition may produce a valuable and original product, but it may struggle to cross the chasm into the mainstream market. Meeting expectations is easier than changing views.

Without a unique value proposition, you will drown in the red sea of competition.

To conclude:

1. Find out who your target consumer is and what their demands and problems are.

2. To meet these needs, develop and test a primary value proposition.

3. Speak with your most devoted customers. Recognize the alternatives they use to compare you against and the market segment they place you in.

4. Recognize the requirements and expectations of the market category.

5. To meet or surpass category standards, modify your goods.

Great products solve client problems and win their category.

He kept it under wraps for years until he legally couldn’t.

His shirt is incomplete. I can’t stop thinking about this…

Actually, ignore the article. Look at it. JUST LOOK at it… It’s quite disturbing, isn’t it?

Ughh…

Me: “Hey, what up?” Friend: “All good, watching lord of the rings on amazon prime video.” Me: “Oh, do you know how Amazon grew and became famous?” Friend: “Geek alert…Can I just watch in peace?” Me: “But… Bezos?” Friend: “Let it go, just let it go…”

I can question you, the reader, and start answering instantly without his consent. This far.

Reader, how did Amazon succeed? You'll say, Of course, it was an internet bookstore, then it sold everything.

Mistaken. They moved from zero to one because of this. How did they get from one to thousand? AWS-some. Understand? It's geeky and lame. If not, I'll explain my geekiness.

Over an extended period of time, Amazon was not profitable.

Business basics. You want customers if you own a bakery, right?

Well, 100 clients per day order $5 cheesecakes (because cheesecakes are awesome.)

$5 x 100 consumers x 30 days Equals $15,000 monthly revenue. You proudly work here.

Now you have to pay the barista (unless ChatGPT is doing it haha? Nope..)

• The barista is requesting $5000 a month.

• Each cheesecake costs the cheesecake maker $2.5 ($2.5 × 100 x 30 = $7500).

• The monthly cost of running your bakery, including power, is about $5000.

Assume no extra charges. Your operating costs are $17,500.

Just $15,000? You have income but no profit. You might make money selling coffee with your cheesecake next month.

Is losing money bad? You're broke. Losing money. It's bad for financial statements.

It's almost a business ultimatum. Most startups fail. Amazon took nine years.

I'm reading Amazon Unbound: Jeff Bezos and the Creation of a Global Empire to comprehend how a company has a $1 trillion market cap.

Many things made Amazon big. The book claims that Bezos and Amazon kept a specific product secret for a long period.

Clouds above the bald head.

In 2006, Bezos started a cloud computing initiative. They believed many firms like Snapchat would pay for reliable servers.

In 2006, cloud computing was not what it is today. I'll simplify. 2006 had no iPhone.

Bezos invested in Amazon Web Services (AWS) without disclosing its revenue. That's permitted till a certain degree.

Google and Microsoft would realize Amazon is heavily investing in this market and worry.

Bezos anticipated high demand for this product. Microsoft built its cloud in 2010, and Google in 2008.

If you managed Google or Microsoft, you wouldn't know how much Amazon makes from their cloud computing service. It's enough. Yet, Amazon is an internet store, so they'll focus on that.

All but Bezos were wrong.

Time to come clean now.

They revealed AWS revenue in 2015. Two things were apparent:

1. Bezos made the proper decision to bet on the cloud and keep it a secret.

2. In this race, Amazon is in the lead.

They continued. Let me list some AWS users today.

• Netflix

• Airbnb

• Twitch

More. Amazon was unprofitable for nine years, remember? This article's main graph.

AWS accounted for 74% of Amazon's profit in 2021. This 74% might not exist if they hadn't invested in AWS.

Bring this with you home.

Amazon predated AWS. Yet, it helped the giant reach $1 trillion. Bezos' secrecy? Perhaps, until a time machine is invented (they might host the time machine software on AWS, though.)

Without AWS, Amazon would have been profitable but unimpressive. They may have invested in anything else that would have returned more (like crypto? No? Ok.)

Bezos has business flaws. His success. His failures include:

• introducing the Fire Phone and suffering a $170 million loss.

• Amazon's failure in China In 2011, Amazon had a about 15% market share in China. 2019 saw a decrease of about 1%.

• not offering a higher price to persuade the creator of Netflix to sell the company to him. He offered a rather reasonable $15 million in his proposal. But what if he had offered $30 million instead (Amazon had over $100 million in revenue at the time)? He might have owned Netflix, which has a $156 billion market valuation (and saved billions rather than invest in Amazon Prime Video).

Some he could control. Some were uncontrollable. Nonetheless, every action he made in the foregoing circumstances led him to invest in AWS.

Crypto and blockchain have rebranded as web3. They probably thought it sounded better and didn't want the baggage of scam ICOs, STOs, and skirted securities laws.

It was like Facebook becoming Meta. Crypto's biggest players wanted to change public (and regulator) perception away from pump-and-dump schemes.

After the 2018 ICO gold rush, it's understandable. Every project that raised millions (or billions) never shipped a meaningful product.

Like many crazes, charlatans took the money and ran.

Despite its grifter past, web3 is THE hot topic today as more founders, venture firms, and larger institutions look to build the future decentralized internet.

Supposedly.

How often have you heard: This will change the world, fix the internet, and give people power?

Why are most of web3's biggest proponents (and beneficiaries) the same rich, powerful players who built and invested in the modern internet? It's like they want to remake and own the internet.

Something seems off about that.

Why are insiders getting preferential presale terms before the public, allowing early investors and proponents to flip dirt cheap tokens and advisors shares almost immediately after the public sale?

It's a good gig with guaranteed markups, no risk or progress.

If it sounds like insider trading, it is, at least practically. This is clear when people talk about blockchain/web3 launches and tokens.

Fast money, quick flips, and guaranteed markups/returns are common.

Incentives-wise, it's hard to blame them. Who can blame someone for following the rules to win? Is it their fault or regulators' for not leveling the playing field?

It's similar to oil companies polluting for profit, Instagram depressing you into buying a new dress, or pharma pushing an unnecessary pill.

All of that is fair game, at least until we change the playbook, because people (and corporations) change for pain or love. Who doesn't love money?

belief based on money gain

Sinclair:

“It is difficult to get a man to understand something when his salary depends upon his not understanding it.”

Bitcoin, blockchain, and web3 analogies?

Most blockchain and web3 proponents are true believers, not cynical capitalists. They believe blockchain's inherent transparency and permissionless trust allow humanity to evolve beyond our reptilian ways and build a better decentralized and democratic world.

They highlight issues with the modern internet and monopoly players like Google, Facebook, and Apple. Decentralization fixes everything

If we could give power back to the people and get governments/corporations/individuals out of the way, we'd fix everything.

Blockchain solves supply chain and child labor issues in China.

To meet Paris climate goals, reduce emissions. Create a carbon token.

Fixing online hatred and polarization Web3 Twitter and Facebook replacement.

Web3 must just be the answer for everything… your “perfect” silver bullet.

Nothing fits everyone. Blockchain has pros and cons like everything else.

Blockchain's viral, ponzi-like nature has an MLM (mid level marketing) feel. If you bought Taylor Swift's NFT, your investment is tied to her popularity.

Probably makes you promote Swift more. Play music loudly.

Here's another example:

Imagine if Jehovah’s Witnesses (or evangelical preachers…) got paid for every single person they converted to their cause.

It becomes a self-fulfilling prophecy as their faith and wealth grow.

Which breeds extremism? Ultra-Orthodox Jews are an example. maximalists

Bitcoin and blockchain are causes, religions. It's a money-making movement and ideal.

We're good at convincing ourselves of things we want to believe, hence filter bubbles.

I ignore anything that doesn't fit my worldview and seek out like-minded people, which algorithms amplify.

Then what?

Is web3 merely a new scam?

No, never!

Blockchain has many crucial uses.

Sending money home/abroad without bank fees;

Like fleeing a war-torn country and converting savings to Bitcoin;

Like preventing Twitter from silencing dissidents.

Permissionless, trustless databases could benefit society and humanity. There are, however, many limitations.

Lost password?

What if you're cheated?

What if Trump/Putin/your favorite dictator incites a coup d'état?

What-ifs abound. Decentralization's openness brings good and bad.

No gatekeepers or firefighters to rescue you.

ISIS's fundraising is also frictionless.

Community-owned apps with bad interfaces and service.

Trade-offs rule.

So what compromises does web3 make?

What are your trade-offs? Decentralization has many strengths and flaws. Like Bitcoin's wasteful proof-of-work or Ethereum's political/wealth-based proof-of-stake.

To ensure the survival and veracity of the network/blockchain and to safeguard its nodes, extreme measures have been designed/put in place to prevent hostile takeovers aimed at altering the blockchain, i.e., adding money to your own wallet (account), etc.

These protective measures require significant resources and pose challenges. Reduced speed and throughput, high gas fees (cost to submit/write a transaction to the blockchain), and delayed development times, not to mention forked blockchain chains oops, web3 projects.

Protecting dissidents or rogue regimes makes sense. You need safety, privacy, and calm.

First-world life?

What if you assumed EVERYONE you saw was out to rob/attack you? You'd never travel, trust anyone, accomplish much, or live fully. The economy would collapse.

It's like an ant colony where half the ants do nothing but wait to be attacked.

Waste of time and money.

11% of the US budget goes to the military. Imagine what we could do with the $766B+ we spend on what-ifs annually.

Is so much hypothetical security needed?

Blockchain and web3 are similar.

Does your app need permissionless decentralization? Does your scooter-sharing company really need a proof-of-stake system and 1000s of nodes to avoid Russian hackers? Why?

Worst-case scenario? It's not life or death, unless you overstate the what-ifs. Web3 proponents find improbable scenarios to justify decentralization and tokenization.

Do I need a token to prove ownership of my painting? Unless I'm a master thief, I probably bought it.

despite losing the receipt.

I do, however, love Web 3.

Enough Web3 bashing for now. Understand? Decentralization isn't perfect, but it has huge potential when applied to the right problems.

I see many of the right problems as disrupting big tech's ruthless monopolies. I wrote several years ago about how tokenized blockchains could be used to break big tech's stranglehold on platforms, marketplaces, and social media.

Tokenomics schemes can be used for good and are powerful. Here’s how.

Before the ICO boom, I made a series of predictions about blockchain/crypto's future. It's still true.

Here's where I was then and where I see web3 going:

My 11 Big & Bold Predictions for Blockchain

In the near future, people may wear crypto cash rings or bracelets.

1. While some governments repress cryptocurrency, others will start to embrace it.

2. Blockchain will fundamentally alter voting and governance, resulting in a more open election process.

3. Money freedom will lead to a more geographically open world where people will be more able to leave when there is unrest.

4. Blockchain will make record keeping significantly easier, eliminating the need for a significant portion of government workers whose sole responsibility is paperwork.

5. Overrated are smart contracts.

6. Tokens will replace company stocks.

7. Blockchain increases real estate's liquidity, value, and volatility.

8. Healthcare may be most affected.

9. Crypto could end privacy and lead to Minority Report.

10. New companies with network effects will displace incumbents.

11. Soon, people will wear rings or bracelets with crypto cash.

Some have already happened, while others are still possible.

Time will tell if they happen.

And finally:

What will web3 be?

Who will be in charge?

Closing remarks

Hope you enjoyed this web3 dive. There's much more to say, but that's for another day.

We're writing history as we go.

Tech regulation, mergers, Bitcoin surge How will history remember us?

What about web3 and blockchain?

Is this a revolution or a tulip craze?

Remember, actions speak louder than words (share them in the comments).

Your turn.

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.

4 Pricey Tools That Are Valuable

I pay based on ROI (return on investment).

If a $20/month tool or $500 online course doubles my return, I'm in.

Investing helps me build wealth.

Canva Pro

I initially refused to pay.

My course content needed updating a few months ago. My Google Docs text looked cleaner and more professional in Canva.

I've used it to:

• product cover pages

• eBook covers

• Product page infographics

See my Google Sheets vs. Canva product page graph.

Google Sheets vs Canva

Yesterday, I used it to make a LinkedIn video thumbnail. It took less than 5 minutes and improved my video.

In 30 hours, the video had 39,000 views.

Here's more.

HypeFury

Hypefury rocks!

It builds my brand as I sleep. What else?

Because I'm traveling this weekend, I planned tweets for 10 days. It took me 80 minutes.

So while I travel or am absent, my content mill keeps producing.

Also I like:

• I can reach hundreds of people thanks to auto-DMs. I utilize it to advertise freebies; for instance, leave an emoji remark to receive my checklist. And they automatically receive a message in their DM.

• Scheduled Retweets: By appearing in a different time zone, they give my tweet a second chance.

It helps me save time and expand my following, so that's my favorite part.

It’s also super neat:

Zoom Pro

My course involves weekly and monthly calls for alumni.

Google Meet isn't great for group calls. The interface isn't great.

Zoom Pro is expensive, and the monthly payments suck, but it's necessary.

It gives my students a smooth experience.

Previously, we'd do 40-minute meetings and then reconvene.

Zoom's free edition limits group calls to 40 minutes.

This wouldn't be a good online course if I paid hundreds of dollars.

So I felt obligated to help.

YouTube Premium

My laptop has an ad blocker.

I bought an iPad recently.

When you're self-employed and work from home, the line between the two blurs. My bed is only 5 steps away!

When I read or watched videos on my laptop, I'd slide into work mode. Only option was to view on phone, which is awkward.

YouTube premium handles it. No more advertisements and I can listen on the move.

3 Expensive Tools That Aren't Valuable

Marketing strategies are sometimes aimed to make you feel you need 38474 cool features when you don’t.

Certain tools are useless.

I found it useless.

Depending on your needs. As a writer and creator, I get no return.

They could for other jobs.

Shield Analytics

It tracks LinkedIn stats, like:

• follower growth

• trend chart for impressions

• Engagement, views, and comment stats for posts

• and much more.

Middle-tier creator costs $12/month.

I got a 25% off coupon but canceled my free trial before writing this. It's not worth the discount.

Why?

LinkedIn provides free analytics. See:

Not thorough and won't show top posts.

I don't need to see my top posts because I love experimenting with writing.

Slack Premium

Slack was my classroom. Slack provided me a premium trial during the prior cohort.

I skipped it.

Sure, voice notes are better than a big paragraph. I didn't require pro features.

Marketing methods sometimes make you think you need 38474 amazing features. Don’t fall for it.

Calendly Pro

This may be worth it if you get many calls.

I avoid calls. During my 9-5, I had too many pointless calls.

I don't need:

• ability to schedule calls for 15, 30, or 60 minutes: I just distribute each link separately.

• I have a Gumroad consultation page with a payment option.

• follow-up emails: I hardly ever make calls, so

• I just use one calendar, therefore I link to various calendars.

I'll admit, the integrations are cool. Not for me.

If you're a coach or consultant, the features may be helpful. Or book meetings.

Conclusion

Investing is spending to make money.

Use my technique — put money in tools that help you make money. This separates it from being an investment instead of an expense.

Try free versions of these tools before buying them since everyone else is.